# Towards Autonomous Driving with Small-Scale Cars: A Survey of Recent
Development

Dianzhao Li, Paul Auerbach, and Ostap Okhrin Dianzhao Li and Ostap Okhrin are
with the Chair of Econometrics and Statistics, esp. in the Transport Sector,
Technische Universität Dresden, Dresden, 01187, Germany and Center for
Scalable Data Analytics and Artificial Intelligence (ScaDS.AI)
Dresden/Leipzig, Germany. (E-mails<EMAIL_ADDRESS><EMAIL_ADDRESS>
Paul Auerbach is with Barkhausen Institut gGmbH, 01067 Dresden, Germany.
(E-mail<EMAIL_ADDRESS>
###### Abstract

While engaging with the unfolding revolution in autonomous driving, a
challenge presents itself, how can we effectively raise awareness within
society about this transformative trend? While full-scale autonomous driving
vehicles often come with a hefty price tag, the emergence of small-scale car
platforms offers a compelling alternative. These platforms not only serve as
valuable educational tools for the broader public and young generations but
also function as robust research platforms, contributing significantly to the
ongoing advancements in autonomous driving technology. This survey outlines
various small-scale car platforms, categorizing them and detailing the
research advancements accomplished through their usage. The conclusion
provides proposals for promising future directions in the field.

###### Index Terms:

Small-scale car, autonomous vehicles, robotics

##  I Introduction

Over the past few decades, extensive research on autonomous driving (AD) has
been conducted by both academic communities and industry stakeholders.
However, the fruition of fully functional applications for AD systems is
anticipated to materialize in the coming decades. The question arises: _Can we
proactively prepare our society for the oncoming fully autonomous driving?_
Despite assertions from researchers that autonomous vehicles (AVs) will
mitigate human error and enhance safety compared to human drivers, public
apprehensions persist regarding the ethical and safety dimensions of AVs [1,
2]. Encouragingly, individuals with prior experience with AVs and younger
generations exhibit a more optimistic stance toward these technologies [3].
Hence, the affirmative response to this question is clear. Given that those
who will design autonomous systems are currently in high school, and those who
will coexist with AVs are presently in elementary schools, the optimal
preparation involves providing opportunities for the public to have touch with
AD systems now. To this end, a small-scale car platform emerges as an ideal
choice, serving not only educational purposes but also facilitating
researchers in testing their autonomous systems on a tangible platform.

Undoubtedly, research in AD research for full-scale cars holds promise.
However, to provide the public and research communities with a more cost-
effective and accessible avenue to engage with AD, the replication of small-
scale car platforms stands out. In educational settings, small-scale cars
prove to be an excellent choice for schools, especially with STEM focus,
offering students their initial exposure and hands-on experience with AD while
simultaneously fostering increased public awareness. On the research front,
the availability of such research platforms lowers entry barriers, inviting a
broader spectrum of researchers into AD exploration. This, in turn, propels
advancements in AD and establishes a virtuous circle of continuous improvement
and innovation before their integration into full-scale autonomous vehicles.
As evidenced in Fig. 1(A), the quantity of research papers focused on small-
scale car platforms has experienced a substantial surge in the past two
decades. Following a relatively gradual incline before 2016, this field has
undergone a notable expansion, particularly catalyzed by the introduction of a
series of well-known platforms. Hence, to advance ongoing research initiatives
and enhance public awareness, we attempt to comprehensively survey the
existing small-scale platforms for educational and research purposes. We also
aim to benchmark the AD tasks accomplished by these platforms. To the best of
our knowledge, this survey represents the first of its kind to undertake such
an extensive analysis.

The rest of this paper is organized as follows: In the second section, we
examine commonly employed small-scale car platforms, accompanied by insights
into their simulators. The third section provides a comprehensive overview of
the AD tasks accomplished within the research community. Following that, the
fourth section inspects the sensor setups employed in these platforms. The
subsequent section outlines promising directions for future developments, and
we draw our conclusions in the sixth section.

Figure 1: Illustration of the development and current states of small-scale
car platforms, each depicted platform image sourced from its respective paper
or website. (A) Based on published studies each year on Google Scholar with
the search terms: robot car, the research on small-scale cars has seen
substantial growth in the number of papers over the years. In the early 2000s,
projects like s-bot [4], e-puck [5], and TurtleBot [6] emerged. Starting
around 2016, with the introduction of Duckietown [7], BARC [8], and Autorally
[9], there was a significant increase in research papers. This trend continued
with the development of projects like DeepRacer [10], Donkeycar [11], and
F1TENTH [12]. More computationally advanced small-scale cars have been
introduced in recent years, such as ART/ATK [13] and XTENTH-CAR [14]. (B)
Examples of small-scale car platforms, categorized into educational platforms
and research platforms, including multiple vehicle setups such as ORAC [15]
and UDSSC [16].

##  II Platforms

For full-scale cars, the standardized dynamics and sizes are mandated by road
regulations. In contrast, smaller-scale cars, designed without regulatory
constraints, are crafted for diverse research purposes by various groups of
researchers and enthusiasts. Recognizing these disparities, this survey
establishes a clear definition for AD robot cars, setting them apart from
other autonomous robots. In this context, an AD small-scale car platform
refers to a miniature vehicle equipped with technologies enabling autonomous
operation. This includes sensors, processing units, control actuators, and
often a set of predefined tasks or challenges for testing and development. In
terms of size, namely the scale factors, there is considerable variability
among different platforms. Platforms intended to carry a lot of sensors and
for outdoor operation can reach almost 1 meter in length [9]. On the other end
of the spectrum, platforms developed for investigating swarm dynamics are
notably compact, with some as small as 3.3x3.3cm [17]. The most common form
factor is the 1/10th scale, representing a model approximately one-tenth the
size of a full-scale car. This scale is frequently employed in hobby Remote-
Control (RC) vehicles, forming the foundation for numerous platforms [11, 12,
18, 8, 19, 20, 21, 22, 23, 24, 14]. Regarding the dynamic system, full-size
cars commonly use Ackermann steering mechanics, rotating the front axle to
facilitate left or right turns [25]. However, due to the mechanical complexity
of this system and its limitations in certain situations, small-scale car
platforms initially favored the use of a differential steering system. In this
system, the left and right sets of wheels are driven independently, enabling
turns by driving one set of wheels faster than the other. Nevertheless, as
illustrated in Fig. 1, recent advancements in this field have seen the
increased adoption of the more realistic Ackermann steering system in small-
scale car platforms.

###  II-A Hardware platforms

Before we introduce the different platforms, we categorize small-scale car
platforms into distinct groups based on their target users and complexity.
First are the educational platforms often powered by MicroBit or Raspberry Pi,
predominantly accessible in the commercial market, including Makeblock mBot
[26], Edison [27], AlphAI Robot [28], TinkerGen MARK [29], and Robolink Zumi
[30], typically using platform-specified block and visual programming
languages. These platforms are carefully crafted to engage students ranging
from early childhood to elementary school and even graduate programs in the
interactive exploration of autonomous cars. They offer users comprehensive
tools, enabling them to effortlessly construct and program robot cars capable
of executing diverse tasks. With these platforms, students can readily
accomplish basic tasks like simple lane following and obstacle avoidance.

The subsequent tier has a more sophisticated system, affording users enhanced
opportunities to innovate and develop new functionalities within the platform.
The typical platforms are e-puck [5], Pheeno [31], Thymio [32], MarXbot [33],
Turtlebot [6], and Duckietown [7]. The e-puck, Pheeno, Thymio, and MarXbot are
commonly employed in swarm robot research due to their compact sizes. The
Duckietown ecosystem, originating in 2016 at MIT, serves as an educational
tool for instructing students in the realms of autonomous and connected
driving. Comprising cost-effective vehicles known as Duckiebots, each unit is
outfitted with either a Raspberry Pi or a Jetson Nano as its computing unit.
Sensor-wise, the Duckiebots employ an RGB Raspberry Pi camera, an inertial
measurement unit (IMU), wheel encoders, and a front-facing distance sensor.
The platform also facilitates the incorporation of a central localization
system through ”watchtowers.” Propelled by two DC motors on each of the two
driven wheels, the cars adhere to a differential drive pattern. Duckietown is
commercially available and has proven effective in various applications, with
lane-keeping using the RGB camera being the most common [34, 35].
Additionally, it has been employed for tasks such as obstacle and traffic sign
detection [36, 37]. Another commercially available platform is the AWS
DeepRacer [10] by Amazon Web Services. This platform also relies on an RGB
camera for sensory input, supplemented by an upgrade kit featuring a second
camera and a LiDAR sensor. The execution of learned strategies is managed by a
small compute board equipped with an Intel Atom CPU. With Ackermann steering
implementation, DeepRacer is also primarily used for tasks like lane-keeping
[38, 39] and obstacle avoidance [40, 41].

For users seeking open-source platforms and aiming to construct a platform
from the ground up, two common options are Donkeycar [11] and F1TENTH [12].
Donkeycar, a collaborative effort within a community, provides comprehensive
instructions for constructing the platform. Due to its open-source nature,
individual cars can vary significantly, accommodating diverse sensor
configurations. The foundational platform centers around a 1/10th scale RC
car, featuring Ackermann steering, and is customized with a Raspberry Pi or
Jetson Nano as the compute unit, along with a Raspberry Pi camera serving as
its primary sensor. Users have the flexibility to incorporate additional
sensors such as IMUs and encoders, and there are options for integrating LiDAR
into the system. Given its open-source framework, users can extend the
platform’s functionality according to their needs. Originally designed for
autonomous racing, Donkeycar is also commonly applied for tasks like lane-
keeping [42, 43] and obstacle avoidance [44]. Likewise, F1TENTH is built upon
a 1/10th scale RC car platform featuring Ackermann steering. Its compute unit
is a Nvidia Jetson, complemented by a 2D LiDAR serving as the primary sensor.
Additional features include an IMU and odometry information provided by the
VESC motor controller111https://vesc-project.com/, with an option to integrate
an extra RGB camera. With its high-speed capable base platform, F1TENTH is
primarily utilized for autonomous racing tasks [45, 46], while, it also finds
application in fundamental tasks such as lane-keeping [47] and obstacle
avoidance [48, 49].

For outdoor applications, the Autorally [9] platform takes center stage,
incorporating an off-the-shelf RC car platform. In comparison to the Donkeycar
and F1TENTH platforms, Autorally is larger, utilizing a 1/5th scale model car.
Leveraging this increased size, it incorporates a more powerful compute unit
consisting of a consumer computer mainboard with an Intel i7 CPU. The primary
sensors include two industrial synchronized RGB cameras for obtaining stereo
images, an IMU, magnetic encoders for odometry measurement, and a GPS
receiver. Due to its substantial size and high-performance components,
Autorally is mainly employed for racing tasks [50, 51, 52]. The platform
offers a variety of sensor combinations for racing approaches, with many
leveraging the combination of GPS and IMU data [53, 54], as well as those that
integrate camera and IMU data [55, 56].

The significance of a small-scale smart city platform, accommodating multiple
vehicles, is underscored as it plays a crucial role in achieving harmony and
efficient collaboration of a fully autonomous driving world. To this end, the
University of Delaware Smart Scaled City (UDSSC) [16], developed for education
and research on connected and autonomous driving, emerges as a distinctive
contribution. It features a downscaled city environment with diverse traffic
scenarios, including intersections and roundabouts, and incorporates Micro
Connected and Automated Vehicles (MCAV). Built on the Pololu
Zumo222https://www.pololu.com/category/170/zumo-32u4-oled-robot/ platform, the
MCAVs have a differential drive DC motor setup with encoders, an IMU, and an
Arduino333https://www.arduino.cc/. Each vehicle is additionally equipped with
a Raspberry Pi as its compute unit, a line-following sensor, and a front-
facing distance sensor. Notably, the absence of a camera or LiDAR prompts the
utilization of a central localization system for accurate positioning. UDSSC
serves a specialized purpose, exclusively engaging in cooperative and
connected driving tasks such as merging [16] and roundabout traversal [57, 58,
59]. The platform is unfortunately not accompanied by any open-source code or
hardware accessibility and is not available for purchase.

For the sake of brevity, we have condensed the details of the usually used
platforms into Table I. This table offers a comparative analysis, outlining
key characteristics such as steering dynamics, installed sensors, utilized
software frameworks, and, when relevant, the simulation software integrated
into each platform.

TABLE I: Small-scale car platforms: an overview of the hardware and software
setup.

Platforms | Size |  Vehicle Dynamics | Sensors | Computation Unit |  Software Framework |  Simulation Platform | Open-source11footnotemark: 1 |  Commercial
---|---|---|---|---|---|---|---|---
AutoRally [9] | 1/5th | Ackermann |  Camera, IMU, GPS, Hall-effect sensor |  Intel i7-6700 Nvidia GTX-750ti SC | ROS | Gazebo | SW |  No22footnotemark: 2
ART/ATK [13] | 1/6th | Ackermann |  Camera,3D LiDAR | Jetson Xavier NX | ROS2 | Chrono | – | No
BARC [8] | 1/10th | Ackermann |  Camera, LiDAR, IMU, GPS | ODROID-XU4 | ROS | – | – | No
Donkeycar [11] |  1/10th 1/16th | Ackermann |  Camera, LiDAR, IMU, Encoder | RPi/Jetson Nano | Python | Donkeycar Gym | SW, HW |  No22footnotemark: 2
F1TENTH [12] | 1/10th | Ackermann |  Camera, LiDAR, IMU | Jetson TX2 | ROS |  Gazebo F1TENTH Gym | SW |  No22footnotemark: 2
RACECAR(MIT) [18] | 1/10th | Ackermann |  Camera, LiDAR, IMU, Encoder | Jetson Tegra X1 | ROS | Gazebo | SW, HW |  No22footnotemark: 2
MuSHR [19] | 1/10th | Ackermann |  Camera, LiDAR, IMU, Bump sensor | Jetson Nano | ROS | – | SW |  No22footnotemark: 2
Qcar [20] | 1/10th | Ackermann |  Camera, LiDAR, IMU, Encoder, Microphone | Jetson TX2 |  ROS, Python, MATLAB | Gazebo | SW | $\checked$
Autominy [23] | 1/10th | Ackermann |  Camera, LiDAR, IMU, Encoder | Intel NUC | ROS | Gazebo | SW | No
JetRacer [22] |  1/10th 1/18th | Ackermann | Camera | Jetson Nano | – | – | SW, HW | $\checked$
Autonomouscar [21] | 1/10th | Ackermann |  Camera, LiDAR, IMU, Encoder, ToF Sensor, Indoor GPS | RPi 4 |  ROS, LabVIEW | – | SW, HW | No
CoRoLa [24] | 1/10th | Ackermann |  Camera, Encoder, Ultrasonic sensor | RPi 4 | ROS2 |  Based on LGSVL | – | No
AutoDRIVE [60] | 1/14th | Ackermann |  Camera, LiDAR, IMU, Encoder, Indoor GPS | Jetson Nano | ROS |  AutoDRIVE Simulator | SW, HW | No
PiRacer [61] | 1/16th | Ackermann | Camera | RPi 4 | ROS | – | – | $\checked$
Duckietown [7] | 34x15x23cm | Differential |  Camera, IMU, Ultrasonic sensor | RPi 2/Jetson Nano | ROS |  Gazebo Duckietown Gym | SW | $\checked$
DeepRacer [10] | 1/18th | Ackermann | Camera, LiDAR, IMU | Intel Atom | ROS | Gazebo | SW | $\checked$
ITS car [62] | 1/18th | Ackermann | Camera | RPi 3 B+ | – | – | – | No
µcar [63] | 1/18th | Ackermann | IMU, Encoder | RPi Zero W | – | – | SW, HW | No
UDSSC MCAV [16] | 1/25th | Differential |  IMU, line following, IR sensor | RPi 3 | ROS | SUMO | – | No
Chronos [64] | 1/28th | Ackermann | IMU, Encoder | Espressif ESP32 | ROS | – | SW, HW | No
Go-CHART [65] | 1/28th | Differential |  Camera, LiDAR, Bump sensor | RPi 3 | ROS | – | SW, HW | No
Cambridge Minicar [66] | 75x81x197mm | Ackermann |  Indoor positioning system33footnotemark: 3 | RPi Zero | – | – | SW, HW | No
ORCA Racer [15] | 1/43th | Ackermann |  IMU, Indoor positioning system33footnotemark: 3 | ARM Cortex M4 | IMU | – | – | No
Epuck [5] | 70mm ⌀ | Differential |  Camera, IMU, IR sensor, Speaker, Microphone | STM32F407 | ROS |  Enki, Webots | – | $\checked$
Turtlebot [6] | 34x34x35cm | Differential | Camera, LiDAR, IR sensor | RPi 4 | ROS | Gazebo | SW | $\checked$
Kilobot [17] | 33mm ⌀ | Vibration | IR sensor | Atmega 328 | – | – | – | $\checked$
GRITSBot [67] | 31x30mm | Differential |  IMU, IR sensor | Atmega 328 | – | – | SW, HW | No
HydraOne [68] | 27x32cm | Omni |  Camera, 3D LiDAR, Encoders | Jetson TX2 | ROS | – | HW | No
Pheeno [31] | 10cm ⌀ | Differential |  Camera, IMU, Encoder, IR sensor |  ATmega328P ARM Cortex-A7 | Python | – | – | No
Thymio [32] | 11x11cm | Differential |  IR sensor, Accelerometer, Microphone, Thermistor | PIC24F | ROS, VPL | Gazebo | – | $\checked$
MarXbot [33] | 17cm ⌀ | Differential |  Camera, IMU, IR sensor, 2D force sensor | ARM 11 processor | ROS | Gazebo | – | No
WolfBot [69] | 17.5cm ⌀ | Omni |  Camera, IMU, IR sensor, Microphone | BeagleBone | ROS | MATLAB | SW, HW | No
LabRAT [70] | - | Differential |  IR sensor | Atmega324p | C | Player/Stage | SW, HW | No
mAGV [71] | - | Differential |  IMU, Encoder, RFID | RPi Zero W | ROS | – | – | No
Rover [72] | - | Differential |  Camera, LiDAR, IMU, GPS, Ultrasonic sensor | Jetson TK1 | ROS | Gazebo | – | No
Jetbot [73] | - | Differential | Camera, IMU | Jetson Nano | ROS | Gazebo | SW, HW | $\checked$
Makeblock mBot44footnotemark: 4 [26] | – | Differential |  Ultrasonic Sensor, IR sensor, Line tracking sensor | Arduino |  mBlock55footnotemark: 5 | – | – | $\checked$
Edison44footnotemark: 4 [27] | 80x80x40mm | Differential | IR sensor, Line tracking sensor | - |  EdBlock, EdPy55footnotemark: 5 | – | – | $\checked$
AlphAI Robot44footnotemark: 4 [28] | – | Differential |  Camera, Ultrasonic Sensor, Line tracking sensor | Raspberry Pi Zero | Python | – | – | $\checked$
Ozobot Evo44footnotemark: 4 [74] | 32mm ⌀ | Differential | IR sensor, Speaker | – |  OzoBlockly55footnotemark: 5 | – | – | $\checked$
Finch Robot44footnotemark: 4 [74] | – | Differential |  Encoder, Ultrasonic sensor, Line tracking sensor, IR sensor, Speaker | MicroBit | Python | – | – | $\checked$
TinkerGen MARK44footnotemark: 4 [29] | 200x185x92mm | Differential | Camera, Ultrasonic Sensor | Arduino | MicroPython | – | – | $\checked$
Robolink Zumi44footnotemark: 4 [30] | 95x67x70mm | Differential | Camera, IR sensor | RPi zero |  Python, Blockly55footnotemark: 5 | – | – | $\checked$

  * 1.

For open-source types, SW: Software and HW: Hardware.

  * 2.

Build guides with off-the-shelf parts are provided.

  * 3.

Motion Capture Systems.

  * 4.

Educational robot kit.

  * 5.

Platform-specified block and visual programming languages.

###  II-B Simulators

Before employing the autonomous system in real platforms, a simulation is
often used first to test the performance. For small-scale cars, numerous
simulators exist to facilitate the training, testing, and evaluation of
autonomous systems.

First, for the requirements of simulating the vehicle dynamics and control
systems, CarMaker [75] and CarSim [76] emerge as notable choices. They furnish
simulation environments capable of comprehensively assessing various facets of
vehicle behavior, encompassing handling, braking, and acceleration, across
diverse driving conditions. For autonomous driving systems, the sensor models
are as important as the physics engine, therefore, Gazebo [77] plays an
important role in robotics and related research areas. It provides a modular
system that facilitates the integration of diverse sensors and physics models.
For small-scale cars such as Turtlebot, Duckietown, DeepRacer, F1TENTH, MuSHR,
Autorally, and others, simulations based on Gazebo have been developed. For a
macro-scale simulation, SUMO [78] emerges as a platform for in-depth analysis
and optimization of transportation systems, road networks, and traffic
management strategies. Similarly, Highway-env [79] provides a gym-based
environment specifically designed for simulating highway-based road
topologies. In the realm of more detailed autonomous driving setups, various
open-source simulators have gained popularity, with TORCS [80], CARLA [81],
AIRSIM [82], DeepDrive [83], and LGSVL [84] being notable examples. These
simulators not only boast high-fidelity physics engines for simulating vehicle
dynamics but also support a diverse array of sensors. This allows for hyper-
realistic simulations of traffic environments, facilitating rigorous testing
of AVs. For multi-agent systems, MADRaS [85], built on top of TORCS, and Flow
[86], built on top of SUMO, are commonly used.

Besides the universal simulators, some simulators are specially tailored for
small-scale cars. For example, Gym-Duckietown [87] for Duckietown, provides a
fast, open, and highly customizable simulator, to train or test various
algorithms for different driving tasks. The DeepRacer cloud simulator from
Amazon for the DeepRacer racing platform provides an online platform to
customize the algorithm, choose the tasks, and modify the structure of the
networks. Donkey-Gym [88] for Donkeycar is similar to Gym-Duckietown, but
built on the Unity game platform with internal physics and graphics, also the
F1TENTH Gym environment [89] for the F1TENTH platform. With these specially
designed simulation platforms, the real-world deployment of autonomous small-
scale cars will be facilitated after training and testing in the simulation.
Nevertheless, the Simulation to Reality (Sim2Real) gap remains a challenge
that needs to be addressed before successfully deploying the trained
algorithms in the real world.

###  II-C Sim2Real transfer

Sim2Real is a concept in robotics and AVs that involves transferring skills,
knowledge, or models acquired in a simulated environment to real-world
applications. Here, the real-world setting or scenario in which the robot is
intended to operate and execute tasks is termed the target domain. Conversely,
the data, and experiences, shaping the development of the robotic system are
called the source domain. The core objective of Sim2Real is to devise
algorithms and methodologies capable of effectively bridging the disparities
between these two domains, known as the Sim2Real gap. For instance, this gap
may manifest as dynamic differences or discrepancies in the sensing part,
where the simulated images and the real images are different. Although the
Sim2Real transfer is primarily centered on transferring Deep Reinforcement
Learning (DRL) policies from simulation to the real world, it can also be more
broadly considered as Machine Learning (ML) problems for the sensing part, for
the agent facing situations in the real world that have not appeared in
simulation. To address the Sim2Real gap, an array of methods is proposed,
including system modeling, dynamics randomization, and randomization for
sensing. Here we introduce the most used methods for small-scale cars.

The first approach for the real-world application of small-scale cars is
_Zero-shot Transfer_ , where the trained model is directly applied in real-
world settings. For this, the Imitation Learning (IL) approach is usually
involved with the dataset collected in the real-world platform. The agent is
trained to mimic the behavior of the expert [34, 90, 9, 56, 91, 55, 92, 65].
In addition to IL, _Zero-shot Transfer_ for DRL can be achieved with compact
observation and output space [93, 94, 95]. Another noteworthy approach is
_Transfer Learning_ , which aims to improve the performance of the target
agent in the target domain by transferring the knowledge contained in
different but related source domains [96], for example, for Sim2Real transfer,
the process typically includes initially pre-training a model in a simulated
environment and subsequently fine-tuning it using real-world data. When the
labeled data in the target domain is scarce or expensive to obtain, _Domain
Adaptation_ is used. As a subset of transfer learning, it seeks specifically
to minimize the distribution mismatch between the source and target domains,
allowing the model to generalize better across different domains. Another
frequently used approach is _Domain Randomization_ , in which the randomness
and variation are introduced during simulation to make the model more robust
to different real-world conditions [97]. It works by for example randomly
varying simulation parameters, such as lighting conditions, textures, object
appearances, and physics properties during training. For small-scale car
platforms, Duckietown and DeepRacer provide _Domain Randomization_ option in
the simulation, which is easier for the users to tackle Sim2Real issue [98,
10, 99, 38].

Figure 2: Comparison of two pipelines for the autonomous driving system. An
end-to-end system maps raw sensor inputs directly into control commands,
whereas a modular system includes multiple subsystems to process the sensor
inputs sequentially and output control commands.

##  III Benchmark Tasks

Autonomous driving systems for normal and small-scale vehicles are normally
categorized into two distinct pipelines: the _end-to-end system_ pipeline and
the _modular system_ pipeline. As illustrated in Fig. 2, a modular system
comprises multiple subsystems that perform various tasks such as perception
and localization, mapping, path planning, and control [100, 101]. Each
subsystem focuses on specific functionalities and tasks. First is the
perception system, where sensors such as cameras and LiDAR are used to gather
information about the surroundings, including lane markings, obstacles,
traffic signs, and other vehicles. Following this, the localization and
mapping system leverages GPS, odometry, IMU, or techniques like simultaneous
localization and mapping (SLAM) to precisely pinpoint the position within the
environment while concurrently creating a detailed map of its surroundings.
The perception system creates an intermediate representation of the
environment for subsequent utilization.

The representation module then uses the information from the perception module
and further processes the sensor data with sensor fusion techniques or creates
an object map with the predicted state of each object within the sensor range.
The combination of the perception module and the representation module can be
seen as the scene understanding, which provides an abstract high-level
representation of the environment [101]. Afterward, the planning system maps
out a safe and efficient route to reach the destination. Normally, in the
autonomous driving system, the planning phase is divided into two different
parts, namely global path planning and local path planning [102]. Global path
planning refers to the process of determining an optimal or feasible route
from the current position to its destination and is done considering the
entire environment and involves high-level decision-making. Local path
planning or path following, on the other hand, focuses on the immediate
surroundings of the vehicle and deals with making real-time adjustments to
adhere to the planned global path. Finally, the control module generates
driving commands based on the processed information. In control theory, the
primary goal is to minimize a cost function. Various methodologies are
employed for the control system, classical controllers such as the
Proportional–Integral–Derivative (PID) controller, Model Predictive Control
(MPC), and ML-based controllers such as IL or DRL.

An end-to-end system, on the other hand, is characterized by a unified
architecture that aims to learn the entire mapping directly from raw sensor
inputs to driving actions without explicitly decomposing the task into
separate modules [103, 104, 105]. It involves training a comprehensive
learning model with ML methods, IL or DRL, directly process raw sensor data
and output control commands. End-to-end systems potentially simplify the
system architecture by eliminating the need for handcrafted modules and
feature engineering.

In this section, our focus is directed toward discussing various tasks studied
in the literature and the methods developed to handle these tasks using
modular or end-to-end systems. The detailed methodologies for achieving these
tasks will not be discussed, instead, we will refer to the respective papers.
The benchmark tasks categorized in this survey are illustrated in Fig. 3.

Figure 3: Research in the literature concerning small-scale cars is classified
into various benchmark tasks in this survey, ranging from localization and
mapping, path planning and following, lane keeping to racing, and cooperative
driving.

###  III-A Localization and Mapping

The localization and mapping, which, although commonly viewed as a subsystem
within the modular autonomous system, are also regarded as distinct and
extensively researched topics in the literature.

GPS can directly provide the location of the vehicle, but the accuracy is
affected by a list of factors, such as atmospheric conditions, satellite
geometry, signal blockage (buildings, trees, etc.), multipath interference,
and the quality of the GPS receiver. Differential GPS (DGPS) systems and
augmentation techniques can be used to enhance GPS accuracy by correcting some
of these error sources, but cannot fulfill the accuracy which is extremely
important when it is small-scale cars. Typically, an IMU consists of various
sensors, such as accelerometers and gyroscopes to measure and report
acceleration and angular velocity in three dimensions: pitch, roll, and yaw
with high rates. However, errors such as sensor drift and integration errors
can accumulate over time with IMU, leading to inaccuracies in long-term
measurements. To address the issues, the two sensors are often integrated
through sensor fusion methods, such as Kalman filter (KF) [106] or its
variations as extended Kalman filter (EKF) or unscented Kalman filter (UKF)
[107], to enhance accuracy and reliability in determining the position and
orientation. As in the studies by [108, 109, 110], that use BARC small-scale
cars, KF makes the fusion of IMU, wheel encoders, and indoor GPS measurements
to achieve an accurate localization. In recent research, [111] presents an
approach to use KF to fuse multiple-camera images with the AprilTag system and
wheel odometry. The results are validated with the OptiTrack Motion Capture
System (MoCap). OptiTrack system can also be used as a global localization
system and provide high accuracy pose estimation but with higher cost [112],
more detail will be discussed in Section IV-E.

For small-scale vehicles, the primary sensor for localization and mapping
components utilized is the LiDAR. They collaborate with a pre-defined map or
SLAM technology to navigate and position the vehicle effectively within its
environment [113]. A well-established method for localizing robot cars with
LiDAR sensors on the pre-defined map involves the use of a particle filter
(PF) as explored in [45] with F1TENTH. However, the computational demands of
the PF present challenges, especially for computation resources and space-
constrained small-scale robot cars. To enhance performance, [114] introduces
the Compressed Directional Distance Transform (CDDT) on the RACECAR platform.
This method focuses on expediting ray casting within 2D occupancy grid maps
and accelerating sensor model computations to mitigate computational expenses.
While comprehensive survey papers delve into various SLAM techniques [115,
116, 117, 118], encompassing feature-based, LiDAR-based, visual, and graph
SLAM, this study offers a concise overview of three prevalent SLAM methods
employed with small-scale cars: GMapping [119, 120, 121], HectorSLAM [122,
123, 124, 125, 72], and Cartographer [126, 127, 128].

GMapping, utilizing a particle filter-based approach, stands as one of the
most widely used SLAM algorithms, adept at handling non-linear sensors and
non-Gaussian noise [129]. To address computational expenses inherent in
particle filters, the Rao-Blackwellized Particle Filters (RBPF) algorithm is
introduced to combine odometry with scan-matching techniques. Based on
particles representing a sampling from a multimodal map proposal distribution,
this approach effectively closes loops without necessitating additional
methods. The efficacy of GMapping hinges on both 2D LiDAR data and odometer
information, utilizing the latter significantly to curtail the number of
particles involved in the mapping process. In contrast, HectorSLAM exclusively
depends on LiDAR sensors and leverages the high sampling frequency of modern
2D LiDAR [130]. It swiftly provides a 2D pose estimate at the scan rate
utilizing a scan-matching algorithm [130]. Upon receiving raw LiDAR data and
joint values, HectorSLAM undergoes preprocessing before importing the data to
the scan matching module, applying the Gauss-Newton approach [131] to estimate
the optimal pose. As robots traverse longer trajectories and handle
substantial data volumes, the limitations of particle-based approaches become
evident. Graph optimization algorithms emerge as more reliable and efficient
solutions for managing these large-scale datasets. As a typical Graph SLAM,
the Cartographer takes odometry data as an initial estimate for the local
optimizer [132]. This optimizer focuses on determining the most probable
relative pose between the scan and local submap coordinate frames. These
relative poses serve as constraints for a Sparse Pose Adjustment optimizer,
specifically engineered to address loop closure challenges. Cartographer
operates as a dual-pronged approach to the SLAM problem, comprising the front-
end (Local SLAM) and back-end (Global SLAM). The front-end executes critical
tasks like scan matching for loop closure detection and estimating the pose.
Conversely, the back-end optimizes poses regularly by forming a graph to
minimize loop-closing constraints, thereby minimizing successive pose errors.

Moreover, a compelling approach gaining traction involves leveraging ML
methods to address mapping and localization challenges. One notable instance
is the GALNet proposed in [133] implemented on the Autominy platform. GALNet
employs a Deep Neural Network (DNN), utilizing two timestamps of inertial,
kinematic, and wheel velocity data to estimate poses effectively. With the
success of Transformer architecture in various research fields, [134]
introduces the Perception-Action Causal Transformer (PACT) architecture. This
model constructs a representation from sensor data by autonomously predicting
states and actions over time, laying the groundwork for subsequent task-
specific networks for localization and mapping. Pre-trained models are
employed initially and later fine-tuned for specific tasks, demonstrating
validation on MuSHR with LiDAR sensor data. For image-based localization,
[135] proposes an effective framework leveraging a pre-trained local feature
transformer (LoFTR). This framework employs a constrained 3D projective
transformation between consecutive key images to establish a visual map on
Jetbot. These studies underscore the growing interest and potential of ML
methodologies, including neural networks and transformer architectures, in
revolutionizing mapping and localization paradigms by offering robust, data-
driven solutions that outperform traditional approaches.

###  III-B Path planning and following

Similar to localization and mapping, path planning and path following can be
regarded as distinct tasks for small-scale cars. While often studied together,
both are essential components that are merged to form a cohesive navigation
process within autonomous systems.

####  III-B1 Path planning

Path planning in autonomous driving refers to the process by which a self-
driving vehicle determines a safe and efficient route from its current
location to a desired destination while navigating through its environment
[136]. This task involves creating a trajectory or path that the vehicle can
follow, considering various factors such as obstacles, road conditions, and
traffic regulations, and aims to ensure safe and reliable navigation while
optimizing factors like travel time, and energy efficiency. Path planning
algorithms vary in complexity and we specify them into traditional [137, 138,
139] and ML-based techniques [140, 141].

##### Traditional methods

The most traditional method is Dijkstra’s algorithm [137], which is used in
[142] for path planning with SLAM methods to obtain the global map
information. Among many applications of the A* [138], [40] uses the A*
algorithm to obtain optimal paths for a racing car, and [143] integrates the
Hybrid A* algorithm [144] with a learned model utilizing a cost function. This
integration aimed to tackle unforeseen obstacles encountered during
navigation, which often escape the detection of traditional global path
planning algorithms. In the work by [145] and [146], genetic algorithms (GA)
are investigated to generate a global path for a mobile robot in the grid-
based environment. Furthermore, to extend the planning capabilities beyond the
sensor horizon, [147] utilizes a generative neural network trained on real-
world data to predict occupancy maps beyond sensor limitations, see Fig. 4.
This predictive capability assists in facilitating planning processes. Authors
leverage the Rapidly-Exploring Random Tree (RRT) algorithm [139] to generate a
global path, followed by a local planner that orchestrates trajectories until
the end of the predicted map.

Figure 4: Overall perception and control pipeline in [147]. The perception
module receives onboard sensor data and produces a predicted occupancy map
using a U-Net style generative neural network. The control algorithm receives
the robot state, predicted occupancy map, and goal point and generates
collision-free trajectories with RRT.

##### ML-based methods

Path planning, treated as an optimization problem, has also seen the
successful application of various ML methods, resulting in notably high-
performance outcomes. For instance, [148] uses Q-Learning, a classical RL
algorithm, to generate a global path for robots, which achieves a shorter and
smoother path compared with the RRT algorithm. A framework is introduced in
[149] comprising a mapper, global policy, and local policy for image-based
navigation for Jetbot. The mapper undergoes supervised learning using camera
images to generate an occupancy grid map. Subsequently, a global policy
employing DRL techniques takes both the map and the position as input to
determine the long-term goal. Following this, a planner, employing the A*
algorithm, computes the short-term goal, which is then forwarded to the local
planner for further execution.

Even with prior maps and acquired information, managing uncertainty during
planning and driving remains a significant challenge. To address these
uncertainties, [150] introduces a model-based RL algorithm incorporating a
probabilistic dynamic model. This method aims to mitigate uncertainty during
planning stages and prevent shortsighted decisions, illustrated in Fig. 5. In
the related approach, [151] uses Bayesian Residual Policy Optimization (BRPO)
with an ensemble of expert policies. Authors train the policy ensemble with
BRPO to diminish overall system uncertainty, enabling safe navigation within
partially observable environments. Demonstrated on the MuSHR platform,
proposed framework integrates a global localization system, ensuring
destination-reaching capabilities while avoiding collisions with other moving
vehicles.

Figure 5: An overview of the method in [150]. The probabilistic dynamics model
is trained using a multistep loss that considers how uncertainty propagates.
The trajectory tracking controller is used to predict a distribution of
closed-loop trajectories. The divergence constrained optimizer is used to find
a closed-loop trajectory with low divergence for MuSHR cars.

####  III-B2 Path following

Once the path is established, the path following becomes the subsequent step,
encompassing the capability of the vehicle to precisely track the designated
path in real time. Here, the control system interprets the planned trajectory
and orchestrates the essential actions required for the vehicle to adhere to
the intended path. Path following necessitates a continual adjustment of the
movements based on real-time sensor feedback and environmental alterations,
ensuring the vehicle maintains the desired trajectory accurately. Like path
planning, solutions for path following tasks encompass a wide spectrum,
including classical control methods like the PID controller, kinematic
controller [152, 153, 154], MPC controller [155, 156, 157, 158], and extending
to ML methods [159, 160, 149, 161].

In [154], an adaptive trajectory tracking control scheme, as shown in Fig. 6,
is introduced with adjustable gains to facilitate adherence to predefined
paths. The designed adaptive control gains aimed to streamline tuning efforts,
augment the convergence rate of tracking errors, and elevate trajectory
tracing performance. Meanwhile, the strategy proposed by [158] involves expert
interventions with pre-existing LiDAR-derived maps to further improve
performance. As for the ML methods, in the work presented by [159], error
states relative to reference trajectories are determined using vehicle state
data obtained from sensor fusion involving IMU and GPS with an EKF. A Neural
Network (NN) is trained using IL methods with a dataset collected from human-
controlled and MPC-controlled driving scenarios. Trained NN maps error states
to control commands. To avoid human-induced errors and large datasets, RL has
also been widely adopted in path following tasks, as evidenced by [160], where
the Deep Deterministic Policy Gradient (DDPG) algorithm is applied to guide an
Autominy car along the desired path, aiming to minimize cross-track errors.
Similarly, in the study by [149], a global policy is determined using DRL,
while a local planner processes received images with another DRL agent to
derive the final action for the path following. To address the data
inefficiency challenges with RL, [161] then introduces a policy gradient-based
policy optimization framework leveraging a first-principles model for path
following task with a JetRacer. This framework facilitated the learning of
precise control policies with limited real-world data.

Figure 6: Architecture of the proposed adaptive trajectory tracking control
system in [154].

###  III-C Lane Keeping

Next, we discuss another fundamental driving behavior for small-scale
vehicles, lane keeping, which has been a pivotal focus in extensive research
across various platforms [7, 10, 11]. While the lane keeping task shares
similarities with the path following, there are distinct differences between
the two. Path following involves adhering to a predetermined path and
utilizing real-time feedback to maintain desired trajectories safely. On the
other hand, the objective of the lane keeping task is to guide the small-scale
vehicle within the designated lane, conforming to specific tolerances for
deviation from the lane center. This task requires the small-scale car to
implicitly follow a path dynamically defined by the vehicle in real time.
Consequently, this task predominantly relies on visual input, utilizing RGB
cameras to monitor the position within the lane continuously. According to the
methods used, we categorize research works into two groups, traditional
methods and ML-based methods.

####  III-C1 Traditional methods

Traditional methodology typically comprises two integral components:
perception and control, similar to various other driving tasks. The perception
module is responsible for processing sensor data, often obtained from cameras,
to extract crucial information necessary for lane keeping. It can be divided
into two parts, lane detection for analyzing the sensor data to identify and
track lane markings on the road; and feature extraction for calculating
relevant metrics such as lateral deviation and orientation deviation to
determine the position of the car within the lane. The control module then
utilizes the information extracted from the perception module to regulate the
trajectory and ensure it remains within the designated lane. Control
algorithms such as PID controllers or other rule-based systems are employed to
interpret the deviation data and adjust the steering angle accordingly.

In [7], a multi-step image processing pipeline, shown in Fig. 7, is devised to
address the lane keeping task for Dukietown using traditional computer vision
methodologies. With k-means clustering, Canny filter [162], Hue-Saturation-
Value (HSV) colorspace thresholding, and probabilistic Hough transform [163],
individual line segments are extracted, afterwards, A nonlinear non-parametric
histogram filter is employed to estimate the lateral displacement concerning
the right lane center and the angle offset relative to the center axis. These
estimates hold immense significance within the lane-keeping task, serving as
critical inputs for the subsequent control module. The control module employs
a PID controller, utilizing these estimated parameters in real time to
minimize deviations and steer the vehicle along optimal trajectories within
the designated lane. Later [164] presents another similar pipeline for the
Donkeycar platform, in which the H layer of HSV colored image is used to go
through Gaussian kernel filter for noise filtering and Sobel edge detector
[165], following the probabilistic Hough transform for lane detection. Then a
list of rules is proposed to compute the steering angle for the car based on
the detected lines. While traditional methods serve well for basic detection
and control tasks, their limitations in generalization prompt the need for
more advanced methodologies.

Figure 7: Different outputs of the multi-step image processing pipeline
proposed by [7] and later employed in [94, 95]. It starts from the image input
from the camera, proceeds to detect road markings according to the color with
HSV thresholding, detects marking edges with canny filter, and finalizes with
detection marking used to estimate the lateral displacement and angle offset.

####  III-C2 ML-based methods

Similar to the other driving tasks, ML-based methods are used to improve lane
keeping capabilities and their generalization. Here, we divide them into two
subcategories, namely modular system and end-to-end system, as in Fig. 2.

##### Modular system

For the lane keeping task, an ML-based modular system typically replaces
traditional perception or control systems with more sophisticated ML-based
algorithms. In [166], Convolutional Neural Network (CNN) models are used to
predict the lateral displacement and the angle offset. These predictions were
then used in conjunction with the lane filter and PID controller derived from
[7] to control the Duckiebot. Besides replacing the perception module, [95,
94] use the same perception module from [7], but replaces the traditional
controller with DRL algorithms that use the outputs from the perception module
and achieves better performance than simple PID controller with multitask
driving, not only lane keeping. With this modular system, the Sim2Real
capabilities of the Duckiebot are also improved. For the Sim2Real capabilities
improvement or generalization, [43] presents a modular system specifically
tailored for lane keeping tasks. First, the perceptual system is trained with
a mixture of simulation and real world datasets and map images into a shared
latent space. Then, a list of DRL algorithms is employed, utilizing these
latent features as inputs to control the Donkeycar. Furthermore, the evolution
of transformer architectures, particularly the Vision Transformer (ViT) [167],
has gathered increasing interest, competing with conventional CNN. For lane
keeping tasks, for instance, in the work by [168], a pre-trained ViT is
employed as a backbone network, see illustration in Fig. 8. This ViT model is
further fine-tuned using limited datasets obtained from the driving
environment to generate segmentation of the environment. Subsequently, a
potential-field-based controller utilizes the segmentation output generated by
the ViT as input and produces steering commands for control.

Figure 8: In [168], image patches are encoded using a ViT and predict a class
label for each patch. Then, the coarse segmentation output is used for a
potential-field based controller.

##### End-to-end system

A more highly researched system is the end-to-end system for lane keeping
tasks, in which the agent receives raw sensor input and output control
commands for lane keeping tasks. This control framework encompasses both IL
and DRL. IL involves the system learning from expert demonstrations or human-
driven data to emulate behaviors, decision-making, and actions observed in
provided training demonstrations. Common IL algorithms include Behavioral
Cloning (BC) [169], Generative Adversarial Imitation Learning (GAIL) [170],
Inverse Reinforcement Learning (IRL) [171], Dataset Aggregation (DAgger)
[172], and others. Since BC is a straightforward approach in IL that involves
training a model to mimic expert behavior by directly mapping observed states
to corresponding actions without understanding the underlying decision-making
process, it is one of the foundational algorithms used for lane keeping tasks.
For example, in [49, 173], CNNs are trained with image data to predict
directly the steering angle for control under various evaluation metrics
within the DeepRacer platform. However, the most crucial drawback of BC is the
compounding errors, especially when the model encounters states not seen
during training, leading to divergence from expert behavior. It is sensitive
to errors in the training data and might not capture the nuanced decision-
making aspects of the expert. To improve the generalization and
transferability of Sim2Real in the Donkeycar platform, [92] proposes
incorporating uncertainty during training. Similarly, [174] augments the
dataset by image style transfer [175]. BC, GAIL, and DAgger are compared by
[176] within the Duckietown environment, and apply Domain Adaption to
facilitate the Sim2Real transfer issue. In addition to IL, DRL stands out as
another commonly used method with raw sensor data as input. [10, 177, 178]
employ raw images directly into DRL controllers, while to speed up
convergence, [179, 180, 99] preprocess input images, employing simple
techniques to reduce input dimensions. For enhanced control smoothness, [181]
combines Conditioning for Action Policy Smoothness (CAPS) with RL, addressing
jerky control issues. For Sim2Real transfer capabilities, [38] includes delays
and sampling rate as additional agent observations at training time to improve
the robustness of DRL policies for Sim2Real transfer of the DeepRacer
platform. Additionally, besides image inputs, raw LiDAR data serves as input
in studies such as [47], where DRL algorithms navigate an F1TENTH car within
structured environments using raw LiDAR input.

To further enhance convergence speed and boost generalization capabilities,
the end-to-end systems often integrate autoencoders [182]. It consists of an
encoder and a decoder: the encoder compresses input data into a lower-
dimensional representation through a sequence of neural network layers, while
the decoder reconstructs the original input from the compressed representation
in the latent space. The training objective of an autoencoder is to minimize
the difference between input data and the reconstructed output. This
encourages the network to learn an efficient representation of the data in the
latent space, subsequently utilized as input for control networks. As outlined
in [183, 184], autoencoders are initially trained to compress image data into
lower-dimensional representations. Following this compression, BC is employed
to train Duckiebots using expert trajectories. Autoencoders are frequently
integrated with DRL algorithms, for example, [42, 185] utilize variational
autoencoders (VAE) [186] to compress images and use the resulting latent
features as inputs for DRL algorithms. This approach, compared to DRL
algorithms with raw image inputs, demonstrates faster convergence and improved
learning efficiency.

###  III-D Car following and overtaking

Among the previously mentioned tasks, car following and overtaking with small-
scale cars remain relatively understudied areas. Nonetheless, they hold
significant importance for research on autonomous small-scale vehicles.

####  III-D1 Car following

Car following maneuver is the capability of an autonomous vehicle to maintain
a safe and appropriate distance from the vehicle ahead while driving on the
road. It requires the vehicle to track and follow the movements of the leading
vehicle, adjusting its speed and position accordingly to ensure a safe,
comfortable, and efficient driving experience. The existing literature on car
following maneuvers with small-scale cars is relatively scarce, with a few
notable studies standing out. A DDPG algorithm with an extended look-ahead
approach is proposed in [187] for longitudinal and lateral control within
vehicle platooning scenarios. For perception, LiDAR technology gauges inter-
vehicle distance, while an IMU provides acceleration data. Additionally, a V2V
system employing Wi-Fi communication transmits leader information to the
follower vehicle. Similarly, in [188], a Cooperative Adaptive Cruise Control
(CACC) system is implemented utilizing Deep Q-learning (DQN) [189]. This
system enables the follower vehicle to dynamically adapt and maintain
appropriate inter-vehicular distances. Notably, both these studies rely on V2V
communication to achieve effective car following behavior. In contrast, [95]
introduces a new perspective by realizing multitasking driving, encompassing
car following and lane keeping, solely relying on visual sensors without the
need for communication with other vehicles. In this approach, a pattern of
circles is affixed behind the leading Duckiebot, aiding the ego Duckiebot in
sensing distance and speed to mimic human driver behavior. A modular system is
deployed, where the perception module extracts compact affordance information,
and a DRL algorithm controls the Duckiebot based on this information. Despite
its simplicity, this system yields promising results. As an imperative area
for advancement, future research should emphasize car following behavior with
small-scale cars, aiming to establish a foundational basis for real-world
driving applications.

Figure 9: Framwork proposed in [95], the perception module leverages camera
images to produce impact attributes regarding the environment, then the DRL
control module utilizes the information to control the agent perform car
following behavior.

####  III-D2 Overtaking

Overtaking refers to the maneuver where an autonomous vehicle changes lanes or
positions itself to safely pass another vehicle traveling in the same
direction on the road. The purpose of overtaking is to move ahead of the
slower vehicle safely and efficiently while maintaining proper traffic flow.
For scaled cars, it is a challenge to obtain the overtaking maneuver, since
during the whole process, a precise localization system is required to avoid
collision with the slower vehicle. Therefore, there are just a few papers
focusing on the overtaking task with small-scale cars. A first contribution
comes from [94], where authors employ a modular system equipped solely with
onboard sensors to execute diverse driving tasks, encompassing lane keeping
and overtaking for Duckiebots. The framework leverages traditional machine
vision technologies to acquire compact affordance information, as discussed in
[7], and utilizes LiDAR sensors for distance estimation relative to other
vehicles. Afterward, the Long Short-Term Memory Soft Actor-Critic (LSTM-SAC)
algorithm assumes the role of the controller. Notably, LSTM aids the agent in
recognizing distinct phases within the overtaking process. Demonstrated
results affirm the efficacy of this proposed framework, managing both lane
keeping and overtaking tasks, and showcasing superior performance when
benchmarked against baselines. Another work regarding the overtaking task is
[190], where a dual control approach with MPC towards active uncertainty
reduction is proposed. This approach automatically balances the exploration-
exploitation trade-off, enabling the MuSHR car to actively minimize
uncertainty concerning the hidden states of other agents without compromising
expected planning performance. The difference compared with [94] from the
hardware side is that the vehicle being overtaken will yield, and the usage of
a known grid map of the track. The exploration of overtaking tasks remains
relatively limited within current research, signaling a crucial need for
further investigation and focus in the future.

###  III-E Obstacle Detection and Avoidance

Obstacle detection and avoidance refers to identifying and navigating around
obstacles or potential hazards in its path to ensure safe and uninterrupted
driving behavior. It utilizes various sensors, including cameras, LiDAR,
ultrasonic sensors, and other technologies, constantly scan the surroundings
to detect and classify obstacles. An effective obstacle detection algorithm
necessitates a range of essential abilities. In this paper, obstacle detection
algorithms are divided into different groups according to the usage of
different sensors, we discuss the most widely used two: camera and LiDAR
sensors.

####  III-E1 Vision-based approach

The vision-based approach relies on images captured by cameras as the primary
input for detection. This approach employs computer vision (CV) or ML
algorithms, prominently leveraging CNN, for effective detection tasks. Within
the realm of cameras used, the vision-based approach can be categorized into
two subclasses: monocular and stereo. Monocular image-based methods rely on a
single image for processing, while stereo methods utilize images captured by
two synchronized cameras. Details about the camera sensors are discussed in
Section IV-A.

Within monocular image-based methods, as outlined in [191], the obstacle
detection task for small-scale cars is typically categorized into three
primary domains: _appearance-based_ , _motion-based_ , and _depth-based_
methods. With _appearance-based_ methods, obstacles are typically identified
as the foreground within images. The primary challenge lies in distinguishing
relevant foreground or background elements based on established criteria, such
as color discrepancies [192] or texture features [193]. In the work by [36],
the RGB images captured by the monocular camera of a Duckiebot are initially
transformed into the HSV colorspace. Subsequently, the color filter from
OpenCV is employed to detect obstacles based on color differences. However,
the drawback is evident: success detection depends on the predefined color of
the obstacles, limiting adaptability to diverse scenarios. _Motion-based_
obstacle detection refers to identifying obstacles or objects in an
environment by analyzing motion vectors in the image, it involves comparing
successive frames of images to determine alterations in position, velocity, or
other motion-related characteristics. Objects that move or exhibit changes in
their motion characteristics are then identified as potential obstacles.
Optical flow analysis [194], background subtraction, differential methods. can
be employed for motion-based obstacle detection [195, 196]. _Depth-based_
methods utilize depth information extracted from images to discern the
distances and spatial arrangement of objects within the environment, aiding in
accurate object detection [197, 198]. Employing conventional image processing
techniques like this may fall short in meeting real-time application
expectations, primarily due to their inability to swiftly adapt to dynamic
conditions. Therefore, recent research endeavors have pivoted towards
enhancing obstacle detection speed by employing CNN and particularly
emphasizing the effectiveness of You Only Look Once (YOLO) [199]. YOLO is
specifically designed for real-time object detection, and its variations have
been employed to enhance success rates [200, 201, 202]. CNN offers promising
capabilities in overcoming the limitations of traditional methods, showcasing
greater adaptability and improved real-time performance in diverse and dynamic
environments. However, given the restricted computational power available in
small-scale cars, a balance must be achieved between accuracy and processing
time. In [65], YOLOv3 and Tiny YOLO are selected as detection algorithms for
the Go-CHART platform. [203] integrate GhostConv to the YOLOv4-tiny model to
achieve faster detection, while in [204], YOLOv5 is used to detect cones and
duckies within the Duckietown environment. Stereo image-based methods work by
using a dual-camera system that captures images from slightly different
angles, similar to the human binocular vision. This setup helps in perceiving
depth and reconstructing three-dimensional scenes by analyzing the differences
between the images from these cameras [205]. However, in the context of small-
scale cars, constraints such as cost considerations and limited space
availability restrict the usage of stereo cameras to select platforms, notably
including RACECAR [18] and Autominy [23].

####  III-E2 LiDAR-based approach

In addition to camera-based approaches, LiDAR is also widely used for obstacle
avoidance systems. The applications of 2D LiDAR are prevalent in compact car
platforms due to factors like size, price, weight, and overall compactness
considerations. However, due to the existing computational limitations of
these car platforms, innovative LiDAR data processing methods are often
unavailable. Consequently, raw LiDAR point cloud data or minimally processed
data is frequently used, involving procedures like imputing empty samples and
cleansing noisy data, to serve as input for neural networks executing
subsequent control tasks. In [48], the process of obstacle detection hinges on
identifying the minimum value derived from a cluster of LiDAR points,
following the determination of the position of the obstacle, a global path is
generated and the vehicle is directed using MPC. After performing data
cleaning, the minimum value obtained from the point cloud is adopted in [206]
as the distance measurement to a potential collision object. Subsequently, a
series of control command policies are executed based on this distance value.
In [12], Follow the Gap (FTG) method [207] is employed for object detection
and avoidance of an F1TENTH car. This technique involves computing the maximum
gap present within the LiDAR point cloud and subsequently determining the
steering control command. This command is derived from the heading angle
directed toward the center of the largest gap to the orientation of the ego
car, guiding the car toward the direction of the maximum gap center.

###  III-F Traffic sign recognition

Traffic sign recognition can be seen as a special element of object detection
tasks, requiring vehicles to initially detect and identify traffic signs
before responding appropriately to a diverse array of road signs and signals.
Unlike the generic object detection task, traffic sign recognition uniquely
relies solely on cameras. This dependence stems from the necessity to detect
and classify the visual characteristics of traffic signs. The process
typically commences with dataset acquisition, wherein cameras capture traffic
signs for later utilization. These captured images undergo a series of
preprocessing steps to refine quality, eliminate noise, adjust lighting, and
employ data augmentation techniques. Subsequently, ML models [208, 209, 210]
are trained using these compiled datasets for detection and classification.
However, research on traffic sign recognition is limited for small-scale cars
due to the unavailability of standard datasets that may differ across
platforms. Hence, researchers must prioritize dataset collection as an initial
step to train the networks. Future research should intensify exploration in
this direction, particularly focusing on mixing traffic sign recognition with
other driving tasks like navigation, lane keeping, or path following.

###  III-G Racing

Autonomous racing represents a distinctive facet of autonomous driving
research, demanding vehicles to leverage intricate algorithms, a suite of
sensors, and cutting-edge control systems. This specialized form of driving
involves navigating tracks, engaging in competitive races, and overcoming
dynamic racing challenges. Unlike standard driving tasks, racing pushes
vehicles to attain high velocities, extensively testing the dynamic limits of
these automated systems. When racing at high speeds, vehicles must swiftly
detect other vehicles or obstacles, demanding rapid reaction times.
Additionally, they must accurately localize their position concerning the
track and strategize dynamic trajectories to optimize performance [211]. While
extensive research has been dedicated to full-size racing competitions like
Roborace [212], our primary focus centers on small-scale car racing events
such as AutoRally [9], F1TENTH [12], Donkeycar [11], ORCA [15] and others.

####  III-G1 Modular system

In this section, we will explore the racing task using the modular system,
which similar to other tasks, primarily comprises two essential modules: the
perception and control modules. The perception module plays a critical role in
racing tasks as it assists the vehicle in determining its position on the
racetrack. In the racing context, swift and accurate reactions are essential
to fulfill subsequent control tasks. Thus, a racing-specific perception module
must balance speed and precision to enable quick and accurate responses during
racing scenarios. For this task, the most commonly used perception module is
the fusion with GPS and IMU as discussed in Section III-A. However, when
states or sensor measurements that are not approximately linear in the
measurement time frame, [9] and [51] use factor graphs combined with
incremental smoothing and mapping 2 (iSAM2) [213], to fuse GPS and IMU
measurements and output smoothing estimation. For the issues with non-linear
dynamics and non-linear measurement models, [55, 54, 53] use the particle
filter to fuse the camera images with IMU, and GPS. Additionally, a single
camera can serve as a perception sensor for racing tasks. For example, in
[56], CNN is used with a single monocular camera to predict the cost map of
the track in front of the vehicle which is directly useable for online
trajectory optimization with MPC. Similarly, [214] uses a top-down lane cost
map CNN and the YOLOv2 CNN to extract feature-input values and a two-point
visual driver control model (TPVDCM) as the controller to control the vehicle.

Figure 10: In [56], the cost map of the track in front of the vehicle is
predicted with CNN.

Same as the perception module, the control module also has a high demand for
timely reactions, often surpassing the importance of the perception module.
The most commonly used control module in racing is MPC, which is a control
strategy that utilizes a dynamic model of the system to predict its future
behavior and make control decisions based on optimization criteria. In [15],
the authors use onboard IMU sensors and a global localization system to
acquire the states of the car within the ORAC platform. Subsequently, a path
planner and a Nonlinear MPC (NMPC) are utilized to guide the racing process.
Later, in [51], a sampling-based MPC algorithm called the Model Predictive
Path Integral Control algorithm (MPPI) is introduced for Autorally. This
algorithm presents a novel derivation of path integral control, offering an
explicit formula for controls across the entire time horizon. A notable
attribute of MPPI is its capability to generate entirely new behaviors
dynamically, enabling the controller to drive the vehicle to its operational
limits. Then, a few improved versions are proposed, such as the robust
sampling-based MPC framework based on a combination of model predictive path
integral control and nonlinear Tube-MPC [215] and best response model
predictive control based on a combination of the game-theoretic notion of
iterated best response, and an information-theoretic model predictive control
algorithm [216]. When the dynamic model lacks perfect accuracy, it introduces
potential disparities between the predicted and actual behavior of the system.
To address this challenge, Learning Model Predictive Control (LMPC) integrates
learning algorithms with MPC, aiming to refine control strategies in the face
of uncertain or changing dynamics. Compared to traditional MPC, LMPC
continually enhances its performance, especially in scenarios where complete
knowledge of system dynamics is unavailable or subject to variation. The
learning mechanism algorithms such as RL, Gaussian processes, neural networks,
or other adaptive learning methods in LMPC continuously update the predictive
model based on collected data and feedback from the system, to achieve better
performance, adaptability to changing system dynamics, and robustness in
scenarios where precise models might be unavailable or incomplete. [53]
establishes the connection between MPC and online learning, and proposes a new
algorithm based on dynamic mirror descent (DMD) and MPC (DMD-MPC), which
provides a fresh perspective on previous heuristics used in MPC. In [217],
historical data is used to construct secure sets and approximate the value
function, facilitating LMPC to learn and improve from past experiences within
BARC platform. Later, [218] applies Gaussian processes to correct the model
mismatch, then uses MPC for tracking pre-computed racing lines using this
corrected model for an F1TENTH car. For more responsive control, [110] uses
Linear Parameter Varying (LPV) theory to model the dynamics of the vehicle and
combine it with MPC (LPV-MPC), which can be computed online with reduced
computational cost. To swiftly identify unsafe conditions, [54] propose a
Perceptual Attention-based Predictive Control (PAPC) algorithm, where MPC is
first used to learn how to place attention on relevant areas of the visual
input and ROI, and output control actions as well as estimates of epistemic
and aleatoric uncertainty in the attention-aware visual input of an Autorally
car. [219] introduces a local, linear, data-driven learning method for error
dynamics within the LMPC framework. This approach exhibits increased
robustness against parameter variations and limited data compared to prior
LMPC implementations. Pure pursuit is another control algorithm used in racing
tasks [220]. A key limitation in model-based control methods is that they
require localization during the race, where the motion capture system is
sometimes used [221, 222], but it is still limited by requiring a map of the
track or a set of cameras and the hardware requirement for real-time
localization.

####  III-G2 End-to-end system

Figure 11: End-to-end IL system used in [91] for an Autorally racing car.

In addressing the challenges within modular systems and enhancing racing
performance, various studies have proposed an end-to-end system that
incorporates methodologies such as IL and RL [223]. The authors in [91]
introduce an end-to-end IL system depicted in Fig. 11, where a learner network
needs to imitate an expert. The expert fuses GPS and IMU for state estimation
and uses an MPC as controller, the learner uses a DNN as control policy to map
raw, high-dimensional observations to continuous steering and throttle
commands. [224] presents a deep imitative RL framework for end-to-end racing
with camera input, where IL is used to initialize the policy, and model-based
RL is used for further refinement by interacting with an uncertainty-aware
world model. To improve the stability of the racing system, [225] designs a
residual control system, in which multiple Bayesian Neural Networks (BNNs) are
trained with two camera inputs and GPS measurements to control an Autorally
car in an end-to-end fashion. Incorporating LiDAR technology into end-to-end
systems has also garnered attention. A model-based RL approach is employed in
[226] using raw LiDAR input on the F1TENTH car to navigate racetracks
effectively. Subsequently, in [227], LiDAR data is used as input for DQN to
control the F1TENTH car. This study also provides a comparative analysis
between neural network methodologies for processing LiDAR data and evaluates
two Sim2Real approaches. Although RL can be used for racing tasks, the well-
known low sample efficiency issue still holds back further development. For
this issue, [228] propose an efficient residual policy learning method with
the raw observation of LiDAR and IMU, in which first a controller based on the
modified artificial potential field (MAPF) is used to generate policies, then
DRL algorithms are used to generate a residual policy as a supplement to
obtain the optimal policy with increased efficiency. Similarly, [229] presents
a trajectory-aided learning (TAL) method that trains DRL with raw LiDAR input
by incorporating the optimal trajectory into the learning formulation. [230]
also presents a residual vehicle controller that learns to amend a traditional
controller with a similar idea. To tackle the safety issues that usually occur
in RL training, [231] uses a Viability Theory-based supervisor to recursively
feasible vehicle safety during the training. Furthermore, to improve the
robustness of RL, [232] first train a teacher model that overfits the training
track, moving along a near-optimal path, then use this model to teach a
student PPO model the correct actions along with randomization.

####  III-G3 Drifting

Autonomous drifting can be seen only as a facet of racing, but also as an
individual task within autonomous vehicle control. This maneuver is
characterized by intentionally inducing oversteer, resulting in the rear tires
losing traction and enabling the vehicle to elegantly slide or ”drift” through
a turn. Autonomous drifting requires advanced control systems and algorithms
that can precisely manage the speed, steering, throttle, and braking to
maintain control while deliberately sliding through a corner. Few studies work
on the drifting problems for small-scale cars, the key point is the awareness
of the kinematic model and dynamic of the vehicle and the accurate estimation
of the state such as position, and steering, to control the car appropriately.
For instance, [8] uses a dynamic bicycle model for a BARC car, which has a
single rigid body model with lumped rear wheels and front wheels. Then EKF is
utilized to fuse the sensor measurement from IMU and encoders with the dynamic
bicycle model and Pajecka tire model [233]. Moreover, optical flow is used to
estimate the velocity, and a Linear-quadratic regulator (LQR) controller with
designed equilibrium drifting points is used to control the vehicle. Later in
[234], EKF is also applied for state estimation but with a six-state bicycle
model with linear front and rear-wheel tire forces. In most cases, an accurate
vehicle dynamics model is often elusive. Hence, [235] proposes an approach to
tackle the drifting park problem with BARC cars by segmenting it into distinct
phases: the normal driving regime and the sliding regime. During the normal
driving phase, a nonlinear MPC operates with a predefined kinematic model. As
the vehicle transitions into the sliding phase, reliance on this model
diminishes. To navigate this shift, a feedforward-feedback controller takes
charge, orchestrating safer maneuvers adeptly under sliding conditions.
Another challenge in previous studies pertains to the singularities embedded
within popular tire models. Specifically, these models exhibit singularity
issues, notably with tire slip angles becoming singular at lower vehicle
speeds due to the presence of the vehicle velocity term in the denominator. In
response, [236] introduces a fused kinematic-dynamic bicycle model accompanied
by a nonlinear MPC system for a RACECAR. This pioneering approach harmonizes
the planning and execution of dynamic vehicle maneuvers within a unified
framework, optimizing the entire process cohesively.

Although autonomous racing does not commonly occur in daily driving scenarios,
it is still an emerging field in intelligent vehicles and transportation
systems. The intriguing aspect lies in the operation of autonomous small-scale
cars pushing the boundaries of vehicle capabilities [211]. Operating at high
speeds with minimal reaction time within dynamic environments, autonomous
racing with small-scale cars emerges as a compelling area within the
autonomous driving domain.

###  III-H Cooperative driving

In comparison to the scenarios mentioned before which typically only involve
one controlled vehicle, cooperative driving refers to a collaborative approach
where multiple autonomous vehicles interact and communicate with each other to
achieve common goals, navigate shared spaces, and enhance overall traffic
efficiency and safety. It relies on vehicles exchanging information, such as
their positions, speed, intended trajectories, and other relevant data, to
make collective decisions. The decision-making processes of cooperative
driving include _centralized control_ and _decentralized control_. In the
_centralized control_ system, a central controller oversees and coordinates
the actions of all vehicles involved in the cooperative driving system. This
central entity collects data from all vehicles, processes the information, and
makes decisions. On the contrary, in a _decentralized control_ system,
decision-making is distributed among individual vehicles within the
environment. Each autonomous vehicle makes independent decisions based on
local information gathered from its sensors and communication with nearby
vehicles. The vehicles negotiate and collaborate among themselves, sharing
relevant data and coordinating actions to achieve common goals.

Figure 12: Overall schematics of the multi-vehicle, mixed reality RL approach
in [41]. Both virtual and real DeepRacer vehicles exist within the simulation
that manages the physics of the virtual cars and emulates collisions in mixed
reality.

Examples of cooperative driving encompass various aspects, spanning from
mapping, path planning, obstacle avoidance to roundabout navigation, and
traffic jam mitigation. For example, in the study by [237], a feature-based
map registration method is utilized for a collaborative SLAM system. Three
distinct vehicles collaborate, enhancing SLAM efficiency by achieving faster
mapping capabilities through collaborative efforts. In the work by [238], a
multi-vehicle path planning strategy Enhanced Conflict Based Search with Task
Assignment (ECBS-TA) [239] is employed for MuSHR cars. It involves optimal
task assignment considering discrete workspace representation. Following this,
collision-free trajectories are planned for all vehicles using decentralized
MPC for precise path tracking. Addressing safety concerns, [240] devises a
distributed priority assignment algorithm called priority-based non-
cooperative distributed model predictive control (P-DMPC). This algorithm
elevates the priority of a vehicle based on the number of potential collisions
anticipated along its planned trajectory, thereby enhancing safety in multi-
vehicle scenarios. [41] introduces a mixed reality framework depicted in Fig.
12 tailored for safe and efficient RL driving policies in multi-vehicle
systems. This setup includes one physical vehicle and sixteen virtual
vehicles. The RL agent demonstrates capabilities in lane keeping and obstacle
avoidance within this complex multi-vehicle environment. In the case of
navigation through a roundabout, the vehicles employ cooperative strategies to
merge into the appropriate lane within the roundabout by communicating their
intentions to enter the roundabout and negotiate their entry sequence.
Vehicles need to follow certain merge and yield protocols, which for example
vehicles yield to others already within the roundabout, vehicles inside the
roundabout adjust their speeds and trajectories. To achieve this scenario,
[59] utilizes RL algorithms to control a set of miniature cars successfully
coordinated at a roundabout and ensure a smooth merge, and demonstrates the
results in University of Delaware’s Scaled Smart City (UDSSC) testbed. Later,
[57] develops a decentralized optimal control framework for 9 cars in a multi-
lane roundabout in UDSSC and eliminates stop-and-go driving while preserving
safety.

Another driving scenario to discuss is traffic jam mitigation, which remains a
focal point in cooperative driving, emphasizing the collaborative endeavors
among multiple autonomous vehicles to prevent or alleviate traffic congestion.
The primary goal is to sustain optimal traffic flow and speed, particularly in
unexpected scenarios such as abrupt braking or lane closures. This requires
vehicles to coordinate actions collectively and respond to dynamic
environmental changes. For instance, [66] introduces a framework that modifies
the intelligent driver model (IDM) [241] and the MOBIL lane change model [242]
for cooperative driving. Additionally, [243] and [244] propose decentralized
optimal traffic control methods. Their approaches demonstrate reduced travel
times compared to baseline scenarios involving human-driven vehicles lacking
connectivity. [24] presents the CoRoLa Car Platform, and achieves multi-
vehicle platooning driving with Adaptive cruise control (ACC). Furthermore,
recent research has explored human-robot cooperation ([245, 246]), as well as
collaborations between Unmanned Ground Vehicles (UGVs) and Unmanned Aerial
Vehicles (UAVs) [247]. These studies delve into cooperative methodologies
between different vehicle types, fostering improved traffic management and
congestion mitigation through collaborative efforts.

Figure 13: Diagram of trajectory planner with C-MOBIL and C-IDM in [66]. The ego vehicle plans a trajectory and velocity profile based on its state and the actual state of the neighboring vehicles. TABLE II: Overview of research in different tasks for small-scale cars. Task Category | Method | References
---|---|---
Localization and Mapping | KF |  [108, 109, 110, 219]: BARC; [111]: QCar
PF |  [45]: F1TENTH; [114]: RACECAR
GMapping |  [122]: Roborace; [123, 125]: -
HectorSLAM |  [119, 120, 121]: -
Cartographer |  [248]: F1TENTH; [127]: Innopolis UGV; [126, 128]: -
GALNet |  [133]: Autominy
PACT |  [134]: MuSHR
LoFTR |  [135]: Jetbot
Path planning | Classical | Dijkstra’s algorithm |  [142]: -
Hybrid A star |  [143]: QCar
GA |  [145, 146]: -
RRT |  [147]: RACECAR
ML-based | RL |  [149]: Jetbot; [150]: MuSHR; [98]: Duckiebot; [148]: -
BRPO |  [151]: MuSHR
IL |  [249]: Donkeycar
Path following | Kinematic controller |  [154]: QCar; [152, 153]: -
MPC |  [158]: MuSHR; [155, 156, 157]: -
IL |  [159]: ART/ATK
DRL |  [160]: Autominy; [161]: JetRacer
Lane keeping | Classical | classical CV + PID |  [7, 250]: Duckiebot
classical CV + rule-based |  [164]: Donkeycar
ML-based | CNN + PID |  [166]: Duckiebot
classical CV + RL |  [95, 94]: Duckiebot
CNN + RL |  [43]: Donkeycar
ViT |  [168]: Duckiebot
IL (raw input) |  [49, 173]: DeepRacer; [92, 174]: Donkeycar; [176]: Duckiebot
RL (raw input) |  [10, 177, 178, 181, 38]: DeepRacer; [179, 99]: Duckiebot; [180]: JetRacer; [47]: F1TENTH
IL (autoencoder) |  [183, 184]: Duckiebot; [251]: -
RL (autoencoder) |  [42]: Donkeycar; [185]: Duckiebot
Obstacle Detection | Vision-based | Appearance-based |  [36]: Duckiebot
Motion-based |  [195, 196]: -
Depth-based |  [198, 197]: -
CNN |  [65]: Go-CHART; [203]: JetRacer; [204]: Duckiebot; [252]: Jetbot; [253, 254]: -
LiDAR-based | Minimum value |  [48, 206]: F1TENTH
FTG |  [12]: F1TENTH
Racing | Classical | iSAM2 + MPC |  [9, 51]: Autorally
Particle filter + MPC |  [55, 53]: Autorally; [218]: F1TENTH
CNN + TPVDCM |  [214]: Autorally
MPC |  [15, 255, 256]: ORAC
LMPC |  [217, 219]: BARC
LPV-MPC |  [110]: BARC
PAPC |  [54]: Autorally
Pure pursuit |  [220]: TUNERCAR
End-to-end | IL |  [91]: Autorally
BNN |  [225]: Autorally
RL |  [226, 227]: F1TENTH; [222]: ORAC; [228, 229, 232, 224]: -
Drifting | EKF, LQR |  [8, 234]: BARC
NMPC |  [235]: BARC; [236]: RACECAR
Car following | DRL |  [95]: Duckiebot; [187]: Donkeycar; [188]: QCar
Overtaking | DRL |  [94]: Duckiebot
MPC |  [190]: MuSHR
Traffic sign recognition | Classical |  [37]: -
Tiny-YOLO |  [210]: -
Cooperative driving | Centralized control |  [41]: DeepRacer; [59]: UDSSC; [24]: CoRoLa; [246]: Jetbot; [247]: QCar
Decentralized control |  [237]: QCar; [238]: MuSHR; [240]: $\mu$Car; [57, 243, 244, 16]: UDSSC; [66]: CamMini; [245]: Jetbot

##  IV Sensors and Sensor systems

In Section II-A, various platforms are explored, each employing diverse
sensors to perceive their surroundings and execute the tasks detailed in
Section III. Following that, we discuss the most commonly utilized sensors
across these platforms, providing their respective use cases.

###  IV-A Camera

An RGB camera provides a stream of information that is easily processed and
understood by humans. This helps researchers to better understand the
perspective of the robot. The amount of information provided by cameras is
huge, which allows the robot to perceive a lot of information with just one
sensor in a short amount of time. The downside of this is, that they need a
high bandwidth communication link to whichever part of the robot needs those
information. Also with the information in images being so densely encoded, it
is difficult for a robot to understand the environment.

The field of computer vision has emerged as a crucial scientific discipline,
facilitating the extraction of pertinent details from images and videos. In
recent years, the advent of deep learning models, such as YOLO [199], has
significantly contributed to enhancing the robot’s ability to interpret scenes
captured by cameras. It is worth noting that cameras face external influences,
such as varying lighting conditions, which can substantially alter the
captured images. In extreme scenarios, such as very dark environments or
instances where direct light affects the camera, the effectiveness of cameras
may be compromised. A big advantage of cameras is their wide pricing range,
starting from 15 USD, corresponding to images of different quality in regards
to resolution and dynamic range. They are also easy to interface with, usually
requiring only a USB connection. Some tasks can be accomplished only with
cameras, e.g. traffic sign or road marking detection [210, 257, 258].

Beyond RGB cameras, more advanced options exist, including those detecting
optical flow [259] or depth cameras [260], employing different methods to
ascertain the distance to objects. These advanced cameras provide both depth
information and standard RGB images. In the context of autonomous tasks for
single-car platforms discussed in this paper, cameras emerge as a ubiquitous
choice. Their cost-effectiveness and information-rich output make them the
preferred sensor for many platforms. Among the commonly used cameras in scaled
vehicles, the Raspberry Pi Camera stands out [7, 11, 31, 61, 22, 73, 29, 30,
65, 24, 60]. Designed for the Raspberry Pi, it combines affordability with
decent image quality, offering a resolution of 1080p and a dynamic range of
44dB. Another popular option used in the discussed platforms are the Intel
Realsense444https://www.intelrealsense.com/ line of cameras [19, 261, 21].
Those include two cameras and an IR projector, which allow to capture not only
an RGB image but also depth information of each pixel by means of
stereovision. The IR projector helps to improve accuracy in scenes with poor
textures. The manufacturer claims an accuracy in the centimeter range.

###  IV-B LiDAR

LiDAR, another widely employed sensor in numerous platforms, functions by
measuring the time taken for a beam of light to reflect off a surface. This
beam is emitted from multiple angles (typically ranging from 360 to 2048)
around the LiDAR, creating a detailed map of points representing objects in
the platform’s vicinity. There are also multi-layer LiDARs with laser beams
spanning up to 128 angles, providing the platform with information about
objects at different heights and generating a comprehensive 3D point cloud.
Available in various resolutions, LiDARs offer enhanced information,
particularly in intricate and structured environments. The typical scanning
frequency of LiDARs ranges from 5 to 20 Hz, with high-end models reaching a
detection range of up to 350 meters.

The distinct advantage of LiDAR over cameras lies in its direct identification
of the platform’s surroundings, including the precise position of objects
relative to the robot. In contrast, a camera first needs to identify an object
in an image and then estimate its position. Moreover, LiDAR provides
significantly more accurate positional information compared to estimations
derived from camera images. However, unlike cameras, LiDARs lack the ability
to discern details such as the color of the detected objects. This limitation
renders them unsuitable, for example, in tasks like traffic signs or lane
detection. LiDARs also possess a notable advantage over cameras as they remain
unaffected by environmental conditions such as varying lighting conditions,
and they can operate effectively even in complete darkness. Additionally,
certain LiDAR models provide information on reflectivity levels, enabling a
rough distinction between different surface types. For instance, they can
differentiate highly reflective road markings from less reflective obstacles.
LiDARs are typically more expensive than cameras. The cheapest hobby LiDARs
start at around 150 USD for a simple one plane sensor. But the prices quickly
go up as the resolution or amount of planes increases.

Typically physically larger platforms use LiDAR sensors [10, 11, 12, 18, 8,
19, 20, 21, 261, 262, 65, 23, 60, 13] as they tend to be bigger and heavier
than cameras. Those platforms are also usually more versatile and used for
more different tasks and therefore the additional cost of a LiDAR is feasible.
In our investigation, all platforms utilizing LiDAR also integrate a camera to
address the limitations of LiDAR for specific tasks. The LIDAR that is used by
far the most on the platforms we investigated is the single plane RPLIDAR
555https://www.slamtec.ai/product/slamtec-rplidar-a3/ [11, 8, 20, 21, 261, 65,
23, 60]. It offers a reasonable detection range of 12m and a resolution of
0.225 degrees for small scale car platforms with a 360 field of view. And is
one of the cheaper options costing around 400 USD. But it only allows for a 10
Hz scan frequency. The second most popular, higher end solution is the Hokuyo
UST-10LX [12, 18], offering a 270 degree field of view and a 10m detection
range.

###  IV-C IMU and Encoders

Some sensors that are usually not used on their own but as an additional
source of information for other sensors are IMU and odometry encoders. Where
odometry encoders provide information about the angle by which each wheel of a
robot has turned and therefore allow us to estimate where it has traveled, the
IMU provides information on how fast a robot is spinning along each of its
axes aiding in estimating the total rotation.

The information of these two types of sensors is usually combined with other
absolute positioning inputs like a camera or a central localization system to
fuse them in a filter like a Kalman filter [106] or in any other kind of
perception module. IMUs are available at relatively affordable prices, ranging
from 15 USD. The two IMU sensors most commonly used in the discussed platforms
are the MPU9250 and MPU6050 [7, 11, 5, 21, 261, 60]. Although it has to be
noted, that not all publications mention the exact model of the IMU used. Both
sensors feature a 3-axis gyroscope, which measures the rotational speed, and a
3-axis accelerometer, which measures linear acceleration. The MPU9250
additionally offers a 3-axis magnetometer to incorporate measurements of the
Earth’s magnetic field into orientation sensing.

Odometry encoders can be found in several different forms, ranging from
magnetic encoders to optical ones. Their implementation is usually dependent
on the platform, as they need to be tightly integrated into the mechanics of
the robot. The encoders are usually below 10 USD. Almost all of the platforms
we investigated used an IMU and some form of encoder as they are cheap and
provide good position information, especially in slow indoor environments. The
exception was the smallest platform, primarily designed for swarm robotic
investigations [17], where extreme cost considerations led to the exclusion of
these sensors.

###  IV-D GPS

GPS is the least frequently used sensor with model scale platforms [9, 8].
Most of the discussed platforms are designed for indoor use, which eliminates
the use of GPS as the signal does not penetrate buildings. However,
recognizing the pivotal role GPS plays in full-sized vehicles, many model-
scale platforms opt to replace it with indoor central localization systems, as
detailed in Section IV-E, to enhance realism. GPS receivers are also
comparatively cheap at around 30 USD. However, for those seeking higher
precision, the cost can escalate to several hundred USD, especially for the
more advanced GPS RTK systems [263]. Such precision may become necessary for
model-scale platforms, as conventional GPS systems typically only offer an
accuracy of approximately 5 meters [264].

###  IV-E Central Localization system

In situations where the emphasis is on studying the interaction among multiple
vehicles rather than individual vehicle behavior, a central localization
system streamlines the task, allowing a more focused approach to other aspects
such as obstacle avoidance or overtaking. By decoupling the algorithms for
(multi) vehicle behavior from the perception of individual vehicles, central
systems enable the study of system dynamics in an idealized ”best case”
scenario. In this context, the term ”best case” refers to a situation where a
vehicle possesses precise knowledge of its own position and that of
surrounding obstacles. However, it is essential to acknowledge that this
assumption may not align with the complexities of real-world road situations.
Conversely, central systems find utility in simulating GPS positioning within
actual vehicles.

One of the cheapest options implemented in the considered platforms involves
the use of special markers or tags on each vehicle, which are observed by one
or multiple overhead cameras [71]. These markers, based on ArUco [265]
technology, are strategically placed on vehicles and key positions or
obstacles. Knowing the intrinsic and extrinsic parameters of the cameras,
existing libraries like OpenCV 666https://opencv.org/ can be employed to
detect these markers and estimate their position and orientation. As discussed
in Section IV-A, cameras are relatively inexpensive, and the markers merely
need to be printed out. While the resulting localization precision is
reasonable [266], it does decline with the distance of the markers from the
camera. Mitigating this issue involves the use of multiple cameras,
necessitating synchronization and precise positioning of all cameras.

Off-the-shelf solutions for tracking also exist from various manufacturers,
such as OptiTrack 777https://optitrack.com/ or VICON
888https://www.vicon.com/. These solutions predominantly rely on high-speed IR
cameras and reflective markers on the tracked objects. The systems inherently
provide position and orientation information for different marker assemblies
(referred to as rigid bodies) without requiring additional processing.
Notably, the accuracy of these systems surpasses that of solutions utilizing
RGB cameras, with manufacturers often specifying sub-millimeter accuracy.
However, the upfront cost is relatively high, typically exceeding 10,000 USD,
and in some cases, additional licensing fees for the requisite software may
apply [66, 16].

###  IV-F Other sensors

Several platforms discussed in Section II-A employ additional sensors to
either emulate sensors found in full-size autonomous cars or fulfill specific
tasks.

A prevalent type of sensor utilized in many platforms [7, 5, 32, 33, 21, 65,
6, 67, 69, 24, 267, 29] is the single-point distance sensor, available as
either ultrasonic or infrared. These sensors operate on the time-of-flight
principle, measuring the time taken for a wave to bounce back into the sensor.
Typically positioned at the front of vehicles, they often mimic radar sensors
and play a crucial role in tasks such as car following.

Line following sensors, consisting of an array of light sensors aimed at the
ground, are employed in certain platforms [21, 16, 21] to assist visual line
following, with a camera serving as a secondary input. While these sensors can
easily discern the line from the road, a limitation is their coverage area,
which is relatively small and located underneath the car. If the vehicle
deviates significantly from the line, the sensor may struggle to rediscover
it.

One platform [71] utilizes RFID readers to detect specific points of interest
on the road, such as the beginning of intersections or as indicators for turns
on a crossroad. Another platform [6] employs cliff sensors, which function as
downward-facing distance sensors, indicating if the part of the vehicle with
the sensor is suspended over a steep drop. This sensor serves to prevent the
vehicle from inadvertently falling off a precipice in the environment.

For platforms designed for use with multiple vehicles [17, 31, 6, 30],
infrared (IR) transmitters and receivers are employed. These facilitate cost-
effective communication between different robots.

###  IV-G Compute Units

To process the information from the sensors and compute actuator movement
according to their task, the platforms require a dedicated computational unit.

The prevailing choice across numerous platforms [7, 11, 21, 61, 30, 261, 16,
66, 62, 63, 71, 24] is the Raspberry Pi single-board computer
(SBC)999https://www.raspberrypi.com/. Built on a System on a Chip (SoC), the
Raspberry Pi features a compact physical size but offers modest computational
performance, with a 4-core 1.5GHz CPU. Despite its relatively low processing
power, the Raspberry Pi is well-suited for the majority of tasks performed by
these platforms.

A comparable alternative to the Raspberry Pi is the Nvidia
Jetson101010https://www.nvidia.com/de-de/autonomous-machines/embedded-
systems/. Also based on an SoC, the Nvidia Jetson stands out by incorporating
an additional GPU to accelerate machine learning tasks. Given their similar
connectivity, some platforms offer compatibility with either the Raspberry Pi
or the Nvidia Jetson [7, 11, 261]. However, the majority of platforms
exclusively support the Nvidia Jetson [12, 18, 19, 20, 22, 73, 13, 60, 68].

For platforms not requiring high computational performance on the vehicles
themselves, microcontrollers [5, 32, 29, 64, 17, 70, 67], such as Arduino, are
commonly employed. Although microcontrollers offer lower performance than
Raspberry Pi, they enable real-time code execution, crucial for interfacing
with low-level sensors and actuators. They also use less power and are
cheaper.

Conversely, some platforms leverage regular PCs as their computational unit
[10, 9, 262]. While PCs provide the highest performance, they have larger
physical footprints and higher power consumption.

##  V Future trends

In the previous sections, we have gone through the currently available small-
scale car platforms, their hardware configurations, and the autonomous driving
tasks achieved by these platforms. In pursuit of augmenting the capacity and
applicability of these small-scale car platforms across diverse age groups,
facilitating educational use for students, and supporting sophisticated
research purposes, we list the following opportunities for further
exploration.

Firstly, it is imperative to contemplate transforming the small-scale platform
into a more widely accessible resource for educational purposes and academic
research. This necessitates the establishment of a lower entry barrier,
reduced pricing, and the implementation of a comprehensive learning pipeline.
For educational usage, the first consideration should be given to the ease of
learning and maintenance for teachers. A more user-friendly starting point
will likely foster increased enthusiasm among educators, encouraging them to
incorporate the platform into their daily teaching activities. This, in turn,
will provide students with a challenging learning experience, ultimately
preparing them for the intricacies of fully autonomous driving in the near
future. For research applications, an in-depth exploration of the
accessibility of the small-scale car platform is essential. This entails
tailoring entry levels to accommodate various research goals, catering to
individuals with zero experience in robotics to seasoned professionals. The
platform should not only serve as a testbed for autonomous systems but also
ignite enthusiasm for exploring diverse robotic configurations. While some
platforms currently offer support for different entry levels, a more
comprehensive development is warranted to meet the diverse needs of the
research community.

Secondly, regarding the more academic research consumption, the versatility of
the platforms should be more considered. Specifically, concerning individual
small-scale cars, attention should be directed towards enhancing their
functional dimensions. In line with advancements in semiconductor technology,
where computation and sensor units are becoming more compact yet powerful,
platforms should incorporate more sophisticated sensors, thereby augmenting
the overall capabilities of the system. Expanding the scope of research
considerations, we propose a thorough examination of smart city
configurations. While smart cities hold significant potential in advancing
research on autonomous driving for small-scale cars, there exists a gap in
terms of accessibility, reproducibility, and standardization of best
practices. There is an urgent need for a common framework across the research
community, encompassing both hardware and software aspects. Furthermore,
existing smart city setups often overlook key elements such as weather
conditions and pedestrian interactions, which are important in real-world
driving scenarios. Addressing these factors in future research is essential
for developing comprehensive and realistic autonomous driving solutions within
smart city environments.

Thirdly, achieving fully autonomous driving necessitates the integration of
critical technologies such as Vehicle-to-Vehicle (V2V), Vehicle-to-
Infrastructure (V2I), and Vehicle-to-Everything (V2X). While these
technologies have been extensively researched in the literature, there remains
a noticeable gap in discussions of small-scale car platforms. Consequently,
the community needs to pivot towards exploring and advancing V2V and V2I
communications as the next steps in the pursuit of comprehensive autonomous
driving solutions.

##  VI Conclusion

In this paper, we offer an overview of the current state-of-the-art
developments in small-scale autonomous cars. Through a detailed exploration of
both past and ongoing research in this domain, we illuminate the promising
trajectory for the advancement of autonomous driving technology with small-
scale cars. We initially enumerate the presently predominant small-scale car
platforms widely employed in academic and educational domains and present the
configuration specifics of each platform. Similar to their full-size
counterparts, the deployment of hyper-realistic simulation environments is
imperative for training, validating, and testing autonomous systems before
real-world implementation. To this end, we show the commonly employed
universal simulators and platform-specific simulators. Furthermore, we provide
a detailed summary and categorization of tasks accomplished by small-scale
cars, encompassing localization and mapping, path planning and following,
lane-keeping, car following, overtaking, racing, obstacle avoidance, and more.
Within each benchmarked task, we classify the literature into distinct
categories: end-to-end systems versus modular systems and traditional methods
versus ML-based methods. This classification facilitates a nuanced
understanding of the diverse approaches adopted in the field. The collective
achievements of small-scale cars are thus showcased through this systematic
categorization. Since this paper aims to provide a holistic review and guide,
we also outline the commonly utilized in various well-known platforms. This
information serves as a valuable resource, enabling readers to leverage our
survey as a guide for constructing their own platforms or making informed
decisions when considering commercial options within the community.

We additionally present future trends concerning small-scale car platforms,
focusing on different primary aspects. Firstly, enhancing accessibility across
a broad spectrum of enthusiasts: from elementary students and colleagues to
researchers, demands the implementation of a comprehensive learning pipeline
with diverse entry levels for the platform. Next, to complete the whole
ecosystem of the platform, a powerful car body, varying weather conditions,
and communications issues should be addressed in a smart city setup. These
trends are anticipated to shape the trajectory of the field, contributing
significantly to advancements in real-world autonomous driving research.

While we have aimed to achieve maximum comprehensiveness, the expansive nature
of this topic makes it challenging to encompass all noteworthy works.
Nonetheless, by illustrating the current state of small-scale cars, we hope to
offer a distinctive perspective to the community, which would generate more
discussions and ideas leading to a brighter future of autonomous driving with
small-scale cars.

## Acknowledgments

This work was funded by ScaDS.AI (Center for Scalable Data Analytics and
Artificial Intelligence) Dresden/Leipzig.

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