Active Collision Avoidance System for E-Scooters in Pedestrian Environment
Xuke Yan ∗Department of Computer Science and Engineering, Oakland University,
Rochester, MI, USA Dan Shen ∗Department of Electrical and Computer
Engineering, Purdue University, West Lafayette, IN, USA 20xx-XX-XXXX

**footnotetext: These authors contributed equally to this work

##  1 Abstract

In the dense fabric of urban areas, electric scooters have rapidly become a
preferred mode of transportation. As they cater to modern mobility demands,
they present significant safety challenges, especially when interacting with
pedestrians. In general, e-scooters are suggested to be ridden in bike
lanes/sidewalks or share the road with cars at the maximum speed of about
15-20 mph, which is more flexible and much faster than pedestrians and
bicyclists. Accurate prediction of pedestrian movement, coupled with assistant
motion control of scooters, is essential in minimizing collision risks and
seamlessly integrating scooters in areas dense with pedestrians. Addressing
these safety concerns, our research introduces a novel e-Scooter collision
avoidance system (eCAS) with a method for predicting pedestrian trajectories,
employing an advanced LSTM network integrated with a state refinement module.
This method predicts future trajectories by considering not just past
pedestrian positions but also accounting for the behavior and locations of
surrounding individuals, acknowledging the influence of human interactions.
Leveraging the pedestrians’ estimated trajectories based on their historical
behaviors, we have devised an e-scooter path planning system that relies on an
interpolating curve planner, which can continuously analyze the driving scene,
understand the behavior of other road users, evaluate the risk assessment, and
predict its future trajectory. This proactive model is designed to ensure
unobstructed movement in areas with substantial pedestrian traffic without
collisions. Results are validated on two public datasets, ETH and UCY,
providing encouraging outcomes. Our model demonstrated proficiency in
anticipating pedestrian paths and augmented scooter path planning, allowing
for heightened adaptability in densely populated locales. This study shows the
potential of melding pedestrian trajectory prediction with scooter motion
planning. With the ubiquity of electric scooters in urban environments, such
advancements have become crucial to safeguard all participants in urban
transit.

##  2 1\. Introduction

Electric scooters (e-scooters) have seen a rapid rise in adoption in cities
worldwide over recent years [1]. Their compactness, efficiency, and ability to
swiftly traverse urban terrain make them a sought-after transportation choice
for short-distance travel. As cities grapple with increasing congestion and
pollution challenges, e-scooters offer an eco-friendly solution, reducing the
number of cars on the road while catering to the convenience-centric
lifestyles of modern urban dwellers [2]. However, the very attributes that
make e-scooters popular – their agility and speed – also present new safety
complexities, particularly when navigating pedestrian-dense environments [3].
Although there are designated lanes in some urban areas for bikes and
scooters, they often find themselves intermingling with pedestrians, either
due to the lack of dedicated infrastructure or user preference [4]. When an
e-scooter with the ability to travel up to 20 mph collides with a pedestrian,
the outcome can be grave [5]. Furthermore, the behavior of pedestrians can be
quite unpredictable. Sudden stops, changes in direction, or clustering in
groups can pose immediate challenges to e-scooter riders aiming to avoid
collisions [6].

Current e-scooter safety mechanisms often involve passive safety measures,
such as reflectors, lights, and bells. While these features are essential for
basic safety, they are reactive in nature. The e-scooter rider must constantly
be vigilant, reacting in real-time to potential obstacles or risky situations.
What is missing from this paradigm is a proactive approach that can anticipate
and navigate through potential collision scenarios before they pose immediate
threats [7]. In the realms of autonomous vehicles and robotics, research has
made significant strides in trajectory prediction and path planning [8, 9].
Predictive models enable these vehicles to anticipate the movement of other
road users and make informed decisions, thereby increasing safety. E-scooters,
by virtue of their smaller form factor and unique operating scenarios, require
tailored solutions that cater to their specific challenges.

Automated vehicles (AVs) are more popular and attractive with the tremendous
and impressive technological progress in recent years. Noticeable achievements
in both software and hardware have brought more automation functionality into
reality, which reduces severe and fatal crashes that are related to vulnerable
road users (VRU) [10], and research topics in VRU safety are an indispensable
part of the advent of AVs driving in cities. The collision avoidance system is
one of the AV active safety features for handling road departure VRU-related
collision problems, which has been devised and equipped on some production
vehicles [11]. Although many efforts have been put into the data set
development [12], e-Scooter risk assessment [13], and sensor fusion technique
[14], to the best of the authors’ knowledge, there are not many research
developments and progress on collision avoidance for e-Scooters with vehicles,
pedestrians, and bicyclists. This situation is more significant with the
increased popularity of micro-mobility vehicles like e-scooters. These tools
tend to be small, fast, agile, and share the road with both cars and VRU.

With this backdrop, our research seeks to bridge this gap, bringing the
scooter collision mitigation system with advanced trajectory prediction and
path planning to e-scooters. The proposed system employs state-of-the-art
methodologies, aiming to enhance the safety of e-scooter riders and
pedestrians alike, while ensuring fluidity in movement even in crowded urban
environments. Through this paper, we shed light on our methodologies, validate
our approach using renowned datasets, and elucidate the potential of
integrating proactive safety measures in e-scooters, ensuring safer urban
transit for all. Due to page limitation, this paper only introduces the
scooter-based collision mitigation system until the motion planner. The eCAS
integrated with the control part will be celebrated in future publications.

This work presents the following contributions:

  * •

e-Scooter Collision Avoidance System: Proposed a new e-Scooter collision
avoidance system with the basic concept, overall architecture with pedestrian
trajectory prediction and motion planner, operation process, and experiment
results. The new system can be utilized in the environment with both
e-scooters and pedestrians.

  * •

Pedestrian Trajectory Prediction Module: The proposed eCAS system is enhanced
with a pedestrian trajectory prediction module that employs an LSTM network,
accounting for the dynamics of urban pedestrian movement. This integration
enhanced proactive safety adjustments in e-scooter navigation within dense
areas.

  * •

E-Scooter Path Planning Module: The dynamic traffic environment for e-Scooter
was modeled using the artificial potential field, which is able to identify
risk levels for all moving trajectories. The gradient decent-based motion
planner was able to generate the collision avoidance path via the energy
surface of the potential.

These contributions, taken together, offer valuable insights and practical
tools for improving the safety and efficiency of e-scooters in urban
environments dense with pedestrian traffic.

This work is organized as follows. In Section 2, we conduct a review of
existing literature, highlighting works on pedestrian trajectory prediction
and vehicle path planning. In Section 3, we present the concept and operation
process of the proposed eCAS with pedestrians on the sidewalks. Section 4
details the implementation and evaluation metrics of the proposed system.
Finally, we draw conclusions in Section 5.

##  3 2\. Related Work

###  3.1 2.1 Predicting Pedestrian Trajectories

Predicting pedestrian trajectories in urban environments has long been a focal
point of research, given its importance in a multitude of applications,
ranging from autonomous vehicles to urban planning. In this section, we survey
key developments in this area, highlighting the evolution of methodologies and
the current state-of-the-art.

####  3.1.1 2.1.1 Traditional Methods and Data-Driven Models

Early approaches to pedestrian trajectory prediction were grounded in physics-
based models. The Social Force Model (SFM) introduced by Helbing et al. [15]
is one such example where pedestrians are modeled as particles and social
forces guide their motion. While these models provide intuitive understandings
of individual and group behaviors, their deterministic nature limits their
capability to capture the intricacies of human movement in dense urban
settings. With the surge of data availability, researchers shifted towards
data-driven approaches. Gaussian Processes (GPs) were employed by Trautman et
al. [16] to predict trajectories by capturing the non-linear relationships
between observed pedestrian movements. These methods showed improvements over
traditional models but often struggled with computational efficiency in real-
time applications.

####  3.1.2 2.1.2 Deep Learning Approaches

The emergence of deep learning techniques has profoundly revolutionized the
field of pedestrian trajectory prediction, primarily due to their ability to
handle large datasets and model intricate patterns in data. The initial wave
of deep learning models in this domain exploited Recurrent Neural Networks
(RNNs) because of their natural ability to handle sequential data. RNNs were
primarily employed to capture temporal dependencies in pedestrian movements,
providing a more dynamic prediction compared to their static, traditional
counterparts. [17] LSTM networks, an advanced variant of RNNs, soon followed,
addressing the vanishing gradient problem typical in conventional RNNs. The
memory cells in LSTMs allowed them to remember patterns over longer sequences,
making them particularly apt for predicting pedestrian trajectories in diverse
settings [18]. As previously mentioned, Zhang et al.’s State Refinement for
LSTM [19] is a notable mention here, extending LSTMs to account for the social
nature of pedestrian interactions.

###  3.2 2.2 Risk Assessment and Motion Planning Techniques

Numerous research and technical approaches have been proposed in the area of
motion planning for AVs or robots under different scenarios to enhance
intelligence and capability in risk assessment and trajectory generation. The
risk assessment module is a prerequisite for motion planning. Risk assessment
computes the danger of potential paths of the ego vehicle based on the
surrounding environment [20]. Since e-Scooter-involved accidents have
increased significantly with little information being available regarding the
behaviors of on-road e-scooter riders, risk assessment and mitigation of
e-scooter crashes is investigated in[13]. Current risk assessment methods are
not designed for multi-vehicle collision scenarios, and the potential risks
from other vehicles and the surrounding environments are modeled separately
[11]. To overcome this issue, some researchers have established the model of
risks of cars using the potential field method [21]. Based on the assessments
from the risk evaluation module, the path planner finds the collision-free
trajectory that the ego vehicle should follow over the next short or long time
period. In [11], a collision mitigation system with features of all-around
(360-degree) collision avoidance capability is proposed for automated vehicles
on the highway driving scenario. A Nonlinear Model Predictive Control
Framework for Whole Body Motion Planning is also explained in [22]. As can be
seen from the literature, most risk assessments and path planners are all
focused on vehicle perspectives, e-Scooter based crash mitigation system has
not been studied and investigated extensively and deeply.

##  4 3\. System Design

###  4.1 3.1 Collision Avoidance System

This section mainly introduces the basic concept and operation process of the
proposed e-Scooter Collision Avoidance System (eCAS) with pedestrians on the
sidewalks. The execution conditions and transition of the riding modes of the
future automated e-Scooter under control are discussed based on its risk
level. The overall architecture for the proposed CAS is also described.

####  4.1.1 3.1.1 Basic Concept of eCAS

As one of the most popular micro-mobility manners, e-scooters are spreading in
hundreds of big cities and college towns in the US and worldwide. Meanwhile,
e-scooters are also posing new challenges to traffic safety [12]. This paper
proposes an active safety e-Scooter collision avoidance system, which can
predict the pedestrians’ trajectories among certain time horizons, evaluate
the potential risks associated with surrounding static and moving obstacles,
and generate the corresponding collision-free trajectory from the start
location to the goal location for e-Scooter. As an operating mode of automated
vehicles, CAS has been developed significantly in both highway and rural
driving environments, which can eliminate and/or minimize human errors. The
development of CAS for e-Scooter under certain riding environments is still an
open topic and research areas based on the best of authors’ knowledge due to
their specific dynamic behaviors as compared to bicyclists and pedestrians.
The proposed eCAS introduced in this paper mainly focuses on specific areas of
the sidewalks/bike lanes with pedestrians, which can continuously analyze the
driving scene, understand the behavior of other road users, evaluate the risk
assessment, and predict its future trajectory. This proactive model is
designed to ensure unobstructed movement in areas with substantial pedestrian
traffic without collisions.

####  4.1.2 3.1.2 Overall System Architecture and Operation Process

The proposed collision mitigation system for e-Scooter receives the predicted
pedestrian moving trajectories from the perception system for all surrounding
objects. The overall architecture and operation process of the eCAS are given
in Figure 1. We assume that the e-Scooter can continuously monitor the
surrounding environment and obtain the required information without any
transmission delays or failures, and the e-Scooter non-linear motion dynamics
are not considered in this paper.

Figure 1 demonstrates the overall system architecture and operation process of
eCAS. As can be seen in the figure, after consuming the predicted pedestrians’
trajectories, the spatial and temporal traffic conditions can be modeled using
artificial potential field representation. Then it sends out both the moving
obstacles and road boundaries along with the pre-determined eScooter’s start
position and goal location to obtain the attractive potential function Uatt
and repulsive potential function Urep across the entire interested riding
environment. By adding Uatt and Urep, the overall energy surface U is obtained
for the further risk assessment module. By taking the negative gradient of the
energy surface, the trajectory generation component will generate the optimal
collision-free route (X, Y) for e-Scooter considering the maximum iteration
number of the gradient descent algorithm, which would push the e-Scooter to
move forward while keeping it away from obstacles.

Figure 1: Overall architecture of e-Scooter Collision Avoidance System.

###  4.2 3.2 Pedestrian Trajectory Prediction Module

In the proposed eCAS, we incorporate a sophisticated Pedestrian Trajectory
Prediction module based on the States Refinement Long Short-Term Memory (SR-
LSTM) framework, as introduced by Zhang et al. [19] as shown in Figure 2 This
module is pivotal for understanding and anticipating pedestrian movements,
thereby augmenting the predictive capability of eCAS in dynamic environments.

Figure 2: SR-LSTM Trajectory Prediction Flowchar.

Input Trajectory Processing: The module commences by acquiring the initial
trajectory data, which comprises historical pedestrian positions. These
positions are crucial for forecasting subsequent movements. The data is first
ingested by a Vanilla LSTM layer, which is fundamental in processing sequence
information and maintaining dependencies over time. The Vanilla LSTM operates
on the input trajectory to produce the preliminary hidden states ($h_{t}^{i}$)
and cell states ($c_{t}^{i}$), which encapsulate the temporal features of the
pedestrian’s movement.

Hidden and Cell States Generation: The hidden state ($h_{t}^{i}$) emerges as
the LSTM’s memory, holding information pertinent to the pedestrian’s prior
trajectory. Concurrently, the cell state ($c_{t}^{i}$) safeguards the long-
term context within the LSTM’s memory. These initial states are the precursors
to the refinement process.

States Refinement (SR) Module: At the core of the SR-LSTM lies the States
Refinement (SR) Module, a novel mechanism that enhances the cell states by
leveraging the trajectories of neighboring pedestrians and supplementary
contextual data. This refinement ensures that the prediction is not solely
dependent on the pedestrian’s path but is also informed by the surrounding
environmental dynamics. Post refinement, the SR Module outputs the refined
cell states ($\hat{c}_{t}^{l+1}$), which encapsulate enriched contextual
information.

Trajectory Prediction: Leveraging the refined cell states, the module predicts
the future coordinates of the pedestrian trajectory. This prediction is rooted
in a more sophisticated understanding of the pedestrian’s context, thanks to
the SR Module’s processing.

Output Trajectory:  The culmination of this process is the output trajectory,
which signifies the eCAS’s foresight into the pedestrian’s future locations.
The refined prediction takes into account not only the pedestrian’s past and
present positions but also the influence of nearby dynamics.

The SR-LSTM’s innovative design, especially the inclusion of the SR module,
distinguishes it from conventional LSTM models. By considering a broader range
of contextual information, the SR-LSTM significantly elevates the accuracy of
trajectory predictions within complex and interactive urban environments, thus
enhancing the decision-making capabilities of the eCAS.

###  4.3 3.3 Overview of Gradient-based Motion Planner for E-Scooter

Gradient-based motion planners are widely utilized for robot and automated
vehicle local planning based on the Euclidean Signed Distance Field (ESDF),
which is significant for modeling the driving environments and evaluating the
risks through gradient magnitude and direction. Then a local optimal
trajectory can be generated using numerical optimizations on a pre-built ESDF
energy surface. As one of the ESDFs, the motion planners relying on artificial
potential fields (APF) have been extensively applied in different user cases
and proved to be effective for generating collision-free paths in complex
environments for practical purposes. Accompanying the convenience of the
trips, there have been numerous injuries and fatalities reported in
association with micro-mobility options like e-Scooters, according to the
estimations from the Centers for Disease Control and Prevention (CDC)[7]. For
better handling of the potential collision risks between e-Scooters and
pedestrians, this section mainly introduces the overall concept of APF and how
to use APF to generate the energy surface for traffic environments. Two types
of collision risks are considered in this paper. One is the predicted static
and moving pedestrians around the e-Scooter, and the other one is the
surrounding driving environment with drivable areas.

####  4.3.1 3.3.1 Artificial Potential Field

The artificial potential field is a motion planning technique for guiding a
moving host vehicle from the start position to the goal position in
2-dimensional configuration space by building the artificial potential fields
that obey Laplace’s equation, such as gravitational, magnetic, and electrical
fields and so on [23]. The APF algorithm is used to guide the robot to be
attracted to the goal and repelled from the obstacles. We can think of the
robot as a positively charged object moving toward the negatively charged
destination and moving away from the obstacles with the same charge as the
robot. Thus, two types of artificial potential fields can be generated in the
motion planner, one is the attractive field and the other one is the repulsive
field. The path planning algorithm utilizes the APF to regulate the e-Scooter
in specific scenarios with pedestrians like university or hotel sidewalks. For
our case, a 2-D space to be divided into a grid of cells with obstacles and a
goal position will be considered.

####  4.3.2 3.3.2 Energy Surface Generation for Traffic Environments

With the generation of both attractive fields and repulsive fields based on
the goal and obstacles. The potential function of the attractive field and
repulsive field can be viewed as the energy surface and the gradient of energy
is force. Thus, the energy surface can be modeled to simulate the real driving
environment the e-Scooter can be vised as a sphere rolling down the energy
surface towards the goal location in a gradient field. As can be seen in
Figure 3, the attractive potential function is the distance from the goal to
the start location of e-Scooter with the highest energy away from the start
and zero at the goal location. Therefore, based on the energy surface, a
gradient-based path planner can be designed using the negative gradient, which
is the largest change in energy.

Figure 3: Illustration of the energy surface.

In terms of the function of attraction force, the following formula of
distance measure can be utilized to generate the force by the goal location.
Thus, $Pos_{x}$ and $Pos_{y}$ are the coordinates of the e-Scooter start
point, $Pos_{g}x$ and $Pos_{g}y$ are the coordinates of the goal node and $h$
is a constant to adjust the strength of the force.

$F_{att}=h*\sqrt{\left|Pos_{x}-Pos_{gx}\right|^{2}-\left|Pos_{y}-Pos_{gy}\right|^{2}}$
(1)

Regarding the function of repulsive force, its strength will be increased when
the e-Scooter is closer to the obstacles. As described in the previous
sections, the drivable areas with boundaries and obstacles are included. The
repulsive forces by the road boundaries will remain the same throughout the
motion planner due to its uniform geometry, which does not affect the
computations afterward. The force generated from boundaries is utilized to
keep the e-Scooter riding along the drivable areas.

$F_{rep}=\frac{1}{\alpha+\sum_{i}^{s}(g_{i}+\left|g_{i}\right|)}$ (2)

Where $g_{i}$ is the representation of boundary with a linear function of
convex region. $\alpha$ is a small constant value and $s$ is the number of
boundary segments.

In the repulsive forces from all obstacles, the following formula can be used
to compute the forces.

$U_{rep}(q)=\begin{aligned}
\left\\{\begin{array}[]{lr}\frac{1}{2\delta}(\frac{1}{D(q)}-\frac{1}{Q^{*}})^{2},D(q)\leq
Q^{*}\\\ 0,D(q)>Q^{*}\end{array}\right.\end{aligned}$ (3)

Where $\sigma/2$ is the maximum potential, $1/(D(q))$ is the potential of
Euclidean distance to the closest obstacle, $Q^{*}$ is the potential of
Euclidean distance around the obstacle. Hence, the resultant potential
function can be represented by adding the attractive potential and repulsive
potential together below:

$U(q)=U_{att}(q)+U_{rep}(q)$ (4)

###  4.4 3.4 Motion Planning and Trajectory Generation

After getting the overall energy surface across the environment including both
road boundaries and the predicted pedestrian trajectories, this section will
mainly elaborate on how to conduct the risk assessment and generate the safest
path through the APF.

Figure 4: Risk Assessment of e-Scooter Riding Environment with the repulsive
potential surface, attractive potential surface and total potential surface.

####  4.4.1 3.4.1 Risk Assessment

In formula (4), the total energy surface has been obtained using a 2D array
containing the potential function values across the environment with predicted
pedestrians’ trajectories. The start coordinates are also defined as an array
specifying the location of the first entry of the x coordinate and y
coordinate, as well as the goal coordinate. The maximum number of iterations
of the planner that need to be searched is also defined for this specific
scenario with e-Scooter and pedestrians.

The risk assessment module is mainly operated through the resultant potential
function with attractive potential and repulsive potential, where attractive
potential represents the goal position with lower potential values that the
e-Scooter would like to reach without collisions after a certain time
duration, and the repulsive potential demonstrates and visualize the locations
of surrounding obstacles and boundaries with higher potential values.

As can be seen in Figure 4, the potential function according to the e-Scooter
riding environment with a red start point and green goal point has been
modeled and illustrated from the outputs of the pedestrian trajectory
prediction module. The goal destination leads to a lower energy barrier with a
smaller potential magnitude in the darker blue color, but the obstacles of
pedestrians and road boundaries create a higher energy barrier with a larger
potential magnitude in the darker yellow color across the whole riding
environment.

####  4.4.2 3.4.2 Trajectory Generation

The safest collision-free trajectory is generated by first computing the
gradient of the potential function over the whole e-Scooter riding
environment, and then taking the negative gradient which is the largest change
in energy surface. Generally, it is known as the gradient descent algorithm
which follows a functional surface until you reach its minimum. The negative
gradient of the total potential function can be calculated below:

$F(q)=-\nabla U(q)$ (5)

The detailed procedures for generating the desired trajectory are illustrated
below:
1\. Determine the start position and goal location of the e-Scooter before
motion planning.
2\. Receive the predicted pedestrians’ trajectories during a certain time
horizon.
3\. Generate the resultant potential function for the overall riding
environment as well as compute its negative gradient.
4\. On every iteration the planner should update the position of the re-
Scooter based on the gradient values contained in the arrays $g_{x}$ and
$g_{y}$ with the normalized gradient vectors.
5\. Update the route by adding the new position of the e-Scooter to the end of
the route array. Note that the distance between successive locations in the
route should not be greater than 1.0.
6\. In some corner cases when the planner gets stuck in a local minimum, the
motion planner will let the e-Scooter take a random movement by perturbing out
of the minima to explore the possible route to reach the goal location.
7\. Continue the same procedure until the distance between the robot’s current
position and the goal is less than 2.0 or the number of iterations exceeds its
maximum value.
8\. Output the optimal collision-free trajectory with an array with 2 columns
(X, Y) that depicts how the e-Scooter position evolves over the state on the
gradient of the potential function.

##  5 4\. Experiment

###  5.1 4.1 Pedestrian trajectory prediction

In the evaluation of our trajectory prediction module, we incorporated the ETH
[24] and UCY [25] datasets, which are comprehensive compilations of pedestrian
paths, containing intricate social interactions amongst 1536 individuals. The
datasets’ extensive collection of non-linear walking trajectories forms the
basis for our training regime. To quantify the performance of our model, we
used two primary metrics [24]: the Mean Average Displacement (MAD) error,
which is the mean Euclidean distance between the predicted and the actual
ground truth positions over all the timesteps, and the Final Average
Displacement (FAD) error, which measures the Euclidean distance at the final
predicted point, indicating the model’s accuracy in determining the
pedestrian’s endpoint. Following the implementation guidelines detailed by
Zhang et al [19]., our model adhered to the prescribed hyperparameters and
underwent a rigorous training process of 1000 epochs, processing batches of 64
pedestrians. A specific scenario from the UCY University dataset, chosen for
its complexity and the richness of pedestrian interactions, was not only
critical for the evaluation of our trajectory prediction module but also
served as a basis for subsequent eCAS performance assessments. The results
were promising, with the trajectory prediction module achieving a MAD of 0.71
meters and a FAD of 1.46 meters, showcasing a high level of accuracy in
forecasting pedestrian movements.

###  5.2 4.2 eCAS performance evaluation

To evaluate and verify the proposed e-Scooter collision avoidance system, some
simulation results on two representative scenarios of “parallel moving” and
“crossing moving” are illustrated in the following figures.

Figure 5: e-Scooter riding environment with parallel moving pedestrians and
planned trajectory. Figure 6: e-Scooter riding environment with crossing
moving pedestrians and planned trajectory.

###  5.3 Scenario 1 - Parallel Moving

As shown in Figure 5 in the “parallel moving” scenario, the e-Scooter will
start from the red point and end at a green point with a total of six
pedestrians with different velocities and varying moving directions as well as
the road boundaries, which has the general parallel motion pattern as compared
to the e-Scooter riding direction. To be specific, the prediction model first
estimated all six pedestrians’ trajectories for around 4 secs with pedestrian
1 average running speed of 2.79 m/s, pedestrian 2 average walking speed of
0.52 m/s, pedestrian 3 average walking speed of 0.71 m/s, pedestrian 4 average
moving speed of 1.97 m/s, pedestrian 5 average walking speed of 1.67 m/s, and
pedestrian 6 average moving speed of 1.55 m/s. Then the collision risks around
the e-Scooter were evaluated using the energy surface with artificial
potential fields. After a gradient decent-based path is planned in eCAS, the
e-Scooter’s collision-free trajectory in a global coordinate is generated from
the red point to the green point (shown in Figure 5).

###  5.4 Scenario 2 - Crossing Moving

In the “crossing moving” scenario from Figure 6, the e-Scooter also starts
from the red point and ends at a green point but with a total of ten
pedestrians with different velocities and varying moving directions as well as
the road boundaries. All pedestrians have the general crossing motion pattern
as compared to the e-Scooter riding direction, which leads to a more
challenging task for e-Scooter to avoid crashes. To be specific, the
prediction model first estimated all ten pedestrians’ trajectories for around
4 secs with pedestrian 1 average moving speed of 0.72 m/s, pedestrian 2
average walking speed of 0.73 m/s, pedestrian 3 average walking speed of 0.45
m/s, pedestrian 4 average moving speed of 0.90 m/s, pedestrian 5 average
walking speed of 0.17 m/s, pedestrian 6 average moving speed of 0.27 m/s,
pedestrian 7 average walking speed of 1.35m/s, pedestrian 8 average moving
speed of 0.19m/s, pedestrian 8 average walking speed of 0.46m/s, and
pedestrian 10 average moving speed of 0.54m/s. The risk assessment around the
e-Scooter was conducted using the energy surface with artificial potential
fields. Since a more complex scenario is encountered, the eCAS first locates
the collision avoidance path from the start point to the indirect point in
yellow in Figure 6, which indicates the planner gets stuck in a local minimum.
Then in re-planning, the eCAS guides the e-Scooter to take a random movement
by perturbing out of the minima to find the possible optimal route to reach
the goal location from the indirect point to the goal location. By combining
two planned paths, e-Scooter’s collision-free trajectory in a global
coordinate is generated from the red point to the green point (shown in Figure
6).

##  6 5\. Conclusion

This paper proposed a novel e-Scooter collision avoidance system using SR-
LSTM-based pedestrian trajectory prediction model and motion planner with the
gradient decent algorithm based on the artificial potential field and energy
surface. It has been shown that the developed pedestrian trajectory prediction
module is able to achieve low Mean Average Displacement (MAD) and Final
Average Displacement (FAD) errors, indicating good accuracy in predicting
pedestrian paths. From the devised motion planner in eCAS, the scooter-riding
environments with both road boundaries and moving pedestrians were modeled and
represented using the spatial and temporal space of the potential field with
the visualization of risks on an energy surface. The collision-free trajectory
with the lowest risk was also generated in the global sense from the start
point to the goal location according to the specific test scenarios. Two
representative scenarios of ”parallel moving” and ”crossing moving” were used
to verify that the developed eCAS can execute successfully to mitigate the
potential crashes between electronic scooters and pedestrians. The simulation
results illustrated the effectiveness and capability of the proposed system.

Moving forward, there are several avenues to enhance the effectiveness of
e-scooter collision avoidance systems. One area is Sensor Fusion, where
integrating a variety of sensors, such as radar and cameras, can improve the
model’s accuracy and robustness. Another direction is adapting the system for
Multimodal Transportation Environments, which requires testing and refining
the system’s capabilities to ensure smooth interactions between e-scooters,
bicycles, cars, and public transportation. Additionally, the system could
evolve to consider the impact of the e-scooter’s own trajectory on pedestrian
behavior. A new model could anticipate how pedestrians might react to the
e-scooter’s path and adjust in real-time.

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