# Development and Simulation-based Testing of a 5G-Connected Intersection AEB
System

Michael Khayyat, Stefano Arrigoni, Federico Cheli CONTACT Michael Khayyat.
Email<EMAIL_ADDRESS>Stefano Arrigoni. Email:
<EMAIL_ADDRESS>Federico Cheli. Email:
<EMAIL_ADDRESS>Politecnico di Milano Department of Mechanical
Engineering Via La Masa 1, 20156 Milano MI, Italy

###### Abstract

In Europe, 20% of road crashes occur at intersections. In recent years,
evolving communication technologies are making V2V and V2I faster and more
reliable; with such advancements, these crashes, as well as their economic
cost, can be partially reduced. In this work, we concentrate on straight path
intersection collisions. Connectivity-based algorithms relying on 5G
technology and smart sensors are presented and compared to a commercial radar
AEB logic in order to evaluate performances and effectiveness in collision
avoidance or mitigation. The aforementioned novel safety systems are tested in
a blind intersection and low adherence scenario. The first algorithm proposed
is obtained by incorporating connectivity information to the original control
scheme, while the second algorithm proposed is a novel control logic fully
capable of utilizing also adherence estimation provided by smart sensors. Test
results show an improvement in terms of safety for both the architecture and
high prospects for future developments.

###### keywords:

Intersection AEB; 5G-Connected Vehicles (V2V); Advanced Driver-Assistance
Systems; Collision Avoidance; Collision Mitigation; Adherence Estimation

##  1 Introduction

According to the World Health Organization (WHO), road traffic injuries are
the 8th leading cause of death in the world, and the leading cause of death
for children and young adults aged 5-29 years [1]. The main cause of death
from road traffic accidents is the inappropriate behavior of drivers, such as:
speeding, driving under the influence of alcohol or drugs, distracted driving,
as well as non-use of seat-belts or safety equipment. Also, some accidents are
due to bad road infrastructure design (such as blind zones) that does not
provide the driver with enough information to make the correct safe decision
at the right time. Although human safety is the primary concern, it is worth
mentioning that fatal road crashes affect the economy as well, costing up to
$\$280$ billion in the United States [2] with a similar number in Europe [3].

With 20% of all road traffic fatalities in the European Union and United
States occurring at intersections, their safety is becoming a concern [3].
What is intriguing, and yet disappointing, is the fact that nearly all
accidents occur at signal-controlled (52%) or stop-controlled (46%)
intersections [4]. It is agreed that Connected Advanced Driver Assistance
Systems (C-ADAS) are needed to help drivers navigate safely through
intersections. In fact, a study done by Scanlon et al. predicted that
Intersection ADAS that delivers a warning only (no Autonomous Emergency
Braking (AEB)) could prevent up to 23% of intersection crashes, while one that
brakes as well as warns could prevent up to 79% of crashes [4]. It could be
concluded that improving intersection safety does not only save lives, but
also reflects well on the economy.

##  2 Literature Review

Vehicle-to-vehicle (V2V) and Vehicle-to-infrastructure (V2I) technologies are
bringing road safety to a new level. These technologies are expanding the
scope of “preventable collisions” to include blind-zone intersections. The
upcoming 5G technology is rendering current AEB systems outdated; this is
because state-of-the-art AEB is unable to utilize the full potential of the
quick and rich information that 5G is able to transmit as well as that
provided by smart sensors (i.e Pirelli Cyber Tyre).

Forward Collision AEB is explored very well in literature. This is not the
case for Intersection AEB (I-AEB), as it is a relatively new concept. Euro
NCAP prioritizes I-AEB development and plans on incorporating it in most
vehicles by 2020 [5]. It is noteworthy that Sander et al. explore various
clustering methods to define a small number of representative intersection
test scenarios, in order to validate any AEB logic developed as well as reduce
the number of intersection scenarios tested due to their diverse nature [6].
Although no set of clusters that could group accidents into homogeneous groups
was found, this paper was very insightful; through the results, it becomes
clear that using simulation software is an inevitable part of testing and
development, as physical testing will be very costly, in terms of money and
time.

###  2.1 Autonomous Emergency Braking Algorithms

The main characteristic of current AEB algorithms is having a predefined
static braking logic which is triggered or activated by a certain collision
risk index: a quantitative measure about driving safety. While this reduces
the complexity of the system, it is not suitable for all scenarios.

####  2.1.1 Collision Risk Index – Time-to-collision

At first, Time-to-collision (TTC) is considered as it is the most widely used
indicator. Usually, TTC is calculated using information given by the radar
system using a constant velocity or a constant acceleration vehicle model, and
after that, if the TTC is less than a certain threshold a warning is issued
and if the driver doesn’t respond, autonomous braking takes place based on a
certain braking logic. Jeon et al. develop a method to take into consideration
the road condition in the AEB logic. As mentioned earlier, the AEB was
activated if the calculated TTC is less than a certain pre-defined threshold
(usually 2 seconds). However, to consider various road conditions, the
threshold was modified such that it increases as the ground friction decreases
(the ground friction dictates the maximum deceleration that the vehicle could
achieve), as seen in Equation 1:

$\text{TTC}_{\text{threshold}}=\frac{V_{\text{ego}}}{2\mu g}$ (1)

where $V_{\text{ego}}$ is longitudinal ego-vehicle velocity; ${\mu}$ is ground
friction (imposed as a fixed parameter) and $g$ is acceleration due to
gravity. The latter AEB logic was experimentally tested, and showed promising
results. However, the main disadvantage of such technique is that full braking
is employed once the TTC threshold is crossed, which is not very comfortable
for the driver and not always safe [17].

####  2.1.2 Collision Risk Index – Time-to-react

Time-to-react or TtR is defined as the remaining time until the very last
possible driving maneuver that could avoid an imminent collision [19]. For
example, if the possible driving maneuvers considered are: Time-to-brake (TTB)
and Time-to-steer (TTS), then $\text{TtR}=\max(\text{TTB,TTS})$.

####  2.1.3 Collision Risk Index – Braking Distance

An interesting work incorporating this index was developed by Lee et al.. The
braking distance calculated takes into consideration the effects of the
actuator dynamics of the braking system leading to a better prediction [20],
as in Equation 2

$d_{\text{brake}}=\frac{v_{x}^{2}}{2a_{x}}+\frac{v_{x}}{2J_{\text{act}}}a_{x}-\frac{1}{24J_{\text{act}}^{2}}a_{x}^{3}$
(2)

Where:

$v_{x}$ ${}={}$ | current vehicle speed
---|---
$a_{x}$ ${}={}$ | desired deceleration
$J_{\text{act}}$ ${}={}$ | actuator response time

It is also interesting to note that Seiler et al. highlight other types of AEB
logics which use braking distance as the safety indicator [21]. Mazda’s
algorithm [22] (developed for Forward AEB) defines the braking distance as
obtained in Equation 3:

$d_{br}=\frac{1}{2}\bigg{(}\frac{v^{2}}{\alpha_{1}}-\frac{(v-v_{\text{rel}})^{2}}{\alpha_{2}}\bigg{)}+v\tau_{1}+v_{\text{rel}}\tau_{2}+d_{0}$
(3)

Where:

$v$ ${}={}$ | ego vehicle velocity
---|---
$v_{\text{rel}}$ ${}={}$ | $v-v_{\text{preceding}}$
$\alpha_{1}$ ${}={}$ | maximum deceleration of the ego vehicle
$\alpha_{2}$ ${}={}$ | maximum deceleration of the preceding vehicle
$\tau_{1},\ \tau_{2}$ ${}={}$ | delay time
$d_{0}$ ${}={}$ | headway offset

The Mazda system issues a warning when the range is less than braking distance
plus a parameter ($d_{br}+\epsilon$) where $\epsilon$ is the system parameter.
The brakes are applied when the range is less than braking distance
($d_{br}$). This systems attempts to avoid all collisions, even the extreme
cases [21]; hence, warnings are issued frequently. It would be interesting to
note that in such systems, drivers were seen to become desensitized to
warnings as such conservative braking distance definition intervened with
normal driving scenarios. Honda’s algorithm is also similar to Mazda’s
algorithm and is explored in [23].

It is also interesting to mention the work of Malinverno et al. where they
assess the performance of a V2I collision avoidance system [24]. The proposed
system relies on two parameters for judgment: Time-to-collision and braking
distance. It is argued that these two parameters have a huge impact on the
performance of the system. For example, relaxing them will lead to a system
that triggers many alerts. On the other hand, being strict will lead to a
system where collisions are not detected or may be detected late for the AEB
to be effective. It is also noteworthy that their work is applicable for both
human driven vehicles as well as autonomous ones. Testing was carried out in a
virtual simulation environment to test the reliability of the algorithm; these
tests showed promising results.

It is noteworthy to note that in literature, most work done on Connected
Intersection AEB systems is based on a 4G network. The characteristics of such
network reduces the scope of the applicability of the work as the mean
communication delay of such systems is noted in literature to be in the order
of hundrends of milliseconds [25] and [26], which is much higher than
milliseconds required [27].

####  2.1.4 Braking Logics

In literature, there are many approaches to the implementation of the braking
logic. In some cases, the braking logic depends on both the velocity and TTC,
as an example [15] is illustrated in Figure 1. The thresholds reported are
calculated as follows:

$t_{LPB}$ ${}={}$ |  $\displaystyle-\frac{v_{rel}}{2\mu g}$ (Last Point to Brake)
---|---
$t_{LPS}$ ${}={}$ |  $\displaystyle\sqrt{\frac{2s_{y}}{\mu g}}$ (Last Point to Steer)

Velocity Region Collision Level Brake Profile LOW Avoidance
$\text{TTC}<t_{LPB}$ HIGH Mitigation $t_{LPS}<\text{TTC}<t_{LPB}$
$\text{TTC}<t_{LPS}$ Full Brake Pre-Brake Full Brake

Figure 1: Alternative Braking Logic [15]

where $s_{y}$ represents relative distance between the vehicles and two
velocity regions are considered. On the other hand, the AEB braking logic
developed in [16] is TTC dependent:
$\bullet$ if $1.6\text{s}<\text{TTC}\leq 2.0\text{s}$
$\rightarrow\text{dec}_{\text{req}}=-3\text{m/s${}^{2}$}$ $\bullet$ if
$0.7\text{s}<\text{TTC}\leq 1.6\text{s}$
$\rightarrow\text{dec}_{\text{req}}=-6\text{m/s${}^{2}$}$ $\bullet$ if
$\text{TTC}\leq 0.7\text{s}$ $\rightarrow$
$\text{dec}_{\text{req}}=-10\text{m/s${}^{2}$}$
where $\text{dec}_{\text{req}}$ represents deceleration requested. In their
work, Kapse et al. present a detailed Simulink implementation of an AEB
algorithm. The used AEB logic implements cascaded braking, which consists of
two partial braking stages and one full braking stage. It is interesting to
note that the AEB logic (transition between stages) does not depend on pre-
defined conditions, but rather on dynamic online-calculated thresholds [18].
These thresholds are velocity dependent and defined based on braking times, as
follows:

$t_{\text{threshold,a}}=\frac{V_{\text{ego}}}{\text{dec}_{\text{a}}},\ \
t_{\text{threshold,b}}=\frac{V_{\text{ego}}}{\text{dec}_{\text{b}}},\ \
t_{\text{threshold,max}}=\frac{V_{\text{ego}}}{\text{dec}_{\text{max}}}$

where $\text{dec}_{a}<\text{dec}_{b}<\text{dec}_{\text{max}}$ and are three
predefined deceleration values. These parameters are combined to obtain the
three stages braking maneuver:
$\bullet$ $\text{TTC}<t_{\text{threshold,a}}$
$\rightarrow\text{dec}_{\text{req}}=\text{dec}_{\text{a}}$ $\bullet$
$t_{\text{threshold,a}}\leq\text{TTC}<t_{\text{threshold,b}}$
$\rightarrow\text{dec}_{\text{req}}=\text{dec}_{\text{b}}$ $\bullet$
$t_{\text{threshold,b}}\leq\text{TTC}<t_{\text{threshold,max}}$
$\rightarrow\text{dec}_{\text{req}}=\text{dec}_{\text{max}}$

##  3 Description of the Setup

###  3.1 Current System Architecture

The architecture of the current commercial system considered in this work is
shown in Figure 2.

Ego VehicleAEB FunctionPerception Radar Sensors Commercial System Braking
SystemModule Radar Deceleration Request In-car DisplayRadar Warning
RequestVehicle Dynamic Response

Figure 2: Commercial System Architecture

The “Radar Sensors” block is responsible for perception, computation of TTC,
and finally issuing the deceleration request to the braking system module,
which in turn regulates the brakes pressure to reach the desired deceleration.

At first, information coming from the Radar Sensors about surrounding vehicles
is evaluated to determine if the TTC should be calculated or not. The
activation criteria for the AEB feature is the following:

  * •

Target vehicles with orthogonal path: $45$°$<|\arctan\frac{v_{y}}{v_{x}}|<$
$135$°

  * •

Target vehicle with $|v_{y}|>2\text{ m/s}$

  * •

Ego vehicle with $5\text{ km/h}\leq v_{ego}\leq 60\text{ km/h}$

where $v_{y}$,$v_{x}$ are respectively the target vehicle longitudinal and
lateral velocities. It is important to note that the AEB is intended to
operate in urban areas. The choice of limiting the orthogonal path of the
target vehicle is explained by the fact that the operation of the system is
limited to intersection application. For example, the AEB system should not be
activated when doing overtakes on a road.

The sensing architecture is composed only of 3 radar sensors, as reported in
Figure 5 as well as a measurement of the ego vehicle velocity as provided by
CAN. It is noteworthy that the side radars (shown in red) are short range ones
(SRR) and the center one (shown in blue) is a long range radar (LRR). The
Radar Deceleration Request is issued once the calculation of TTC calculated by
the “Perception” Block is $<1.5$ s. In this work, the proposed commercial AEB
logic provided by the “AEB Function” block in Figure 2 is velocity-dependant.

  * •

if $v\leq v_{\text{b}}\rightarrow$ $\text{dec}_{\text{requested}}=\text{Level
C}\text{ m/s${}^{2}$}$

  * •

if $v_{\text{b}}<v\leq v_{\text{a}}\rightarrow$
$\text{dec}_{\text{requested}}=\text{Level A}\text{ m/s${}^{2}$}$ for $300$
ms, followed by $\text{dec}_{\text{requested}}=\text{Level B}\text{
m/s${}^{2}$}$

  * •

if $v_{\text{a}}<v\leq v_{\text{max}}\rightarrow$
$\text{dec}_{\text{requested}}=\text{Level A }\text{m/s${}^{2}$}$

It is important to note that value of the deceleration levels could not be
provided at the request of our research partners. For this purpose, a scheme
is provided in Figure 3 as an additional support to fully understand the
logic.

$0$$v_{\text{max}}$$v_{\text{a}}$$v_{\text{b}}$$|\text{deceleration}|$Level
CLevel BLevel ALevel AStage BStage AStage Ct ms

Figure 3: Scheme of Commercial AEB Logic

###  3.2 Connected System Architecture

The architecture considered for the development of the novel connected AEB
algorithms is shown in Figure 4.

Ego VehicleAEB FunctionPerception Radar Sensors Commercial System Braking
SystemModuleRadar Deceleration RequestIn-car DisplayRadar Warning
RequestVehicle Dynamic Response  5G Virtual Sensor Perception5G AEB
FunctionAEBMerge MABX ECUCyber Tyre

Figure 4: Connected System Architecture

It is important to note that the operation of the radar safety system remains
the same (i.e activation criteria and braking logic) as that described in the
previous section. It could be seen in Figure 5 that the additional sensors
mounted on the vehicle of the connected system are the following:

1$x$$y$23GPS Sensor5G Receiver Pirelli Cyber Tyre

Figure 5: Complete sensor configuration for connected system architecture

  * •

GPS Sensor with RTK Correction (ZED-F9P) operates at 10 Hz for a global
accurate localization (error $<0.5$ m).

  * •

5G Virtual Sensor receives information by other connected vehicles about their
speed and location, and by the infrastructure about potential ground friction
(a digital map containing potential ground friction estimated by the Cyber
Tyre sensors of other vehicle who traveled on the road earlier).

  * •

Pirelli Cyber Tyre is able to measure the potential ground friction after
braking takes place. It is noteworthy that through the use of Kalman Filters
(for slip estimation), Dynamic Estimators (for force estimation) and Pre-
defined Reference Curves, the Friction-Identification Algorithm is able to
provide the potential ground friction with an accuracy dependent on that of
the measurements provided by the accelerometers.

###  3.3 Testing Scenario

The scenario under consideration in this work is described in Figure 6.

Figure 6: Scenario under consideration in the development of the AEB systems
and their testing

It is important to note that the simplified setup was chosen for two main
reasons:

  * •

it is one of the most common intersection crashes schemes;

  * •

it allows the work to concentrate on the power of connectivity and its
potential influence on safety systems.

The scenario considered is a blind intersection where the initial velocity of
the Target Vehicle is set to ensure that it crashes with the Ego Vehicle just
before any braking takes place. It is also important to note that it is
assumed that the Ego Vehicle, unlike the Target Vehicle, can be actuated.

##  4 IPG CarMaker/Simulink Implementation

IPG CarMaker is a tool for virtual testing of vehicles, which allows to
recreate realistic test scenarios in a virtual environment. IPG CarMaker
provides a Virtual Vehicle Environment (VVE), which consists of the virtual
road, virtual driver, and virtual vehicle. Furthermore, IPG CarMaker has a
user-friendly MATLAB/Simulink interface that facilitates modeling the
connected system architecture, as seen in Figure 4.

###  4.1 Scheme of Connected Commercial System

The summary of the developed Simulink model for the two control logics is
presented in Figure 7.

Connected System ModelCurrent System Model Radar Sensors IMU 5G Module
Activation Criteria Activation Criteria TTC Calculation TTC/BD Calculation
Radar AEB Connected AEB AEB Merge Acceleration Controller Pirelli Cyber Tyre

Figure 7: Summary of Developed System

####  4.1.1 Current System Model

As an initial step, the current system architecture (as modeled in Figure 2,
and illustrated in 7) was modeled in IPG CarMaker.

For the perception block, information from the radars of the ego vehicle about
surrounding visible vehicles was analyzed to determine whether to activate the
TTC Calculation block or not (according to the activation criteria defined
earlier).

For the purpose of this work, [14] and [28] are used to build the TTC
calculation block in the Simulink model.

$y_{G}$$x_{G}$$\theta_{target}$$\theta_{ego}$$(x_{target},y_{target})$$(x_{ego},y_{ego})$$(x_{+},y_{+})$$v_{ego}$$v_{target}$

Figure 8: TTC in X-Y Plane

Figure 8 illustrates the setup for the calculation of the TTC, based on an a
global X-Y reference frame. The equations can be easily adjusted to take into
consideration that the radar gives information relative to the ego vehicle
reference frame. The point at which the intersection can be given by the
following expressions, given by Equations 4 and 5:

$\displaystyle x_{+}$
$\displaystyle=\dfrac{(y_{ego}-y_{target})-(x_{ego}\cdotp\tan\theta_{ego}-x_{target}\cdotp\tan\theta_{target})}{\tan\theta_{target}-\tan\theta_{ego}}$
(4) $\displaystyle y_{+}$
$\displaystyle=\dfrac{(x_{ego}-x_{target})-(y_{ego}\cdotp\cot\theta_{ego}-y_{target}\cdotp\cot\theta_{target})}{\cot\theta_{target}-\cot\theta_{ego}}$
(5)

After finding the intersection point, the Time-to-Reach (TTR) values for each
vehicle can be obtained by Equations 6 and 7:

$\displaystyle\text{TTR}_{target}$
$\displaystyle=\dfrac{\sqrt{(x_{+}-x_{target})^{2}+(y_{+}-y_{target})^{2}}}{v_{target}}$
(6) $\displaystyle\text{TTR}_{ego}$
$\displaystyle=\dfrac{\sqrt{(x_{+}-x_{ego})^{2}+(y_{+}-y_{ego})^{2}}}{v_{ego}}$
(7)

To consider a more realistic case, it cannot be said that the vehicles will
crash only when $\text{TTR}_{target}=\text{TTR}_{ego}$. Hence, it could be
said that $\delta$ is a safety parameter described by Equation 8, such that:

$\text{TTC}=\begin{cases}\min(\text{TTR}_{target},\text{TTR}_{ego})&\text{if}\
|\text{TTR}_{target}-\text{TTR}_{ego}|\leq\delta\\\
\text{NaN}&\text{else}\end{cases}$ (8)

To achieve similar performance between the simulated system and the reference
real one provided by our partners, an expression of $\delta$ as a function of
speed was reached by tuning the system to achieve the results described in
Figure 9b and Figure 10b. In the latter figures, the green square means that
the collision was avoided using the radar AEB logic, and the red square means
that a collision occurred. The tuning of $\delta$ was done taking into
consideration speeds of vehicles that yield the following collision
configurations during the braking process, as seen in Figure 11.

It is important to note that the AEB logic is a temporal one. Hence, it
required a FSM finite state machine implemented by means of Stateflow on
MATLAB.

Ego VehicleTarget Vehicle

(a) Open Field Scenario

Target Vehicle VelocityEgo Vehicle
Velocity5101520253035404550556051015202530354045505560

(b) Open Field Results

Figure 9: Description of Open Field Case

OBSTRUCTIONEgo VehicleTarget Vehicle

(a) Obstructed Field Scenario

Target Vehicle VelocityEgo Vehicle
Velocity5101520253035404550556051015202530354045505560

(b) Obstructed Field Results

Figure 10: Description of Obstructed Field Case

Figure 11: Different Collision Configurations

The acceleration controller block is PI controller that provides as an output
a value of the required brake pedal (between $0$ and $1$) to achieve a certain
deceleration value. It is important to note that the acceleration controller
module is activated only when the AEB is taking place. The development of this
ACC is based on work presented in [29]. It is important to note that for our
work, it is required that the driver can no longer press the gas when the AEB
takes into action.

Desired Acceleration Current Acceleration $+$$-$$\Delta
a_{x}$$p_{\text{gain}}$$i_{\text{gain}}$$\Sigma$ $u(k)$ Brake Value
MemorySaturationPI Controller

Figure 12: Acceleration Control Architecture

Figure 12 shows the longitudinal acceleration controller. The architecture of
the controller can be described as: Discrete PI Controller with Integral Anti-
Windup. The control signal is calculated using the backward Euler
discretization method [31] (where the gains were tuned), as given by Equations
9 and 10:

$\displaystyle u(k)=\text{sat}(p_{\text{gain}}\Delta
a_{x}+\text{sat}(i_{\text{gain}}\frac{T_{s}z}{z-1}\Delta a_{x},A,B),A,B)$ (9)
$\displaystyle\text{sat}(x,A,B)=\min(\max(x,A),B)$ (10)

Where:

$p_{\text{gain}}$ ${}={}$ | 0.001
---|---
$i_{\text{gain}}$ ${}={}$ | 0.001
$Ts$ ${}={}$ | 0.001 s
$A$ ${}={}$ | Lower Limit of Saturation (1)
$B$ ${}={}$ | Upper Limit of Saturation (0)

####  4.1.2 Connected System Model: Type A

The connected system architecture shown in Figure 4 was reached by making
modifications to the previously developed Simulink model to take into
consideration information from GPS, 5G, and Pirelli Cyber Tyre. It is
important to note that the activation criteria remains the same for both the
radar and 5G systems, as it is a characteristic of the type of collision the
system is aimed at avoiding.

Concerning 5G communications characteristics, literature suggests that the
maximum value of delay accepted for this kind of applications is $10$ ms. This
guarantees the minimum safety requirements [27]. The exact characteristics of
the 5G network provided by Vodafone were used in the development; however,
they could not be mentioned here for confidentiality purposes.

Concerning the GPS information, CarMaker allows the modeling of the following
errors [29, 30]:

  * •

Receiver Clock Error: Arises because the receiver clock is not always
synchronized with that of satellites. Standard deviation $=5$ m with
Correlation time $=3600$ ms.

  * •

Ephemeris Error or Orbit Error: Arises because of the small variations of the
orbits of travel of the GPS satellites. Standard deviation $=3$ m with
Correlation time $=1800$ ms.

  * •

Ionospheric Delay: Arises because of ions in the ionosphere. Standard
deviation $=5$ m with Correlation time $=3600$ ms.

  * •

Tropospheric Delay: Arises because of the refractions of the signal due to the
variations of atmospheric conditons. Standard deviation $=2$ m with
Correlation time $=1800$ ms.

  * •

Receiver Noise: Pseudorange $=0.1$ m, and Rated range = $0.05$ m

Cocerning the Pirelli Cyber Tyre, it was mentioned earlier that it is able to
provide information about the potential friction after braking takes place.
Hence, the use of this information is not optimal for the commercial braking
logic, as it is a static velocity-dependent logic. For example, even if the
potential ground friction was measured to be low while braking is taking
place, nothing can be done about it. Considering the current AEB logic, it is
clearly seen that the nature of the logic prohibits utilizing the Ego
Vehicle’s Pirelli Cyber Tyre to its full potential.

As an initial attempt to utilize information about potential ground friction,
it is assumed that it is available from an active map. This assumption is
realistic, for example, in a scenario where several vehicles are driving in
the same area and sharing their potential ground friction estimation that is
so collected in an active map available to the ego vehicle. For this reason,
it was reasonable to include braking distance in the logic. The scheme is
shown in Figure 13. Now, the trigger to the system is no longer the TTC only,
but also the braking distance. If the braking distance becomes equal to a
certain threshold related to the distance to the collision point, the braking
maneuver takes place.

Velocity-dependent Braking Logic dec($v$) Acceleration Controller ORTTC
Trigger Braking Distance Trigger MAXdec($v$) Commercial Radar Logic 5G AEB

Figure 13: Detailed Scheme of Upgraded System A

It is noteworthy that the parameters of the braking logic weren’t modified:
this first novel approach assumed that the ECU containing the AEB logic can’t
be modified. In fact, this approach can be considered as an add-on with
respect to the commercial configuration with the minimum modification
required. The braking distance is calculated based on a the braking scheme
originally described in Figure 3. The maximum deceleration level was modified
based on the potential ground friction measurement, as described in Figure 14,
and then the braking distance was calculated by performing numerical
integration.

$0$$v_{\text{max}}$$v_{\text{a}}$$v_{\text{b}}$$|\text{deceleration}|$Level
CLevel BLevel ALevel AStage BStage AStage C300 msLevel CLevel
B$0$$v_{\text{max}}$$v_{\text{a}}$$v_{\text{b}}$$|\text{deceleration}|$Level
CLevel BLevel ALevel AStage BStage AStage C300 ms Based on $\mu$, the maximum
possible deceleration achievable for each stage is modified

Figure 14: Modification of Braking Scheme

It could be seen that the scheme provided in Figure 13 complies with the
system architecture provided in Figure 4. Here, information coming from 5G
about the position ad speed of nearby vehicles is used to evaluate TTC, and
the BD based on the received potential ground friction. Once either one of the
triggers is activated, a deceleration request is sent to the acceleration
controller.

####  4.1.3 Connected System Model: Type B

This approach means a deeper modification of the vehicle original hardware and
software configuration in order to obtain better results in terms of safety.
In an attempt for further development, it was decided to modify the braking
logic block. This is because the current braking logic does not facilitate
taking advantage of the potential of he information received from the Pirelli
Cyber Tyre. It is important to explore the option of giving the vehicle a
reference deceleration that is adaptable to the road conditions: for example:
increases exponentially throughout the braking maneuver. This approach tries
to mimic the commercial braking logic, in that the braking logic requests an
initial deceleration value of around $30\%$ of the maximum possible
deceleration, and reaches $80\%$ of its maximum value (this allows the driver
to still have control on the vehicle at maximum braking). The formulation is
given by Equation 11:

$a(t)=Ae^{\alpha t}$ (11)

Where $A=-\max\ (k_{\text{min}}\mu g,a_{\text{current}})$. The velocity
profile is given by Equation 12:

$\displaystyle v(t)$ $\displaystyle=v_{0}+\int_{0}^{t}a(t)\text{d}t$ (12)
$\displaystyle=v_{0}+\frac{A}{\alpha}e^{\alpha t}-\frac{A}{\alpha}$

The displacement of the vehicle is given by Equation 13:

$\displaystyle s(t)$
$\displaystyle=\int_{0}^{t}v_{0}+\frac{A}{\alpha}e^{\alpha
t}-\frac{A}{\alpha}\ \text{d}t$ (13)
$\displaystyle=\frac{A}{\alpha^{2}}e^{\alpha t}+Bt+C$

Now, it is considered that the vehicle must reach a complete stop at time $T$
($v(T)=0$), such that $a(T)\leq-a_{\text{max}}\mu g$ and $s(t)\leq d$, where
$d$ is the distance to the intersection. Due to complex nature of the problem,
the following approach was considered, as seen in Figure 15:

Exponential Deceleration Request dec($v$) Acceleration Controller MAXdec($v$)
FCA Radar Logic 5G AEBTrigger

Figure 15: Detailed Scheme of Upgraded System B

At every loop, the trigger block solves the following to get values of
$\alpha$ and $T$:

$v(T)=0,\ s(T)=d$

Once the values of $\alpha$ and $T$ are obtained, $a(T)=Ae^{\alpha T}$ is
calculated. If $a(T)\geq a_{\text{max}}\mu g-a_{\text{tuned}}$ (where
$a_{\text{tuned}}$ is a certain threshold that is tuned, for our case it is
$1\ \text{m}/\text{s}^{2}$), then the trigger is activated and the braking
maneuver is initiated.

It could be immediately seen that the system still has an issue with the
characteristic of the Pirelli Cyber Tyre (since it provides an estimate of the
value of potential ground friction at the moment of braking). However, the
advantage here is that the system is somehow adaptable, providing a higher
deceleration value as a compensation. The adaptability of the system is highly
affected by the value of $a_{\text{tuned}}$.

##  5 Simulation Testing Results

###  5.1 Upgraded System Type A: Normal Ground Friction Environment

The test was carried out with a ground friction coefficient equal to $0.85$ .
The test results displayed are for the case when both vehicles approach the
intersection traveling at 60 km/h. It is noteworthy that the system was tested
for all velocity combinations as outlined in Figure 10b. However, as a choice
of displaying results in this paper, the case of having both vehicles
traveling at high velocities was chosen, as it is usually the case causing
collision without connectivity as well as with low ground friction. The
modified system was tested in a normal friction conditions, as seen in Figure
16. It could be clearly seen in 17a that the maximum deceleration a vehicle
can reach in normal ground friction conditions is approximately
$8.3\text{m/s${}^{2}$}$.

(a)

(b)

(c)

Figure 16: Screenshots from CarMaker in Chronological Order: System Type A,
Normal Ground Friction

From Figure 16 it could be seen that the vehicle avoids the collision. Also,
the vehicle approaches the intersection at a reduced speed, and the stopping
takes place before the pre-defined collision point; this is quite important as
the AEB action turns off when the vehicle reaches a complete stop.

Relevant numerical results are presented in Figure 17.

(a)

(b)

Figure 17: System Type A: Normal Ground Friction, (a) Desired Deceleration vs
Actual Deceleration (b) Ego Vehicle Velocity vs Distance to Collision Point

Figure 17a shows the deceleration request as well as the actual one. Note that
the value of $-15$ $\text{m}/\text{s}^{2}$ is the equivalent of no
deceleration request. It could be seen in Figures 17a and 17b that once the
condition described in Equation 8 is satisfied, the AEB system is activated.

###  5.2 Upgraded System Type A: Low Ground Friction Environment

The modified system was tested in a low friction condition ($\mu=0.4$).

(a)

(b)

(c)

Figure 18: Screenshots from CarMaker in Chronological Order: System Type A,
Low Ground Friction

Relevant numerical results are presented in Figure 19.

(a)

(b)

Figure 19: System Type A: Low Ground Friction, (a) Desired Deceleration vs
Actual Deceleration (b) Ego Vehicle Velocity vs Distance to Collision Point

From Figure 18 it could be seen that the vehicle avoids the collision. Also,
the vehicle approaches the intersection at a reduced speed, and the stopping
takes place before the pre-defined collision point. Furthermore, comparing
Figures 17b and 19b, it could be seen that in low ground friction, the braking
starts 34 meters and 44 meters before the predicted collision point,
respectively.

It could be seen in Figure 19a that the vehicle is unable to reach the
deceleration requested as a result of the ground condition.

It is clear that the only way for the system to avoid the collision in this
case is activating the AEB system earlier thorough the braking distance
calculation which utilizes a priori knowledge about ground friction. The
difference could be clearly seen when comparing Figure 17b and Figure 19b.

###  5.3 Upgraded System Type B: Normal Ground Friction Environment

(a)

(b)

(c)

Figure 20: Screenshots from CarMaker in Chronological Order: System Type B,
Normal Ground Friction

Relevant numerical results are presented in Figure 21. It could be seen from
Figure 21a that the braking maneuver follows an exponential deceleration
scheme. Hence, with respect to the stepped scheme in System A, System B
provides a smoother profile that reflects an improvement in driving comfort.

(a)

(b)

Figure 21: System Type B: Normal Ground Friction, (a) Desired Deceleration vs
Actual Deceleration (b) Ego Vehicle Velocity vs Distance to Collision Point

When inspecting Figure 20, it could be seen that the vehicle successfully
avoids the collision. Also, the vehicle stops completely before the collision
point.

###  5.4 Upgraded System B: Low Ground Friction Environment

####  5.4.1 Friction Known A Priori

It would be interesting to investigate the case where the low ground friction
value ($\mu=0.4$) is known before the braking maneuver takes place.

(a)

(b)

(c)

Figure 22: Screenshots from CarMaker in Chronological Order: System Type B,
Low Ground Friction

Relevant numerical results are presented in Figure 23. It could be seen that
the vehicle successfully stops before the intersection, and avoids the
collision. It could be seen from Figure 23a that the braking maneuver is
smooth and comfortable. When comparing the performance of System B in low
friction with that of System A (Figure 19a), it could be seen that the system
manages to stop further away from the intersection. On the other hand, the
tunability of System B allows the vehicle to be more under control (steerable)
since the maximum value of deceleration requested can be set well below the
friction limit.

(a)

(b)

Figure 23: System Type B: Low Ground Friction, (a) Desired Deceleration vs
Actual Deceleration (b) Ego Vehicle Velocity vs Distance to Collision Point

####  5.4.2 Friction Retrieved from an Active Map

In this last case, a more realistic scenario is presented. Considering the
presence of an active map containing inaccurate ground friction estimates (due
to several possible factors such as old data, measurements errors, …). The
value provided by the map ($\mu=0.6$) is higher than the real value
($\mu=0.4$).

(a)

(b)

(c)

Figure 24: Screenshots from CarMaker in Chronological Order: System Type B,
Low Ground Friction

Relevant numerical results are presented in Figure 25.

(a)

(b)

Figure 25: System Type B: Low Ground Friction,(a) Desired Deceleration vs
Actual Deceleration (b) Ego Vehicle Velocity vs Distance to Collision Point

It could be seen that with respect to the latter situation (as in Figure 23a),
the system starts braking later. However, the maximum possible deceleration is
reached as fast as possible to ensure that the collision is avoided. It could
be seen that the safety is improved as the system is now more adaptive to the
environmental conditions.

##  6 Conclusion

To conclude, the aim of this work was the following:

  * •

develop and test two novel intersection collision avoidance systems that rely
on information from 5G, radar sensors, and Pirelli CyberTyre;

  * •

test the usability of 5G in safety systems of connected vehicles.

As presented in the paper, state of the art AEB systems usually issue a
predefined braking request based on a static trigger. The advantage of such
existing systems is the simplicity which reflects the simple hardware and
reduced costs. Starting from these consideration, a novel approach consisting
of an upgrade of the original architecture in order to limit costs of the
design of new hardware and software, is presented. While keeping the same
braking logic adopted in the radar system, an additional trigger was added to
enable braking based on braking distance. The results of the virtual testing
have been positive in both normal ground friction and low ground friction
environments. In order to fully take advantage of the ground friction
estimate, a second novel AEB control logic able to adapt itself during the
braking is proposed. In detail, a braking scheme with an exponential-scheme
was adopted as it ensures smooth braking as well as high robustness and
adaptability to road conditions as shown by virtual simulations and comparison
to the previous AEB proposed. However, due to the complex nature of the
equations to be solved (to find $T$ and $\alpha$), the system requires much
higher computational power. Also, the tuning of the parameter
$a_{\text{tuned}}$ presents a disadvantage, as setting a high value will make
the system start braking very early but the system will be more robust, and
setting it very low will make system start braking at normal time but this
will limit robustness.

## Acknowledgments

This work is done in collaboration between mechanical engineering department
of Politecnico di Milano, iDrive Lab of Politecnico di Milano and Vodafone.
The work presented is funded by, and part of the Vodafone 5G Trial in Milan.
The authors would like to thank all their collaborating partners in the
Vodafone 5G Trial in Milan: Altran, Vodafone Automotive, Magneti Marelli,
Pirelli, and FCA. A special thanks goes to IPG for providing student licenses
that were necessary for the development and testing of the developed work. A
special thanks goes to Mr. Gianluca Stefanini for his help and advice on 5G
communications.
The Italian Ministry of Education, University and Research is acknowledged for
the support provided through the Project “Department of Excellence LIS4.0 -
Lightweight and Smart Structures for Industry 4.0”.

## Disclosure statement

The author(s) declared no potential conflicts of interest with respect to the
research, authorship, and/or publication of this article.

## Funding

The author(s) disclosed receipt of the following financial support for the
research, authorship, and/or publication of this article: This work has been
supported by Vodafone Italia under Vodafone 5G Trial in Milan.

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