# Development and Virtual Testing of 5G Connected Adaptive Cruise Control

Andrea Leo  Giacomo Reatti  Stefano Arrigoni  Michael Khayyat  Federico Cheli

###### Abstract

It is estimated that 90% of crashes occur due to human error, mainly induced
by poor judgement, distraction or lack of situation awareness [1]. The
development of systems that aim to increase road safety and to improve driving
comfort is the key challenge that many OEMs are currently tackling. The use of
fast and reliable of innovative communication technologies such as 5G can
enhance advancements and developments in the field of ADAS. The aim of the
paper is the study of the potential improvements connectivity can bring to ACC
systems in terms of safety and vehicle comfort. Starting from state-of-art ACC
present in literature, a novel connected ACC is designed an deeply reported.
The main features of the algorithms consist in integrating information about
other road vehicles fed by 5G and about potential grip given by smart sensors
such as Pirelli Cyber Tyre. The novel solution is then compared to the
standard ACC by means of several simulations considering two real traffic
situations with different road friction coefficients. Finally benefits and
limitations of the proposed algorithm are deeply discussed.

###### keywords:

Connected Adaptive Cruise Control , 5G , V2V , V2I , Connected Vehicles , ADAS

[inst1]organization=Department of Mechanical Engineering,addressline=Via La
Masa, 1, city=Milan, postcode=20156, country=Italy

##  1 Introduction

According to the World Health Organization (WHO), road traffic injury
represents the eight cause of death in the world and the Leading cause of
death for children and young adults aged 5-29 years [2]. Every year the number
of road deaths is around 1.3 million and between 20 and 50 million people
sustain non-fatal injuries. Among the various causes, human errors seem to be
the main cause of road accidents. In fact, speeding contributes up to 35% of
fatal road crashes and driving under the influence of alcohol contributes up
to 30% of them [3]. Scientific research and car manufacturers are therefore
focused on increasing road safety by introducing Advanced Driver Assistance
Systems (ADAS). By adjusting human driver decisions and actions, ADAS aim to
eliminate most of driver errors and achieve a more regular and smooth vehicle
control [4]. Recently, the evolution of ADAS is progressively shifting towards
connected driving [5]. In particular, the use of 5G for V2V and V2I
transmissions is gaining importance due to its reliability, speed and ability
to support a high demand of data. Features like Adaptive Cruise Control (ACC)
were introduced to increase driving comfort and traffic safety, especially
during the use in highways or non-urban roads. The evolution into a Connected
ACC permits to have an improved behaviour in dangerous situations, especially
in ones involving side and rear-end collisions,[6], and to reduce traffic jams
yet maintaining a high traffic throughput [7]. Hence, the development and the
testing of a Connected Adaptive Cruise Control systems that use 5G V2V
communication system and Pirelli Cyber Tyre to estimate potential grip is
therefore proposed.

##  2 Literature Review

###  2.1 Adaptive Cruise Control Systems

The Adaptive Cruise Control (ACC) is a technology that extends the
capabilities of the standard Cruise Control (CC) [8]. Cruise Control is
capable of making the vehicle travel at a user-set speed, typically selected
through steering wheel commands. ACC is supported by range sensors that make
the car aware of the condition of the Preceding vehicle and therefore can
adjust the speed according to the relative distance and velocity with it.

ACC has been introduced in commercial vehicles in Japan since 1995, followed
by Europe since 1998 and North America since 2000 [9]. They were firstly
introduced as devices aimed at increasing passengers driving comfort. However,
soon researchers and car companies started considering them also as systems
able to deal with safety issues and improvement of traffic congestion.

####  2.1.1 System Architecture

Figure 1: Example of ACC system architecture [9]

The typical architecture of a ACC is represented in Figure 1. Here four main
modules can be recognized:

  1. 1.

Signal Collecting (SC): this module is responsible for providing the whole
logic the necessary inputs. Relative distance and relative speed are obtained
through the radar sensor, while other sensors are able to detect the path that
the car is going to approach.

  2. 2.

Signal Processing (SP): in this part the control logic of the ACC system is
located. The picture shows a typical control logic structured into a Speed
Control module and a Spacing Control one (the latter indicated as Distance
Control module).

  3. 3.

Signal Actuating (SA): the desired acceleration computed in SP module is here
converted to throttle or brake command. If the vehicle is provided with a
gearbox, also a transmission control module is needed to select the correct
gear.

  4. 4.

Signal Displaying (SD): when the ACC is activated, the control indicator
displays the operating state (speed control or spacing control).

Another scheme usually considered to specifically describe controller logic
architecture is reported in [8] and shown in Figure 2:

Figure 2: Upper and Lower Level Scheme

ACC control algorithm is designed through a hierarchical scheme composed by
two levels. The upper level controller is properly called ACC controller,
while the lower level is called longitudinal controller. The output of the ACC
controller is the desired acceleration which is transmitted to the
longitudinal controller. The latter determines the throttle and/or brake
command using vehicle dynamic models, engine maps and control techniques.

The desired acceleration is limited in a range of values. The choice of the
extremes of the range is, in fact, a design parameter. In Table 1 the values
used by literature for the maximum acceleration and deceleration are reported.

Reference |  Max. Acceleration [$m/s^{2}$] |  Max. Deceleration [$m/s^{2}$]
---|---|---
[10] | 0.6 | -1.5
[11] | 1 | -2
[12, 13] | 1 | -2.5
[8, 14, 15] | 2 | -5
Table 1: Acceleration and deceleration limits for ACC systems according to
literature

Apart from acceleration constraints, some ACC systems consider acceleration
rate (also referred to as jerk) as part of the optimization process when high
importance is given to driving comfort [16, 10]. Reference limitation values
are 0.1 $m/s^{3}$ for positive jerk and -1 $m/s^{3}$ for negative jerk.
Another constraint is that ACC automatically deactivates when the speed is
lower then a certain threshold (typically of $25-40km/h$). As discussed in
[17], ACC systems find their typical application in highways, while their
introduction into urban traffic requires the use of complicated additional
sensors to properly recognize all objects and obstructions. Moreover,
automatic positive acceleration at low speed has to be avoided for safety
reasons [12].

Most of the developed ACC systems have two modes of operation:

  * 1.

Speed control (often referred to as Velocity Control)

  * 2.

Spacing control (or Vehicle Following, Distance Control)

In order to shift from one logic to the other, a switching method is needed.
The purpose and need of a transitional control algorithm is to allow a smooth
transition between these two operation modes and a reaction in case of a new
preceding vehicle is taken as reference [8]. This situation is common in case
of vehicle cut-in111A cut-in is defined as a manoeuvre where a vehicle
travelling on lane suddenly moves to an adjacent one in between two other
vehicles. or lane change maneuver.

Different spacing control systems have been proposed. Linear controllers are
the most common one for their programming simplicity and consists of a
Proportional Integral Derivative controllers (PID)[8, 18, 14, 19, 20, 21, 22].
Their use for ACC systems is widespread in car companies, as many patents show
[23, 24]. Model Predictive Control (MPC) has been used more recently since it
is able to solve at a higher computational cost multi-objective problems [10,
25, 26, 27]. Moreover, fuzzy and neural controllers have been suggested to
replicate a car-following behavior similar to the humans would have [28, 29,
30, 31]. Finally, also the sliding surface method derived from [32] has been
used in Rajamani [33], providing accurate and reliable performances.

####  2.1.2 Spacing Policies

The definition of a ACC system starts with the definition of a spacing policy
[34]. When the controlled vehicle operates in Spacing Control, a certain
spacing policy has to be selected, which is responsible for defining the
desired steady state distance between two successive vehicles. The choice of a
suitable spacing policies is fundamental both for safety reasons since the
time available to brake in emergency scenarios is determined by the inter-
vehicle distance and for comfort reasons since a too aggressive policy can
cause unnecessary acceleration and deceleration which lead to a low comfort
for the passengers.

Figure 3: Notation for spacing policies [8]

Many spacing policies are present in literature and discussions are still open
on which one can guarantee better performances. In the following, the index
notation of Figure 3 will be used. Among the numerous policies, the most
widespread are:

  * 1.

Constant distance spacing (CD):
In this kind of policy, two consecutive vehicles must keep a constant distance
$L_{desired}$. The inter-vehicle distance is defined in equation 1:

$\epsilon_{i}=x_{i}-x_{i-1}+l_{i-1}$ (1)

where $x_{i}$ and $x_{i-1}$ are the position of the two vehicles and $l_{i-1}$
the length of the Preceding vehicle. The spacing error of the $i-th$ vehicle
is then defined in equation 2:

$e_{p,i}=x_{i}-x_{i-1}+L_{desired}$ (2)

Assuming that the acceleration of the vehicle can be controlled immediately, a
linear controller is applied, as shown in 3:

$\ddot{x}_{i}=-k_{p}e_{p,i}-k_{v}\dot{e}_{p,i}$ (3)

where $k_{p}$ and $k_{v}$ are control gains.

With this kind of controller, Rajamani [8] demonstrates that a linear control
system that adopts the constant spacing policy cannot guarantee string
stability for autonomous vehicles that are not provided with inter-vehicle
communication.

  * 2.

Constant time gap spacing (CTG):
In CTG policy, the inter-vehicle distance is not constant. As shown in 4, it
varies linearly with the following vehicle velocity:

$L_{desired}=d_{min}+h\dot{x}_{i}$ (4)

where $h$ is the desired time gap and $d_{min}$ is the inter-vehicle distance
at rest. Time gap is defined as the time it takes for a vehicle to cover the
distance measured from the front of the following vehicle to the rear of the
preceding one [35, 36]. Consequently, the spacing error varies linearly with
velocity, as represented in 5:

$e_{p,i}=\epsilon_{i}+h\dot{x}_{i}$ (5)

where $\epsilon_{i}$ has the same expression of 1. Iannou and Chien [37]
proposed the control law in 6:

$\ddot{x}_{i,des}=-\dfrac{1}{h}(\dot{\epsilon_{i}}+\lambda e_{p,i})$ (6)

where $\lambda$ is a control parameter and has to be chosen greater then zero
[38]. Again, computing the transfer function between spacing error of
following vehicle and spacing policy of Preceding vehicle, its magnitude is
always equal or less then the unity only if the necessary and sufficient
condition represented in 7 is respected:

$h\geq 2\tau$ (7)

where $\tau$ is the time constant of the lower level control (typically its
value is around 0.5$s$). Therefore, in general time gap should be increased
over 1$s$ to avoid string stability: this represents a limit for this kind of
logic when the aim is to maximise the traffic capacity.

  * 3.

Variable time gap spacing (VTG):
An evolution of CTG policy for ACC systems has been made by Santhanakrishnan
[39] and Wang [14]. It aims at satisfying string stability and traffic flow
stability, still guaranteeing high traffic flow capacity. The policy has a
nonlinear dependency on speed unlike the common one. Starting from the spacing
policy shown in 8:

$e_{p,i}=\epsilon_{i}+g(\dot{x}_{i})$ (8)

where $g(\dot{x}_{i})$ is the nonlinear function that substitutes the linear
term with time gap of 5. By linearizing this policy, it is possible to get a
local condition for string stability, as shown in 9:

$\frac{\partial g}{\partial\dot{x}_{i}}>2\tau$ (9)

This condition is then evaluated in term of traffic volume rate $Q$ and
highway vehicle density $\rho$ in order to add the requirements of traffic
flow stability, which is $\frac{\partial Q}{\partial\rho}>0$. However, this is
possible only for certain values of $\rho$: in fact, the traffic flow rate
drops to zero at maximum density, so that a negative slope is inevitable.
Therefore these results are restricted only to particular situations and the
control designer should be aware in which scenarios its logic will be mainly
used.

  * 4.

Variable separation error gain spacing:
This logic [22] finds interesting application in heavy-duty vehicles that
require larger inter-vehicle distances due to the higher difficulties for
their actuators to act on such a big inertia. Considering spacing error (10)
and relative speed (11):

$e_{p,i}=(x_{i}-x_{i-1})-(d_{min}+h\dot{x}_{i})$ (10)
$v_{rel}=v_{prec}-v_{follow}$ (11)

Putting together 10 and 11 the control objective is shown in 12:

$v_{rel}+ke_{p,i}=0$ (12)

The idea is to vary the separation error gain $k$. In fact, if two vehicles
are closer then desired ($e_{p,i}<0$) and the following vehicle is slower then
the Preceding one ($v_{rel}>0$), the controller of the following vehicle does
not need to react drastically. The same holds also if the vehicle are
separating ($e_{p,i}>0$) but ($v_{rel}<0$): the vehicle behind should behave
so that spacing error decreases smoothly.
$K$ is computed in 13 as:

$k=c_{k}+(k_{0}-c_{k})e^{-\sigma e_{p}^{2}}$ (13)

where $0<c_{k}<k_{0}$ and $\sigma\geq 0$ are design constraints. According to
which value of $k$ is chosen, the control objective becomes nonlinear in
$e_{p}$. Simulation results show that the variable separation error gain
spacing policy should be preferred when control smoothness and control
robustness a priorities in control design.

  * 5.

User acceptance spacing:
In [40], Han and Yi suggest a spacing policy that can accurately replicate the
behaviour of a human driver. A recursive least-square algorithm has been
applied to data collected from 125 human drivers to estimate driver tendency
parameters. In particular, these variables have been selected:

    * (a)

Time gap

    * (b)

Time to Collision (TTC), where:

$TTC=\frac{x_{i}-x_{i-1}}{v_{i}-v_{i-1}}$ (14)

The adopted controller is a quadratic range policy and it has been set
according to these estimated parameters. The comparison between data from
manual driving and with ACC system with this logic show a good similarity,
showing that it is possible to obtain a driving behaviour that can prioritize
user-acceptance. Similarly, in [41] a study has been performed on 107 human-
driven vehicles to obtain an analytical expression of their range behaviour.
Once more, a quadratic range policy has been proposed, as shown in 15:

$R=A+Tv_{i}+Gv^{2}_{i}$ (15)

where A is the distance at standstill and T and G are coefficients found by
curve fitting. The relationship between T and G is found to be almost linear.
Furthermore, negative values of G show that at high speed human drivers tend
to reduce the time gap.

Among all the aforementioned systems present in literature, from the point of
view of the automotive industry, the most widespread adopted spacing policy
results to be the CTG spacing policy, considering a good balance between
safety, stability, reliability, feasibility and capability.

###  2.2 Cooperative Adaptive Cruise Control

Cooperative Adaptive Cruise Control (CACC) is defined as an ACC system
equipped with V2V communication [42, 43, 44, 45, 46, 47, 48]: with this kind
of communication the vehicle gets information not only from its Preceding
vehicle, as occurs in ACC, but also from the vehicles in front of the
Preceding one [44, 47]. In this way, the controller is capable of better
anticipate of risky situations thanks to the information of vehicles that are
behind the so-called LOS (Line Of Sight), thus giving a smoother response in
terms of driver comfort [46] (mitigation of shock waves effects) and safer
response in terms of road safety. It is therefore important to notice that the
CACC system has a potential beneficial effect on traffic efficiency and
safety: the idea is to significantly reduce the time-gap to increase the
traffic throughput [49] with respect to the allowed ACC time-gap which is
lower bounded by the string-stability requirements [8].

####  2.2.1 Control Strategies

Many authors in literature focus on developing their CACC control logic by
extending the functionality of an ACC system adding communication between
vehicle and infrastructures. In particular, Dey et al.[45] review literature
on this aspect and they pointed out that, considering a string of vehicles,
the majority of the CACC systems extensively exploits V2V communication with
just the immediate nearest vehicle, contrary to communication with multiple
Preceding vehicles or with a designated platoon leader. The change in type of
vehicles communication is therefore reflected in different control strategies.

This kind of systems are generally referred to as Semi-Autonomous Adaptive
Cruise Control (SAACC) and they have the advantage of being easy to be
implemented. As a first example, Rajamani et al. [50] claim that the positive
aspects of the ACC system can be combined and improved by introducing
communication with only the preceding vehicle. More recently, the idea of
enhancing the control logic of a ACC system by adding communication with only
the nearest preceding vehicle was employed also by Naus et al.[42]. Their
control design has been widely used in literature, for example by [47, 43,
44]. They therefore propose a decentralized controller design in which the
communication with the nearest preceding vehicle makes the acceleration of the
aforementioned vehicle available to the controlled one. This means that a
delay has to be added which in their work is represented by a constant
$\theta$.

It is interesting to note that, in this case, communication will be
implemented as a feedforward signal. Hence, the acceleration of the preceding
vehicle is used as a feedforward control signal through a feedforward filter.
A feedforward filter design is thus proposed, based on a zero - spacing error
condition: this condition is equivalent to say that the inter-vehicle distance
coincides with the desired distance and so the vehicles keep the correct
spacing between them.

The previously described control logic is also present in [48], where it is
applied to heterogeneous traffic, focusing in particular on heavy duty
vehicles. The authors claim that a significant reduction of drag force and so
decreasing fuel consumption is expected, but results show that only marginal
string stability can be obtained. Therefore, the design cannot be considered
robust for uncertainties or errors in the modelling process Furthermore, Van
Der Werf et al.[51] focus as well on providing the simplest possible
cooperative architecture and the easiest one to be implemented.

Their work is then used as base model to develop a CACC control logic by Van
Harem et al.[46] in which a focus on the impact of CACC on traffic - flow
characteristics is also present.

The authors finally claim that the CACC system shows potential positive
effects on traffic throughput but they also state that the type of
communication chosen might be not useful enough to guarantee better traffic-
flow performance.

Hence, it is clear how results are affected by the type of communication
chosen and how different and more complicated information flow topologies
should be investigated. It is also evident that an innovative and reliable
type of communication such as 5G can be beneficial in this case. As a
consequence of this, Lidstrom at al.[47] proposed a modified CACC controller
(based on the afore-mentioned work of Naus et al.[48] and [42]) in particular
considering the design of the feed forward filter.

Figure 4: Vehicles platoon equipped with CACC functionality [47]

As it can been seen in Figure 4, this control strategy differs from the ones
of [48] for the type of communication: information are taken not only by the
directly previous vehicle, but the acceleration signal is transmitted also by
the leading vehicle.

So, essentially:

  * 1.

The feedforward filter is now chosen to achieve string stability, but no
conditions on having zero spacing error are stated;

  * 2.

The acceleration to be given in feedforward is chosen to be the minimum
between the acceleration of the leading vehicle and the acceleration of the
preceding vehicle.

Results show that the controller is able to achieve string stability with a
certain robustness against dynamics variations and is also able to improve
safety during deceleration.

##  3 Developed Systems Description

###  3.1 Commercial Adaptive Cruise Control System

Figure 5: ACC System architecture of a commercial vehicle

The functional architecture of a base case, commercial ACC is shown in Figure
5. It is based on a radar installed on the front part of the vehicle that
monitors the road ahead and detect other vehicles. As long as no obstacle is
detected, the ACC follows the user-set speed working as a classic Cruise
Control in Velocity control. If the system detects a slower object within the
radar sensor range, it reduces the speed comfortably either by releasing the
gas pedal or by activating the Braking System Module. If the vehicle in front
accelerates or change its driving lane, ACC automatically increases its speed
up to the user-set speed through the Engine Torque Module. These two tasks are
accomplished by a commercial ECU (represented in Figure 5). Moreover, to
perform the overall functioning of the system (and to close the simulation
loop as well), a vehicle model is considered, which is represented by the
”Vehicle Dynamic Response” block in Figure 5.

Afterwards, the commercial ACC system is chosen to be characterized by a
hierarchical longitudinal control system architecture, composed by an upper
and a lower level controller:

  * 1.

The upper level controller is the designed ACC controller and determines the
desired acceleration;

  * 2.

The lower level controller determines the gas and/or brake commands required
to track the provided desired acceleration.

####  3.1.1 Upper Level Control Logic

The upper level controller has to provide a desired acceleration command to
the lower level control logic. This command is limited between fixed values
$a_{Ego,min}^{des}$ and $a_{Ego,max}^{des}$ in order to avoid abrupt
manoeuvres. Their values are:

$\begin{cases}a_{min}^{des}=-5m/s^{2}\\\ a_{max}^{des}=2m/s^{2}\end{cases}$

The maximum boundaries for acceleration are chosen considering the most common
values shown in 2.1.1. In particular, the values selected by [8, 14, 15] have
been chosen. As already mentioned, two types of controller have to be
implemented in order to allow the system to work both in Speed and Spacing
Control. Speed control is active when the Preceding vehicle is far away from
the Ego vehicle or the Preceding vehicle is travelling faster then the Ego
car. The control logic is a PI-controller [8] that gets as input the actual
car speed and the driver-set speed $v_{user}$. By definition, a PI-controller
has no steady state error. Moreover, settling time and overshoot were adjusted
by tuning the controller gains.
Hence, the desired acceleration in speed control is given by 16:

$a_{Ego,VC}^{des}(t)=k_{p}(v_{user}(t)-v_{Ego}(t))+k_{i}\int\limits_{0}^{t}(v_{user}(t)-v_{Ego}(t))dt$
(16)

where $k_{p}$ and $k_{i}$ are results of gain tuning.

Before introducing the choice of the spacing controller, it is now important
to present the transitional controller design, which is needed in order to
switch from Speed Control to Spacing Control. Hence, a range versus range-rate
relationship [52] has being adopted. A generic representation of a range
versus range-rate diagram is reported in Figure 6.

Figure 6: A range versus range-rate diagram: example of linear trajectory
along the $R-\dot{R}$ diagram [52]

The conventions used to express range $R$ and range rate $\dot{R}$ are:

$R=x_{Preceding}-x_{Ego}$ $\dot{R}=v_{Preceding}-v_{Ego}$

An interesting property of this representation is that on the left half-plane,
since $\dot{R}$ is negative, $R$ can only decrease, while on the left half-
plane, since $\dot{R}$ is positive, $R$ can only increase.
The desired operating condition is represented by point $(0,RH)$. In this
point the range $R$ corresponds to the desired distance given by the spacing
policy, while the relative speed $\dot{R}$ among the vehicles is zero. The
reasons why this control strategy has been selected are the following:

  * 1.

It takes into consideration transitional manoeuvres, so that the desired
distance from the Preceding vehicle can be established and maintained
smoothly;

  * 2.

Engine and especially brake actuators can saturate, so a solution that avoids
hard braking is preferred;

  * 3.

When a new Preceding vehicle appears, it is not considered as a target vehicle
if it is far enough or its speed is higher then the controlled vehicle speed;

There are two kinds of transitional manoeuvres according to the considered
traffic scenario [52]:

  * 1.

When a slower vehicle is encountered, the controlled vehicle performs a linear
trajectory in the $R-\dot{R}$ diagram until the steady state condition $RH$ is
reached, as shown in 6.

Starting from velocity control, the transitional manoeuvre sub-logic activates
when, for a certain $\dot{R}$, the difference between the actual inter-vehicle
distance and the corresponding inter-vehicle distance $R_{line}$ on the
switching line for the same $\dot{R}$ is smaller than a tolerance, as shown in
17:

$R-R_{line}<tol_{speed-linear}$ (17)

where it is assumed $tol_{speed-linear}=0.5m$ as a reasonable value during the
selection of parameters. The term $R_{line}$ refers to the expression of the
linear trajectory and it is reported in 18:

$R_{line}=-T\dot{R}+RH$ (18)

The slope of the switching line is indicated as $T$ and it is given by 19
[52]:

$T=\sqrt{\frac{R_{s}-RH}{2D}}$ (19)

where:

    * (a)

$R_{s}$ is the maximum range to consider, in this case the maximum radar
sensor range in $[m]$;

    * (b)

$RH$ is the desired inter-vehicle distance in $[m]$ and in the case of
constant time gap it depends linearly on the controlled vehicle speed, as
shown in 20:

$RH=d_{min}+hv_{Ego}$ (20)

where $d_{min}=2m$ [8] is the inter-vehicle distance at rest.

    * (c)

$D$ is the coasting deceleration equal to 0.04$g$ $m/s^{2}$ [52].

A better description of the behaviour of this transitional manoeuvre is
possible considering 6. Starting from an initial condition A (where the inter-
vehicle distance is high) and considering that the Ego vehicle is travelling
in velocity control with a constant negative relative speed with respect to
the Preceding vehicle, the sub-logic activates when the trajectory crosses the
switching line in point B. The controller then brings the operating condition
to spacing control (thus, to point $(0,RH)$) following the slope.
With a linear transitional manoeuvre, the acceleration command is the result
of a P-controller and it is shown in 21:

$a_{Ego,TC}^{des}(t)=k_{p}(R_{line}-R)$ (21)

where $k_{p}$ is a controller gain. During the process of controller tuning,
it is possible to make the vehicle perform a straight line from B to $RH$ even
if some small oscillation is inevitable due to the controller overshoot.

  * 2.

When a Preceding vehicle cuts-in with a lower speed then the Ego car, it could
be necessary to perform a hard braking. Assuming that the maximum deceleration
$a_{Ego,min}^{des}$ of the system is required, this implies a constant
deceleration manoeuvre. On the $R-\dot{R}$ diagram this corresponds to a
parabolic trajectory as described by 22:

$R_{parabola}=R_{amn}+\frac{\dot{R}^{2}}{2a_{Ego,min}^{des}}$ (22)

where $R_{amn}$ is the minimum of the parabolic trajectory, which occurs when
$\dot{R}=0$, as visible in 7. The condition to activate the parabolic
transitional manoeuvre is based on comparing the actual range $R$ with the
value of $R$ belonging to the switching line for the same $\dot{R}$. As
indicated in 23, if their difference is lower then a tolerance, parabolic
trajectory sub-logic is activated:

$R-R_{line}<tol_{linear-parabolic}$ (23)

where it is assumed $tol_{linear-parabolic}=-30m$ [52].
Once this transition mode is activated, the acceleration command to be given
to the lower level controller is the result of a P-controller (as discussed in
[8]). This controller is applied to the relative distance between vehicles and
the corresponding $R$ point along the parabola, given a certain $\dot{R}$, as
shown in 24:

$a_{Ego,TC}^{des}(t)=k_{p}(R_{parabola}-R)$ (24)

where $k_{p}$ is a controller gain. As said, this parabolic trajectory is
likely to occur when a Preceding vehicle cuts-in with a lower speed then the
Ego car. Considering 7, initial condition can be described by point A, where
$\dot{R}$ is negative and the desired spacing $R$ is lower then the desired
value. The transitional manoeuvre activates according to the condition
reported in 23 and the parabolic trajectory reaches its minimum in
correspondence of $R_{amn}$ which has to be positive to avoid collision. In
this point the inter-vehicle distance is lower then the desired $RH$. Then the
trajectory keeps on following the parabolic path in the right half-plane.
Here, a positive $\dot{R}$ means that the Preceding vehicle is driving faster
than the controlled one and so this represents a safe situation. In order to
reach the final position in point $(0,RH)$, the trajectory continues along the
parabola until it crosses the switching line given by 18 in point C. From this
point the vehicle performs the linear trajectory until final condition is
reached.

Figure 7: Example of parabolic trajectory along the $R-\dot{R}$ diagram

The sub-logic that contains the spacing control is activated when the
operating point in the range versus range-rate diagram is close enough to the
desired final condition in point $(0,RH)$:

$|R-RH|<tol_{transitional-spacing}$ (25)

where it is assumed $tol_{transitional-spacing}=0.5m$, the same value also
given to $tol_{speed-linear}$. The reason for the absolute value in equation
is to include both the situations in which during the transitional manoeuvre
the operating point can move on the switching line coming from the left half-
plane or from the right half-plane.
The choice of the spacing controller has been made based on a series of
considerations. Firstly, it has to satisfy the requirements of string
stability and reduction of any distance and speed deviation from reference
values. Furthermore, among all the possible control strategies, it is
preferred to adopt one that depends on physical quantities, so that it is more
intuitive to set the parameters. The selected controller is taken from the
work of Winner et al. [53] which has deeply influenced the development of
commercial ACC systems [11]. The controller is represented in 26:

$a_{Ego,SC}^{des}(t)=\frac{\dot{R}-\frac{d_{set}-R}{\tau_{d}}}{\tau_{v}}=\frac{\dot{R}}{\tau_{v}}-\frac{d_{set}-R}{\tau_{v}\tau_{d}}$
(26)

where $\tau_{d}$ and $\tau_{v}$ are parameters and $d_{set}$ is defined
according to the constant time gap spacing policy, as shown in 27:

$d_{set}=d_{min}+hv_{Ego}$ (27)

where $d_{min}=2m$ [8] is the inter-vehicle distance at rest. In order to
guarantee string stability, the condition on the parameters in 28 has to be
satisfied according to [53]:

$\tau_{v}\leq\tau_{gap}\left(1+\frac{\tau_{gap}}{2\tau_{d}}\right)$ (28)

As described in [53], the control approach represented by 26 is string stable
and also meets the requirements of a constant time gap only if the condition
in 29 is satisfied:

$\tau_{v}=\tau_{set}$ (29)

Therefore, keeping into account 28, the condition on $\tau_{d}$ in order to
have string stability is shown in 30:

$\tau_{d}\geq 0$ (30)

The value for $\tau_{d}$ is selected considering the scenario described in
[53]: a vehicle cuts in without a speed difference at a distance that is
$\gamma=20m$ smaller the the desired distance. Considering a reasonable
deceleration of $1m/s^{2}$ to be applied to the vehicle, which corresponds to
take the foot off from the gas pedal or slightly braking, according to 26 it
is possible to find a value for $\tau_{d}$ inversely proportional to
$\tau_{v}$, as seen in31:

$\tau_{d}\tau_{v}=\gamma\implies\tau_{d}=\frac{\gamma}{\tau_{v}}$ (31)

###  3.2 CACC system

The architecture of the novel CACC system is shown in 8.

Figure 8: Connected ACC system architecture

The approach adopted is to consider the base case commercial ACC logic
previously described as the main logic on which the novel developed
functionalities are connected as Add-Ons. These additional features are able
to extend the potentialities of the original ACC system. Hence, the original
ACC function block is considered as a fixed component and consequently is not
modified, but the logic received now information coming from the 5G
communication block and from the Cyber Tyre block. This approach is motivated
by the following considerations:

  * 1.

The implementation of the Connected ACC is much faster and not very demanding
in term of cost and time for design since the original ECU of the vehicle does
not need to be redesigned or substituted. Therefore, this solution is ready to
be introduced in the market in a short time;

  * 2.

The original commercial ACC system is a logic that has been already tested and
has proved to work correctly in a wide range of scenarios.

Considering a string of three vehicles as shown in 9, it is assumed that real
time positioning, speed and acceleration of the Preceding and Leading vehicles
through GPS information can be transmitted through the 5G network. Hence, the
Ego vehicle receives information both through 5G communication (displayed with
the red arrows) and the radar system (displayed with the blue arrow).

Figure 9: Information Flow Topology

At this point, from the exchanged data between the Ego and the Leading
vehicle, a a Time-To-Collision ($TTC$) has been built. The use of $TTC$ as
safety indicator is widespread in literature. It was firstly introduced by
Hayward [54] and later developed by Hyden [55], founding a great success in
collision avoidance problems such as [56, 57, 58] because of its ability to
consider the distance between vehicles weighted on their relative speed.
Therefore, a risky situation occurs when $TTC$ is too low which is the result
either of a much bigger $v_{Ego}$ compared to $v_{lead}$ or of a too small
inter-vehicle distance. A threshold value $TTC_{lim}$ is taken as indicator
for the severity of the scenario and its value ranges from $1.5s$ to $4s$[58].
A high $TTC_{lim}$ can result in generating too many false alarms while, in
the case of autonomous vehicles, lower values are allowed due to a more
adequate response in case of dangerous situations [58].
In case the TTC is computed between two vehicles separated by another vehicle,
not only two inter-vehicle spacing have to be considered, but also the length
of the car in-between. Therefore, it is suggested to assume $TTC_{lim}=6s$.

In this work, the inverse of the Time-To-Collision $TTC^{-1}$ is considered.
Its definition has the same physical meaning of the $TTC$, but the expression
is different. The inverse of the time to collision of the controlled vehicle
with respect to the Leading vehicle is represented in 32:

$TTC_{E,L}^{-1}=\frac{\Delta v_{E,L}}{\Delta x_{E,L}}$ (32)

where:

$\Delta v_{E,L}=v_{Ego}-v_{lead}\\\ $ (33) $\Delta x_{E,L}=x_{lead}-x_{Ego}$
(34)

The threshold value to be considered therefore is:

$TTC_{lim}^{-1}=\frac{1}{TTC_{lim}}=\frac{1}{6}s$ (35)

The information coming from 5G network and the computation of the $TTC^{-1}$
are used to modify the relative speed between the Ego vehicle and the
Preceding one when the cars are in Spacing Control. In 10 the representation
of the modification to $\dot{R}$ is showed.

Figure 10: Representation of the modification to $\dot{R}$

In the case where that $TTC_{E,L}^{-1}$ is lower then the threshold value, the
modifications to the relative speed between Ego and Preceding vehicle is given
by 36:

$\dot{R}_{mod}=\left(1-sgn(\dot{R})\frac{\Delta v_{E,L}}{n_{1}}\right)\dot{R}$
(36)

where $n_{1}$ is a normalization factor and it is represented by the imposed
speed limit of the road.

Figure 11: Effect of $\dot{R}$ modification according to different situations

In 11, the physical meaning of the modification to $\dot{R}$ for the Ego
vehicle is explained. In particular, it can be stated that the control logic
is able to perform in a wide range of scenarios:

  * 1.

In case of a positive $\Delta v_{E,L}$ (therefore $v_{Ego}>v_{lead}$), the
Leading vehicle is travelling slower then the Ego vehicle. Therefore, a
preventive action is desired in order to avoid possible dangerous scenarios.
If the speed of the Preceding vehicle is higher than that of the Ego vehicle
(situation ①), a potential variation of $\dot{R}$ is smoothed. In fact, since
the Leading vehicle is slower, it is predictable that soon also the Preceding
vehicle is going to slow down. This modification to $\dot{R}$ can allow to
avoid unnecessary acceleration command. On the other hand, if the Ego car is
faster than the Preceding one (situation ②) , the modified $\dot{R}_{mod}$ is
higher in absolute value with respect to the original $\dot{R}$. Considering a
scenario where the Leading vehicle starts decelerating, the advantage of
adopting this solution is that a connected Ego vehicle is able to anticipate
the braking manoeuvre, so that it does not have to wait for the car in-between
to react. This can prevent risk of collision.

  * 2.

In case of a negative $\Delta v_{E,L}$ (therefore $v_{Ego}<v_{lead}$), the
Leading vehicle is increasing its distance from the Ego vehicle. If the
Preceding vehicle is faster than the Ego (situation ③) , the modified
$\dot{R}_{mod}$ is higher than $\dot{R}$ so that a higher acceleration is
produced as output by the control logic. This can result in a vehicles being
closer and therefore a higher traffic capacity. On the other way, when the
Preceding vehicle travels at a lower speed then the Ego (situation ④) , the
value for $\dot{R}$ is decreased in absolute value so that the control logic
can provide a smoother deceleration. This does not represent a danger in case
of hard-braking since in 36 the behaviour of the Preceding vehicle still plays
a major role.

  * 3.

In the case that the Leading and Ego vehicle are travelling at the same speed,
so $\Delta v_{E,L}=0$, the input to the vehicle-following control logic does
not change and therefore the inter-vehicle distance matches with the distance
given by the constant time gap policy.

In case that $TTC_{E,L}^{-1}$ is higher then the threshold value, the
modifications to the relative speed between Ego and Preceding vehicle is given
by the following equation 37:

$\dot{R}_{mod}=\left(1-sgn(\dot{R})\frac{\Delta
v_{E,L}}{n_{1}}+\frac{TTC_{E,L}^{-1}-TTC_{lim}^{-1}}{n_{2}}\right)\dot{R}$
(37)

where $n_{1}$ and $n_{2}$ are normalization factors. As concerns $n_{1}$, it
holds what explained for 36. The value assigned to $n_{2}$ is assumed to be
$TTC_{lim}^{-1}$, so that all the multipliers of $\dot{R}$ in 37 are
comparable in term of order of magnitude. It is important to underline that in
the case where 37 is applied, $\Delta v_{E,L}$ is always positive. Comparing
this formulation to 36, an additional term that considers the difference
between the actual $TTC_{E,L}^{-1}$ and the threshold value is introduced.
Since in this case this difference is always positive, the higher the
difference, the higher $\dot{R}_{mod}$. So, not only a smoother deceleration
is guaranteed, but it is also possible to mitigate the risk of collision.

When the controlled vehicle has only one vehicle ahead, the value for
$TTC_{E,L}^{-1}$ does not influence the controller. In this case, the data
transmitted to the 5G network by the Preceding car are compared to the one
measured by the radar sensor. In particular, the relative speed given as input
to the controller is the highest in absolute value comparing the one observed
by the radar and the one received through V2V communication, so that the worst
case scenario is considered by the algorithm.

In addition to this, it is important to notice that the potential grip is
still unknown in initial conditions because state-of-the-art Cyber Tyres can
provide the value of the estimated potential adherence during a braking
manoeuvre. At the beginning, the initial value of potential grip is assumed as
a reference value (like in commercial systems), while it is updated by the
estimated value by the system after the deceleration reached a certain
threshold. In this work it is supposed that Cyber Tyre system has already
calculated the potential grip in a preceding braking manoeuvre, so that during
the simulation the value for asphalt adherence is already estimated.

The value for potential grip is used to change the limit values
$a_{ego,min}^{des}$ and $a_{ego,max}^{des}$ for the desired acceleration or
deceleration. Their values are:

$\begin{cases}a_{min}^{des}=max(-5;-\mu g)m/s^{2}\\\ a_{max}^{des}=min(2;\mu
g)m/s^{2}\end{cases}$

This is done in order not to overcome the threshold related to the maximum
allowed longitudinal acceleration and deceleration. The vehicle can thus avoid
possible tyres forces saturation.

Moreover, the estimated value for grip is used to modify the time gap that the
controlled vehicle uses to regulate its distance from the preceding one. In
fact, in case of wet or icy road, the braking capabilities of the system
cannot be enough to avoid a collision, so that the inter-vehicle distance must
be increased. Therefore, the reference $RH$ is compared with a braking
critical distance $d_{brak}$ [59], expressed by 38:

$d_{brak}=f(\mu)\left(\frac{v_{ego}^{2}-(v_{ego}+\dot{R}_{mod})^{2}}{2|a_{ego,min}^{des}|}\right)-\tau_{s,delay}\dot{R}_{mod}$
(38)

In this expression, $\tau_{s,delay}$ is the estimated braking actuators delay,
$\dot{R}_{mod}$ is the relative speed with the modifications due to the V2V
communication and $f(\mu)$ is a piece-wise linear function used for the
friction scaling [60]. The latter is is shown in and it is described as:

$\begin{cases}{f(\mu)=}f(\mu_{min})&if\mu\leq\mu_{min}\\\
f(\mu_{min})+\frac{f(\mu_{norm})-f(\mu_{min})}{\mu_{norm}-\mu_{min}}(\mu-\mu_{min})&if\mu_{min}<\mu<\mu_{norm}\\\
f(\mu_{norm})&if\mu\geq\mu_{norm}\end{cases}$ (39)

where $\mu_{min}=0.2$ is the smallest friction coefficient to be considered
and $\mu_{norm}=0.9$. In particular, $f(\mu_{norm})$ is set to unity and
$f(\mu_{min})$ is set to $\frac{\mu_{norm}}{\mu_{min}}=4.5$ [59]. So, if
friction decreases, the scaling factor $f(\mu)$ increases and consequently
also $d_{brak}$.

Figure 12: Friction scaling function

The controller then compares the computed $d_{brak}$ with the $RH$ given by
the spacing policy. The modified value for the time gap $h_{mod}$ is:

$\displaystyle h_{mod}=$ $\displaystyle h$ if $d_{brak}<RH$ (40)
$\displaystyle h_{mod}=$ $\displaystyle\frac{d_{brak}-d_{min}}{v_{ego}}$ if
$d_{brak}>RH$ (41)

where $d_{min}=2m$ [8] is the inter-vehicle distance at rest. The described
procedure can be summed up in the representation in 13.

Figure 13: Representation of the modification to time gap $h$

Another possible way to take advantage of the information of potential grip is
modify the minimum distance in the constant time gap policy $d_{min}$. In
particular, in those situations where $d_{brak}>d_{min}$, the minimum distance
could be substituted by the braking critical distance so that safety can still
be guaranteed. However, such a modification can compromise the benefit of
using a Constant Time Gap (CTG) policy, which is string stable. In fact, the
term $d_{brak}$ is not linearly dependent on the $v_{ego}$ and it is function
also of $\mu$ and $\dot{R}_{mod}$. Therefore, this alternative use of the
information of potential grip is only mentioned but it will not be discussed
later in this work.

##  4 Scenarios Description

The scenarios considered for systems performance evaluation and comparison are
now briefly described.

Simulations will be divided into a ”Vehicle Approaching and Sudden Braking
Manoeuvre” and an ”Oscillatory Movement of the Leading Vehicle”. When dealing
with connected systems, the information flow topology chosen refers to Two
Predecessors Following Topology.

###  4.1 Vehicle Approaching and Sudden Braking Manoeuvre

This kind of braking manoeuvre is a typical situation on a highway [50]. The
use of this particular stressful scenario is widespread in literature both in
the ACC and the CACC field: as an example, Martinez at al. [18] and Nilsson
[61] made high use of this type of scenario in testing the ACC system, while
Rajamani et al. [50] uses the hard braking scenario to test string stability
in a semi-autonomous adaptive cruise control system as outlined in
LABEL:Cap2_CACC_Classic_control_systems. In many other works heavy braking
manoeuvre are included in more complex speed/acceleration profiles in order to
test the robustness of CACC systems [62, 63, 64]. Starting from these
considerations, the developed scenario is characterized by three vehicles:

  * 1.

Leading vehicle with imposed motion;

  * 2.

Preceding vehicle with the selected control logic;

  * 3.

Ego vehicle with the selected control logic.

Figure 14: Vehicles set-up

In 14 the spacial order of the vehicles is reported: in configuration A only
radar is present, while in configuration B also 5G communication and Pirelli
Cyber Tyre are available. It is important to specify that both the Ego and the
Preceding vehicle are equipped with the same control logic.

The three vehicles start at a initial distance of $140m$, so that the vehicles
can exhibit the behaviour in Cruise Control. The leading vehicle drives at a
constant speed of $90km/h$ while the following vehicles (the Ego and the
Preceding vehicle) reach the user set speed of $v_{user}=100km/h$. After the
vehicle in front has been detected, the Preceding and Ego vehicles perform a
transitional manoeuvre until the inter-vehicle distance with the vehicle in
front corresponds to the one established by the spacing policy. Just before
$t_{braking}=150s$, all the vehicles are in steady-state following condition.
At that exact instant, the leading vehicle performs a sudden braking, from
$90km/h$ to $30km/h$ with a deceleration of $-4.5m/s^{2}$. It is worth noting
that hard braking scenario is also a suitable situation to highlight the
limitation of the commercial ACC system as Nilsson [61] points out in his
work. Furthermore, the scenario will be analyzed varying the friction
coefficient of the road in order to highlight the action of the Cyber Tyre. It
is assumed that for dry surface $\mu_{dry}=0.8$ and for wet surface
$\mu_{wet}=0.5$ [65]. Concerning Cyber Tyre, it is supposed that the potential
grip estimation system has already evaluated the asphalt adherence in a
preceding braking manoeuvre, so that during the simulation the value for
potential grip is already estimated.

###  4.2 Oscillatory Movement of the Leading Vehicle

The goal of this scenario is the study of string stability of a line of
vehicles, together with the evaluation of ride comfort in terms of
acceleration amplitude. An oscillatory movement (sine wave) is imposed to the
leading vehicle in order to mimic traffic oscillations often present on
highways. Many authors in their works perform string stability experiments in
scenarios with roughly regular oscillations of the leading vehicle in real
traffic conditions [42, 66, 43]. According to [67], traffic oscillations can
be caused by a series of reasons, among them lane-changing and car-following
behaviour, and they are characterized by the following parameters:

  * 1.

Amplitude (Ampl): Zielke at al. [67] define the amplitude of a traffic
oscillation as one-half the difference between the maximum observed speed and
the minimum observed speed: $\frac{1}{2}(v_{max}-v_{min)}$

  * 2.

Period (T): regular oscillations can be assumed in this scenario as Schonhof
et al. [68] demonstrate in their work.

For the work purpose, an 8 vehicles string is taken into account, as it has
been used in literature to evaluate performances of controlled vehicles [69,
14]. All the vehicles are equipped with the same control logic.

The characteristics of the oscillatory scenarios are shown in Table 2:

Scenario Name | Velocity Mean Value | Amplitude | Sine Wave Period
---|---|---|---
Oscillatory 1 | $80km/h$ | $Ampl=\pm 4km/h$ | $T=40s$
Oscillatory 2 | $80km/h$ | $Ampl=\pm 4km/h$ | $T=20s$
Table 2: Oscillatory scenarios

The Oscillatory 1 scenario is the base case. The developed system is tested in
a more severe situation with respect to the typical traffic scenario, which
are characterized by a period around $3-4min$ [67]. Finally, the amplitude of
velocity oscillations is taken from the work of Zheng et al.[70].

The Oscillatory 2 scenario has the same amplitude of the base case but with a
halved sine wave period.

The road friction is not a relevant parameter in this kind of scenario, so it
is assumed to be in dry surface conditions where $\mu_{dry}=0.8$.

##  5 IPG CarMaker/Simulink Implementation and Vehicle Setup

CarMaker is a simulation tool developed by IPG Automotive, which allows to
recreate realistic scenarios in a virtual environment. This software includes
a complete model environment comprising an intelligent driver model, a
modifiable vehicle model and flexible models for roads and traffic. The
software can perform virtual testing of light-duty vehicles with the
possibility of recreating every type of road and traffic by means of a Virtual
Vehicle Environment (VVE). CarMaker provides a user-friendly Simulink
interface through the option CarMaker for Simulink. This latter is a complete
integration of CarMaker vehicle dynamics simulation software into the
MathWorks modeling and simulation environment MATLAB/Simulink [71].

The vehicle selected for the scenarios simulations is a 2012 Volkswagen New
Beetle, as shown in 15. Furthermore, it is important to consider that:

  * 1.

The vehicle is loaded with an additional weight of $160kg$ (2 passengers,
$80kg$ each) in order to simulate a more realistic situation;

  * 2.

The vehicle is provided with an automatic transmission: the ACC (and thus the
proposed CACC systems) works in a wide range of velocities, thus requiring
gears changing [11].

The vehicle is equipped with the following sensors:

  * 1.

Radar sensor

  * 2.

Inertial sensor

  * 3.

GPS sensor

  * 4.

Cyber Tyres (only in case of Connected systems)

While Radar, Inertial and GPS sensors are modelled in CarMaker, a model for
Cyber Tyres has to be developed.

Figure 15: Sensor set-up: the green, the red and the blue dot represent
respectively the radar, the GPS and the inertial sensor

As concerning 5G communication, the information coming from the preceding and
the vehicle in front of the preceding one is transmitted via 5G network. The
main parameters considered in this work regarding this communication
technology are the following:

  * 1.

Average Latency: $\bar{l}ms$

  * 2.

Minimum Latency: $l_{min}ms$

  * 3.

Maximum Latency: $l_{max}ms$

  * 4.

Latency standard Deviation: $l_{std,dev}ms$

As every kind of communication, also 5G suffers from a certain package loss.
The estimated percentage of package loss will be addressed as $PL$ due to
confidentiality reasons. In order to model this very small error, the
following strategy is followed:

  * 1.

A MATLAB function is created in which a random number N is generated;

  * 2.

The number N is compared with a threshold value fixed to $1-PL$;

  * 3.

If N is greater than the threshold value, the delay due to package loss will
be added to the Latency delay. Otherwise, the delay of the 5G communication
will be made up only by the latency of the signal.

##  6 Virtual Testing Results

The Commercial ACC and the Connected ACC systems are virtually tested in order
to highlight the advantages of connectivity and the use of smart sensors.

###  6.1 Vehicle Approaching and Sudden Braking Manoeuvres

The comparisons shown in this section refer to the scenario described in 4.1.
It is important to note that the commercial ACC system with $h=0.6s$ cannot be
implemented in a real scenario since it is characterized by string
instability, while the connected system have shown to achieve string stability
also with $h=0.6s$.

Therefore, a ”Commercially available candidate” comparison is carried out in
which the control logics adopt the time gap required for string stability
requirements. Thus, the developed control logics are compared considering
systems that can be actually implemented in commercial applications, which
results in using a time gap $h=1.1s$ for the ACC system and $h=0.6s$ for the
Connected one. For each control logic, the comparison concerns the following
profiles:

  * 1.

Acceleration profile of the Ego vehicle;

  * 2.

Velocity profile of the Ego vehicle;

  * 3.

Relative distance profile between Ego and Preceding vehicles;

  * 4.

Spacing error profile between Ego and Preceding vehicles;

The purpose of these tests is to show how safely the developed control logics
can operate. Therefore, the plots for relative distance and $TTC^{-1}$ are
suitable to asses the improved safety features of the connected systems with
respect to the non-connected ones. At the same time, concerning the level of
comfort during the braking manoeuvre, the comparisons for the acceleration,
velocity for the different control logics are provided.

####  6.1.1 Normal Ground Friction

The tests presented were conducted considering a normal ground friction
coefficient set to $\mu_{dry}=0.8$.

Figure 16: Comparison of the Acceleration profiles of the Ego vehicle for the
different control logics

From 16 a comment on the operation of the different logics can be made.

As expected, the commercial ACC shows a delay, in terms of braking manoeuvre,
compared to the connected system. Hence, the CACC system is the one
characterized by the highest anticipation of the braking action since it
receives the information of real-time acceleration from the vehicles ahead.

It is important to note that the commercial ACC has to be designed with a
higher time gap to have a stable behaviour (for example, $h=1.1s$, as
previously done in this work for commercial ACC). Therefore, its delayed
response to the breaking manoeuvre and its lower traffic flow capacity is a
consequence of this choice.

Figure 17: Comparison of the Velocity profiles of the Ego vehicle for the
different control logics

As a result of the previous considerations, the velocity profiles in 17 show
once again that the connected systems are able to anticipate the braking
manoeuvre.

Figure 18: Comparison of the Relative Distances of the Ego vehicle with
respect to the Preceding vehicle for the different control logics

It can be seen how the information transmitted by other vehicles to the Ego
vehicle is used to guarantee a safer behaviour. Looking at 18, advantages that
connected systems can bring are well evident: not only safety is guaranteed,
but it is also possible to achieve a higher traffic flow capacity since the
relative distances are reduced, thus reducing traffic congestion.

Figure 19: Comparison of the Spacing Errors of the Ego vehicle with respect to
the Preceding vehicle for different control logics

By looking at 19, it is evident how during braking maneuvers the ACC system
has a negative spacing error, which means a reduced relative distance between.
Therefore, the ACC system cannot guarantee safe operation due to the
significantly low value of the spacing error during the braking manoeuvre. For
the CACC system, it is important to note that the Ego vehicle keeps a positive
spacing error.

####  6.1.2 Low Road Friction

The tests presented in these sections were conducted considering a low ground
friction coefficient set to $\mu_{wet}=0.5$.

Figure 20: Comparison of the Acceleration profiles of the Ego vehicle for the
different control logics

Comparing the results shown in 20 and 16, it is possible to highlight one of
the limits of the commercial ACC system. The system is not able to modify the
behaviour of the vehicle in case of low asphalt adherence because it is not
provided with a system that can estimate potential grip. This is clearly not
an ideal and safe situation, also by looking at the maximum value of
deceleration of the ACC system: almost the entire grip is used in the
longitudinal direction thus limiting possible steering actions. Hence, in the
case the driver wants to steer in order to avoid a collision, the steering
capability can be lower than the case with normal ground friction.
In 20, the delayed action of the ACC with $h=1.1s$ is visible. However, thanks
to a higher time gap value, in this case the system is able to perform a
braking maneuver with a comparable level of deceleration with respect to the
proposed novel control logics.

Figure 21: Comparison of the Velocity profiles of the Ego vehicle for the
different control logics

As a result of the acceleration profiles, the velocity profiles in 21 show
that the connected systems are able to anticipate the braking manoeuvre.

Figure 22: Comparison of the Relative Distances of the Ego vehicle with
respect to the Preceding vehicle for the different control logics

For the relative distances, similar comments can be made with respect to the
scenario with normal friction. Looking at 22, the comparison between the novel
system and the commercial one with $h=1.1s$ shows similar minimum values of
relative distance, but a higher traffic flow capacity of connected system with
respect to the non-connected one.

Figure 23: Comparison of the Spacing Errors of the Ego vehicle with respect to
the Preceding vehicle for different control logics

From 23, it can be inferred how the CACC systems promotes safety, allowing the
Ego vehicle to keep a positive spacing error with respect to the Preceding
vehicle.

In conclusion, the considerations made for the relative distance between
vehicles can be further examined considering the traffic flow capacity that is
achievable using the different systems. In fact, the Commercial ACC has to
adopt a time gap $h=1.1s$, while for the connected system, the time gap can be
decreased to $h=0.6s$. In fact, as already seen, the 5G communication allows
shorter inter-vehicles distances without compromising the level of safety.
Therefore, it is possible to have a higher traffic flow capacity then with
non-connected system, whose time gap has to be kept higher in order to
guarantee string stability. In order to quantitatively asses this advantage,
the expression for traffic flow capacity $TFC$ introduced in LABEL:trafficflow
is used. Its expression is shown in 42:

$TFC=\frac{3600v}{L_{v}+L_{c}}\hskip 11.38092pt[vehicles/lane/hours]$ (42)

where:

  * 1.

$L_{v}$ is the distance given by the selected control strategy in $[m]$;

  * 2.

$L_{c}$ is the car length in $[m]$;

  * 3.

$v$ is the platoon speed in $[m/s]$.

The results for different speed of the leading vehicle are visible in 24:

Figure 24: Traffic Flow Capacity for different time gaps

The assumptions made for these curves is that the leading vehicles keeps a
constant speed. However, in real scenarios there could also be oscillations.
As a result, for real situations the values for traffic flow could be also
smaller. Nevertheless, it is possible to notice how connected systems can
increase considerably the traffic flow due to the lower lower minimum time gap
required in order to guarantee safety and string stability.

###  6.2 Oscillatory Movement of the Leading Vehicle

The comparisons described in this section refer to the scenario described in
”Scenario Description”. The aim of this section is to show the major
differences between the control logics developed in this particular scenario.
The following configurations of the systems will be compared:

  * 1.

Commercial ACC system with $h=1.1s$;

  * 2.

Connected ACC with Add-Ons with $h=0.6s$;

As already said in 4.2, two different scenarios are presented. For each of
them, the comparison of the control logics involves the following parameters:

  * 1.

Ego vehicle acceleration profile;

  * 2.

Ego vehicle velocity profile;

  * 3.

Relative distance between the Ego vehicle and the vehicle ahead.

The plots that are shown refer to the behaviour of vehicles after the
approaching manoeuvre to the vehicle ahead, so for simulation time between
$t=130s$ and $t=210s$. Concerning driving comfort, comments are provided based
on the behaviour of the acceleration and velocity profiles. The safety
features of the control logics can be inferred observing the relative
distances and, in particular, their minimum value. Further comments to verify
the strength of the developed control logics in term of comfort can be made
considering input for Gas and Brake pedals that the Ego vehicle (i.e., the
last vehicle of the string of eight cars) has to apply to follow the desired
motion. The virtual testing for the Oscillatory Scenario has been performed
assuming that the vehicle model of the vehicles is represented by a First
Order Filter, which do not provide Gas and Brake pedals command. In order to
show the results in term of Gas and Brake pedals, the simulations for the
three oscillatory scenarios have been repeated considering that:

  * 1.

For the first seven vehicles, the vehicle model is represented by a First
Order Filter;

  * 2.

For the eight vehicles (i.e. the Ego vehicle), the vehicle model is
represented by the CarMaker model.

However, before showing the results using the CarMaker model, the following
plots are presented:

  1. 1.

Comparison of Acceleration profiles of the Ego vehicle using firstly the First
Order Filter and then the CarMaker model;

  2. 2.

Comparison of Relative Distance profiles of the Ego vehicle from the vehicle
ahead using firstly the First Order Filter and then the CarMaker model.

Each of these plots is presented for each of the control logics used for the
comparison (i.e., ACC, CACC with Add-Ons). The reason why also these plots are
shown is that, in this way, it is possible to demonstrate that the comments
made so far concerning string stability are not influenced by the use of the
First Order Filter instead of the CarMaker model. In fact, the CarMaker model
has proven to present a faster response in case of a step input and also to be
quicker in following an oscillatory input. However, a verification concerning
string stability was left open and it is dealt with in this chapter.

####  6.2.1 Oscillatory Scenario No.1

As described in 4.2, the leading vehicle has the following imposed velocity
profile:

  * 1.

$v_{lead}=80\pm 4km/h$

  * 2.

$T=40s$

The advantages of using a connected system is represented by the possibility
of increasing the traffic flow capacity still guaranteeing a better level of
comfort and safety with respect to the Commercial ACC, as the acceleration
profiles in 25 show. The information coming from the ”Leading” vehicle of the
selected Information Flow Topology (IFT) are used to damp the acceleration
oscillations. In fact, if an ACC system with a time gap $h=0.6s$ is used, the
string instability associated to the selection of a too low time gap would
make the oscillations amplify even more. The most remarkable improvement can
be proved by the 10% reduction of the amplitude of acceleration oscillations
with respect to the Commercial ACC. Concerning comfort, the values for RMS of
the last car of the vehicles string reflect the described situation and are
reported in 3:

Control Logic |  Time Gap [$s$] |  RMS of Acceleration [$m/s^{2}$]
---|---|---
Commercial ACC | $h=1.1s$ | $0.121$
Connected ACC with Add-Ons | $h=0.6s$ | $0.101$
Table 3: RMS of Acceleration Profile for Last Vehicle of the String

Observing the behaviour of the relative distances profile in
LABEL:reldistcomparisonoscill1, it is visible how the ACC system ensures
higher inter-vehicle distances, at the cost of a reduced traffic capacity. The
latter can be increased using connected systems, even if shorter inter-vehicle
distances might be a problem in term of safety. However, as already proved
before, connected systems have produced better results then a commercial ACC
with the same time gap.

Figure 25: Comparison of the Acceleration profile of the Ego vehicle for
different control logics Figure 26: Comparison of the Velocity profile of the
Ego vehicle for different control logics

Comparing the graphs shown in 27, for each control logic the deceleration
phase is accomplished simply reducing the Gas input and not operating on the
Brake. Both the connected systems are able to reduce the Gas pedal compared to
the commercial ACC. Furthermore, it is visible how the smoother profile can
help the vehicle in guaranteeing a more comfortable drive. Finally, these
consideration can intuitively have an impact on the fuel consumption:
considering the curves in 27 and the previous work of Li et al. [10],
reduction on accelerations (directly related to a lower gas pedal percentage)
have a similar effect of reduction on fuel consumption.

Figure 27: Gas and Brake Pedal for Ego Vehicle for different control logics

####  6.2.2 Oscillatory Scenario No.2

As described in 4.2, the leading vehicle has the following imposed velocity
profile:

  * 1.

$v_{lead}=80\pm 4km/h$

  * 2.

$T=20s$

As shown in 28, it is possible to notice that the amplitude of the
oscillations considering the CACC with Add-Ons approach is reduced up to
almost 20% with respect to the ACC systems. This great difference can be
attributed mainly to the fact that the information coming from the vehicles
ahead in the line are used to reduce the oscillations of acceleration.

Considering the acceleration profile of Commercial ACC instead, in order to
avoid risk of collisions, the time gap must be increased and therefore traffic
capacity is decreased. Concerning comfort, the advantages of a connected ACC
are evident, also looking at the values for RMS of the last car of the vehicle
string reported in 4

Control Logic | Time Gap [$s$] | RMS of Acceleration [$m/s^{2}$]
---|---|---
Commercial ACC | $h=1.1s$ | $0.248$
Connected ACC with Add-Ons | $h=0.6s$ | $0.170$
Table 4: RMS of Acceleration Profile for Last Vehicle of the String

As a result of this, also the velocity profiles shown in 29 show that
connected system presents a reduced amplitude and confirms that the system
based on the connected controller is able follow the string of vehicles with a
velocity oscillation $1km/h$ less wide then the system with Add-Ons.

Figure 28: Comparison of the Acceleration profile of the Ego vehicle for
different control logics Figure 29: Comparison of the Velocity profile of the
Ego vehicle for different control logics

Considering 31, it is evident how the connected system reduce amplitude of
deceleration with respect to a commercial ACC. Furthermore, it is important to
remark that also the Gas pedal presents a lower amplitude for the connected
systems with respect to a Commercial ACC. The CACC with Add-ons shows a 15%
reduction of the Gas input with respect of the commercial system and a 5%
reduction the Brake pedal input .
As a result of this, the driving results more comfortable since no unnecessary
acceleration and deceleration are needed. Improvements are expected also
considering the fuel consumption.

Figure 30: Gas and Brake Pedal for Ego Vehicle for ACC system Figure 31: Gas
and Brake Pedal for Ego Vehicle for CACC with Add-Ons system

##  7 Conclusion

In conclusion, as defined in 1, the aim of the work was showing how
connectivity between vehicles and infrastructures can increment road safety
and comfort, as well as increase traffic capacity if properly integrated in
ADAS algorithm development The targets can be summarized by the following
tasks:

  * 1.

Show the convenience of connectivity by focusing on the use of fast and
reliable 5G communication for Connected ACC systems;

  * 2.

Investigate the benefits of using information about potential grip from
Pirelli Cyber Tyre in the design of connected systems;

After an accurate literature review about ACC and CACC logics in order to
highlight advantages of connectivity applied to commercial ACC systems, at
first a commercial ACC based on the most common techniques adopted in
literature has been developed. Then, a novel control approach was proposed.

Thenovel approach was developed by considering the assumption of maintaining
an architecture as close as possible to the one of the partner companies
involved in the project. In this way, the resulting system is easier and
faster to be implemented on a commercial vehicle (since the main architecture
and control boards are maintained) and with limited costs. Therefore, the CACC
with Add-Ons system was designed.
Considering the results obtained in the virtual testing, it can be stated that
the use of 5G communication allows to overcome the limits of the commercial
ACC systems. In particular, the braking manoeuvre is anticipated with respect
to the movement of the vehicles ahead and a higher inter-vehicle distance
during the manoeuvre is ensured. Thus, it is possible to increase the traffic
flow capacity, guaranteeing string stability. At the same time, the use of
Cyber Tyre permits a reduction of deceleration value in case of low adherence.

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