Prototype model car design for vehicle platooning [614233]

Prototype model car design for vehicle platooning
Florin C. Braescu and Constantin F. Caruntu
Department of Automatic Control and Applied Informa tics, Gheorghe Asachi Technical University of Iasi, Romania
Email: [anonimizat]

Abstract- The paper presents a new prototype model car design
devoted to provide a better study and research of t he control
techniques and navigation algorithms employed withi n vehicle
platooning. The prototype car has a modular design and is
equipped with various sensors. All the car parts we re designed
in a 3D CAD application and 3D printed. The embedde d
application designed to control the car behavior is developed in
compliance with the FreeRTOS real-time operating sy stem. The
embedded system was designed to be highly customiza ble and is
composed of a main board with a dsPIC33 microcontro ller and
an optional secondary one, namely STM32F3DISCOVERY
board. The latest can implement a more powerful pla toon
controller whilst freeing the main board of this ta sk.

I. INTRODUCTION
“Road accidents remain a major cause of death and injury
in Europe in spite of considerable improvements ove r the past
decade. The European Commission recognizes the impo rtant
role which intelligent transport systems can play i n making
Europe’s roads safer still.” [1]. Moreover, road tr ansport
amounts to roughly 27% of the energy consumption of the
European Union [2]. The increased traffic flow on b oth
existing highways and city roads has many disadvant ages,
e.g., increased risk of accidents, higher fuel cons umption,
increased pollution and wear of vehicle mechanical parts,
driving stress and passenger discomfort [3], [4]. T he
platooning of vehicles could be the solution to the se stringent
problems by enforcing them to follow each other and to
maintain a safe distance between them [5]-[7]. More over, it
has been already proved in [7], [8] that by includi ng
communication between vehicles the performances of the
resulting platoon are much greater than those obtai ned
without communication between vehicles. Yet, the
advantages brought by this solution depend on ensur ing the
string stability of the vehicle platoon [9].
The general representation of a vehicle platoon of n
vehicles is illustrated in Fig. 1 in which the vehi cles ( Vi) with
control agents ( Ai) exchange information and negotiate using
vehicle-to-vehicle (V2V) communications through a w ireless
network ( ) to improve their performances and ensure the
string stability of the whole platoon. Note that th eir absolute
position is detected using a global positioning sys tem (GPS) sensor ( ) mounted on the vehicle and the relative d istance d
between vehicle Vi and vehicle Vi-1 is determined using a
radar device ( ). The idea of platooning means that each
vehicle in the platoon has to control its velocity and the
relative distance to the vehicle in front ( d) by negotiating with
the other control agents in the platoon in order to improve
road safety, to reduce fuel consumption, to increas e road
capacity and to decrease the emissions.
Researchers approached vehicle platooning by creati ng a
safe demonstration platform; hence the study of the
platooning problem on model-scale vehicles became
desirable, as they can be driven indoors. Low-cost
implementations of vehicle platoons can make the fu ture of
platooning vehicles on the highway more efficient a nd cost-
effective. Experiments are usually performed on two or more
electrical microcontroller driven cars in a control led
laboratory environment. A low-cost, but efficient
implementation of a platooning system was designed and
implemented on two cars, by using PIC18 microcontro llers
and various sensor technologies in [10]. The longit udinal
control was realized using the distance information available
from infrared and ultrasonic sensors. The communica tion
system was based on a 434MHz RF radio module. Anoth er
approach is presented in [11], where 3 robotic vehi cles known
as PIE (Platform for Intelligent Embedded Systems) formed a
platoon. The base-to-vehicle and vehicle-to-vehicle
communications were based on nRF24L01 module. The p ath
control and platoon control were based on the infor mation
received from a video camera that simulated the fun ctionality
of the Global Positioning System (GPS). A more comp lex
demonstration platform, composed by a Carrera racet rack and
10 slot cars equipped with onboard 32-bit microcont roller-
based control systems, is presented in [12]. Additi onal
components were added to the slot cars, namely infr ared
range sensors and wireless communication modules (x Bee
802.15.4 OEM radio frequency (RF) module). Given th e fact
that a Carrera racetrack is a slot system using car s linked to
track lanes, the path control was not implemented.
Keskikangas and Sällberg consider the implementatio n and
evaluation of the longitudinal control of a model-s cale vehicle
platoon in which Model Predictive Control (MPC) is used

Fig. 1. Cooperative vehicles in a platoon. 978-1-5090-4489-4/17/$31.00 ©2017 IEEE 953

[13]. Modified model vehicles from Tamiya were cons idered.
For these vehicles, a Hall sensor measures the gear box speed,
a video camera is used to determine the distance an d angle to
a vehicle in front, an ultra-sonic sensor senses if there is any
vehicle next to it and an optical sensor array is u sed in order
to have the ability of following a line.
This paper presents a flexible, multi-features prot otype
model car designed for vehicle platooning research. In
comparison with the presented approaches, the propo sed
solution offers a wide selection of sensors that al low lateral
and longitudinal control of the platooning vehicles , radio
communication and an easy expansion of the car’s em bedded
system. The car allows activity recording in specif ic log files,
making offline analysis very easy. The embedded sys tem is
customizable and is composed of a main board, in ch arge
with all the sensors and modules and a second (opti onal)
board that can be used for employing a high computa tional
platoon controller. The numbers of cars composing t he
platoon can vary and it is not restricted. Within o ur project, a
platoon of 10 cars is targeted.
The paper is organized as follows. Section II prese nts the
considered initial requirements for the prototype m odel car
and the hardware design, including chassis, sensors and the
embedded system architecture. Section III describes the most
important facilities of the FreeRTOS real-time oper ating
system that was employed for the embedded applicati on.
Details regarding the embedded application and seve ral
preliminary experimental results that demonstrate t he
usability of the suggested approach are discussed i n section
IV. The last section is devoted to conclusions.
II. HARDWARE DESIGN
One of the main initial requirements in our project was to
be able to do experiments with up to 10 cars in ord er to better
observe the platoon behavior. We wanted that all th e cars to
be identical, to be car-like, to be able to follow a line, to have
space for all the necessary sensors, to be able to record
activity in specific log files on a SD card and to employ radio
frequency communication modules. A 3D side view of the
prototype model car in graphically illustrated in F ig. 2.
A. The model car architecture
Instead of buying ready off the shelf model cars an d
customize them in order to accommodate the modules, the
chosen solution was to design and customize the pro totype
model car in a 3D CAD application and to 3D print a ll the
parts.
The prototype model car was designed to be car-like ;
moreover, the steering acts upon front wheels whils t the drive
motor is placed in the back. The DC motor is equipp ed with
an encoder. The steering respects the Ackermann ste ering
geometry. It is driven by a servomotor. A 3D back v iew of
the prototype model car, illustrating how the DC mo tor drives
the back wheels, is presented in Fig. 3.
Instead of using a video camera or a GPS module to
implement the path control, the platoon vehicles ac t like line followers. A dark line on a lighter surface indicat es the path
that the vehicles in the platoon have to follow. Ea ch car is
equipped with an infrared line sensor that sends th e line
position information to the microcontroller. This i nformation
is used by the path controller in order to command the
servomotor and to achieve the desired trajectory.
The model car makes use of two distance sensors. Th e first
one is a range infrared sensor that provides the in formation
regarding the distance to the car in front. This in formation is
used by the distance controller to command the spee d
controller. The second distance sensor is a close p roximity
infrared sensor that has the role of a wireless bum per. It
detects if the front car is within its range and tr iggers an
interrupt, so the vehicle will stop immediately in order to
avoid collision. Both infrared distance sensors are mounted
on a sliding support, so the minimal collision avoi dance
distance can be easily setup. A 3D front view of th e prototype
model car, presenting the line sensor, the infrared distance
sensors support as well as the steering geometry is illustrated
in Fig. 4.
Each car has an accelerometer and a compass sensor that
provide useful information about the car’s orientat ion and the
static and dynamic acceleration that the car is sub ject to. The
accelerometer allows the embedded system to underst and the
surroundings and to implement better speed control. It can be
used, combined with the DC motor encoder, to employ soft

Fig. 3. 3D back view of the prototype model car.

Fig. 2. 3D side view of the prototype model car.
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start and stop algorithms as well as other useful
functionalities like car handling detection.
The platoon cars are able to exchange data wireless ly by
using a RF module. Each RF module is able to act as a
transmitter and as a receiver, so different V2V
communication architectures and protocols can be te sted and
employed.
One of the most important car’s design requirements was to
be able to save all the activity data in specific l ocal log files,
instead of sending them to a base computer by RF
communication. In this way, a larger bandwidth is a vailable
for RF communications between vehicles and the log files
can be loaded later on a PC and analyzed. As such, a micro
SD card was used in order to fulfill these requirem ents.
A 2 cells 1300mA Lithium-ion Polymer (LiPo)
rechargeable battery powers the model car. This cho ice was
motivated by the LiPO battery characteristics: low weight,
high current output and a low discharge rate, which is about
5% per month. Fig. 5 roughly illustrates all the mo dules
composing the prototype model car designed for vehi cle
platooning and their position as well.
B. PCB design and controller implementation
The embedded system was designed to be modular and
very customizable. The main board was designed and
implemented to accommodate all the modules and sens ors
presented within previous subsection. The board inc ludes a
dsPIC microcontroller which is able to manage with all the
low level functionalities, namely reading informati on from
accelerometer and infrared sensors, communicating b y means
of RF module, saving data on micro SD card, impleme nting
path control and low computational power platoon co ntrol. The second board, which is optional, can be used wh en the
employed controller for platoon control requires hi gh
computational power. One of the intended goals of t his
project is to use a MPC controller for controlling the distance
to the front car. The second board has been chosen by taking
in consideration the results obtained by other rese archers after
implementing a MPC controller.
There is a spectrum of possible implementations for
embedded MPC ranging from software to complete hard ware.
While a pure software approach can be more direct, a
hardware-software co-design approach leading to a f ield
programmable gate arrays (FPGA) implementation can be an
efficient solution when performance and reduced mem ory are
sought.
Moreover, fast FPGA-based linear MPC implementation s,
with sampling periods ranging from hundreds of
microseconds to milliseconds, are described in [14] and [15].
They employed a midrange FPGA to implement the cust om
architecture by using parallelism, pipelining, and specialized
numerical formats. A successful approach of an MPC
algorithm implementation with a QP solver that make s use of
the FPGA’s computational efficiency and flexibility is
presented in [16]. It is shown in [17] that satisfa ctory control
performance at sampling rates of 1MHz and beyond ar e
achievable on devices clocked at more than 400MHz u sing
chips from Xilinx’s high-performance Virtex 6 famil y or at
more than 230MHz using devices from the low cost an d low
power Spartan 6 family.
FPGA-based MPC implementations with application to
aircraft control are presented in [18], [19] and [2 0], the
employed controllers being based on Nesterov’s fast gradient
method and on linear time varying model. The hardwa re
architecture for implementing an interior point met hod for
MPC on FPGA is approached in [21], the solution of
quadratic programs occurring in MPC at very high sp eed.
Recently, a MPC implementation [22] employs Spartan6
Nexys3 FPGA chip and solves the quadratic programing
problem by using the QPKWIK method.
As presented above, the FPGA devices allow high
sampling rates due to their high clocking frequenci es, the
biggest disadvantage being the high power consumpti on. An
embedded system should have low power consumption, so
great interest has been given to microcontroller-ba sed MPCs.

Fig. 4. 3D front view of the prototype model car.

Car front
Line sensor
Proximity sensor
Distance sensor
Servomotor Car middle
LiPo battery
PCB with:
dsPIC microcontroller
RF module
Accelerometer
Micro SD card
DC motor driver
STM32F3DISCOVERY Car back
DC motor
Motor encoder
Motor gear
Fig. 5. Modules of the prototype model car for vehi cle platooning.
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The MPC implementation requires high amount of
computing power, hence 32bit microcontrollers with
Advanced RISC Machine (ARM) architecture represente d a
starting point. [23] and [24] describe MPC approach es based
on ARM microcontroller with Cortex-M3 core running at
72MHz. The MPC implementation described in [25] emp loys
an ARM9 architecture microcontroller and the maximu m
sampling rate of 800Hz was obtained whilst the one in [26]
make use of an ARM7 architecture microcontroller an d a
sampling period of 4ms. A complete automatic way to
implement a MPC controller on embedded hardware sta rting
from a dynamic model in MATLAB at a sampling rate i n the
order of kHz is presented in [27].
Many recent researches targeted the use of MPC in
controlling unmanned aerial vehicles [28], [29], an d [30].
Many such embedded systems are complex systems, hen ce
the use of real time operating systems is often a n ecessity,
allowing an easier and flexible development.
III. SOFTWARE DESIGN
As mentioned in the previous section, the use of re al-time
operating systems is often a necessity. The propose d solution
for the prototype model car for vehicle platooning includes
FreeRTOS real-time operating system on the main boa rd as
well as on the second board. This option allowed ea sily
design and implementation of the real-time embedded
applications.
A. FreeRTOS real- time operating system facilities
FreeRTOS facilitates gaining an insightful understa nding
of how a real-time kernel works, what are the commo n
challenges encountered in real time application dev elopment
and how recommended design/programming techniques
could be applied. It is worth mentioning that FreeR TOS
illustrates the facilities that are common for a la rge variety of
real time operating systems, such as the mechanisms specific
to multitasking execution in combination with inter rupt
service routines (ISR), inter-process communication via
queued and unqueued messages and passive resource s haring
via mutex/semaphore based synchronizations [31][32] .
The concurrent processes of the embedded applicatio n are
defined as tasks, co-routines or interrupt service routines. A
task can have one of the following states: running (it is
executed), ready (it demands to be executed), block ed (it is
waiting for a temporal event) and suspended (it is not
available for scheduling). Comparing with tasks, th e ISR
processes (used for providing specific responses to interrupts)
accept only a limited range of application programm ing
interface (API) services. Their priorities (higher than the
priorities of any task) are not managed by FreeRTOS .
However, the ISR processes that call API services m ust be
assigned with a priority equal to the priority of t he real time
kernel interrupt, in order to allow a proper execut ion of
necessary system operating services. On the other h and, the
execution of tasks (preemptive or non-preemptive), is
supervised by a time-driven scheduler according to the allocated priorities. FreeRTOS real-time operating system
includes API services that allow full control over the tasks
priorities and states during application execution, like
changing the tasks priorities, blocking, suspending or even
creating/deleting the tasks.
The FreeRTOS kernel allows preemptive or non
preemptive scheduling. In the preemptive mode, the implicit
scheduling points are periodically distributed. The scheduling
algorithm is “highest priority first”. If more task s in the ready
state have the same highest priority, the round rob in
scheduling is employed. The scheduling points can a lso occur
asynchronously, for instance when a task is blocked ,
suspended or set with a higher priority than the pr iority of the
running task, when a task of higher priority is cre ated or when
an explicit rescheduling is demanded.
FreeRTOS also provides three different memory alloc ation
schemes that allow scalable RAM usage within the
application. These schemes support memory usage lim ited to
static allocations, dynamic allocation of blocks of predefined
sizes, or memory allocation with unconstrained util ization of
malloc() and free() functions.
The kernel functionalities and the API services ava ilability
are tailored to a particular application by means o f a
configuration file, FreeRTOSConfig.h . Its specific
definitions indicate the kernel type (preemptive or
cooperative), the microcontroller’s frequency, the allowed
tracing facilities, the employed inter-task communi cation and
synchronization mechanisms etc.
B. The main board embedded application
The main board embedded application is based on
FreeRTOS real-time operating system. It deals with all the
sensors and modules installed within the prototype model car
for vehicle platooning, namely the line sensor, the infrared
distance sensors, the accelerometer, the micro SD c ard, the
RF communication module, the servomotor and the DC
motor with encoder. The tasks within the FreeRTOS
embedded application are presented in Fig. 6. Line_S_Task is
the task in charge with collecting the line positio n
information from the line sensor. This information is passed
to LineFoll_Task , that implements the line following
controller and calculates the command transmitted t o the
servomotor. Obst_S_Task and Acc_S_Task are the tasks
processing the data received from close proximity i nfrared

Fig. 6. FreeRTOS based embedded application. Line_S_Tas k
Dist_S_Task
Obst_S_Task MicroSD _Task
Acc_S_Task CommRF_Task
LineFoll_Task
LongCtrl_Task FreeRTOS
Scheduler 956

sensor and accelerometer sensor respectively. The
MicroSD_Task task saves periodically the activity in specific
log files on the SD card. The distance information made
available by Dist_S_Task task is further used by the
LongCtrl_Task in order to implement the longitudinal control
of the vehicle in the platoon. The task in charge w ith V2V
communication is CommRF_Task .
The information is sent from one task to another by
messages queues and the synchronization between
Obst_S_Task task and LongCtrl_Task task is made with a
binary semaphore. In this way, all the FreeRTOS spe cific
mechanisms are employed in order to assure that no data is
corrupted and the tasks are synchronized.
IV. EXPERIMENTAL SETUP
Several experiments have been carried out to demons trate
the main functionalities of the proposed solution. The
FreeRTOS-based embedded application was targeted to a
dsPIC33FJ128MC802 microcontroller from Microchip. T he
microcontroller is composed of standard on-chip per ipherals,
including six asynchronous serial communications in terfaces
(SCI) and one ECAN module compatible with CAN 2.0 A , B.
dsPIC33FJ128MC802 is a 16bit microcontroller with a n
enhanced set of peripherals, compliant with digital signal
processing and control applications.
The dsPIC microcontroller is able to deal with all the
previously presented tasks, including the longitudi nal
controller task, if the latest does not have requir ements for
high computational power. As one of these project g oals is to
implement the longitudinal control with a MPC contr oller, the
previous condition could not be met and the use of the
STM32F3DISCOVERY board is necessary. This board has a
32bit microcontroller with 256kB of flash memory an d 48kB
of RAM memory and can run at the maximum frequency of
72MHz. The embedded system becomes more flexible if the
second board is used. The MPC controller is impleme nted on
the STM32F3DISCOVERY board that will communicate
with the main board by Serial Peripheral Interface (SPI).
Moreover, because the longitudinal controller makes use of
the information from the range infrared sensor, the sensor can
be connected to the second board directly. The RF
communication module or the micro SD card could be
connected to the main board as well, if needed. The prototype
model car for vehicle platooning equipped with the base
board is presented in Fig. 7.
The main board embedded application was designed to
comply with the timing requirements of the sensors and
modules involved. The line sensor, a QTR-8RC from P ololu,
is able to provide line information with a frequenc y of 500Hz.
The LSM303D accelerometer sensor can be interrogate d
through I2C bus at 100 kHz in standard mode and at 400 kHz
in fast mode. The infrared closed proximity sensor is a Pololu
carrier with a Sharp GP2Y0D810Z0F that has a typica l
sampling rate of almost 400 Hz. The maximum update period
of the Sharp GP2Y0A60SZLF analog distance sensor is
20ms. The periods and the priorities of the tasks running on the
FreeRTOS-based embedded application are presented i n the
Table I. They represent a starting point only, the embedded
application being customizable and depending of the
experiments requirements. Being in charge with the path
control, the Line_S_Task task and LineFoll_Task task are set
with the highest frequency and the highest priority . The tasks
in charge with longitudinal control and implementin g a
control algorithm with low computing requirements, namely
Dist_S_Task task and LongCtrl_Task task, have the 4
priority. The other tasks responsible with the othe r sensors
and modules are assigned with lower priorities and lower
frequencies. These setting will change considerably , if the
STM32F3DISCOVERY is used and a MPC controller is
employed.

TABLE I
EMBEDDED APPLICATION TASKS CHARACTERISTICS
TASK NAME T ASK
PRIORITY TASK PERIOD
(MS )
Line_S_Task 5 10
Dist_S_Task 4 25
Obst_S_Task 3 100
Acc_S_Task 2 100
Line_Foll_Task 5 10
Long_Ctrl_Task 4 25
Micro_SD_Task 1 100

V. CONCLUSION
The paper presents a new prototype model car for ve hicle
platooning. It was designed to support the developm ent of a
model predictive controller and navigation algorith ms used in
path control and longitudinal platoon control. The proposed
solution acts like a car-like line follower equippe d with an
infrared line sensor. The model car is able to avoi d collision
and to keep the distance to the car in front by mea ns of a
close proximity infrared sensor and a range infrare d sensor
respectively. A RF module is employed on each car i n order
to implement the V2V communication. The drive motor is a
Fig. 7. The prototype model car for vehicle platoon ing (with the main
board).
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DC motor equipped with encoder and the Ackerman ste ering
is based on a servomotor.
In comparison with other available nowadays model c ars
used in vehicle platooning research, the proposed s olution
makes use of an accelerometer in order to implement soft
start and stop as well as other useful facilities, like handling
detection. It is able to record the whole activity in specific log
files on a micro SD card for future analysis on a c omputer.
The presented embedded system has a modular design and
is composed by two boards, namely the main board an d the
second board. All the presented sensors can be
accommodated on the main board, but the whole embed ded
system is highly customizable. The second board, na mely
STM32F3DISCOVERY, can be employed in order to
implement a high computational MPC controller.
Future work will include the MPC implementation, as well
as the development of specific V2V communication
protocols. Moreover, navigation algorithms for late ral control
to be used within vehicle platooning can be impleme nted,
such as vehicles joining a platoon and/or leaving a platoon.

ACKNOWLEDGMENT
The work was supported by a grant of the Romanian
National Authority for Scientific Research and Inno vation,
CNCS UEFISCDI, project number PN-II-RU-TE-2014-4-
0970.
Florin C. Braescu thanks to Constantin George Panai t for
the design work and 3D printing of all car parts.
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