Detecting Malicious Behavior In Cooperative Autonomous RC Cars

Master thesis (2020)

Authors

G. Tombakaitė Mechanical Engineering

Contributors

Riccardo M.G. Ferrari Team Jan-Willem van Wingerden - Mechanical, Maritime and Materials Engineering (supervisor 1)
T. Keijzer Team Jan-Willem van Wingerden - Mechanical, Maritime and Materials Engineering (supervisor 1)
S. Wahls Team Raf Van de Plas - Mechanical, Maritime and Materials Engineering (supervisor 2)
Faculty
Mechanical Engineering
Copyright: © 2020 Gabriele Tombakaitė
Platooning CACC Fault detection
More Info
expand_more

Published Date

20-10-2020

Language

English

Reuse Rights

Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.

Faculty

Mechanical Engineering

Abstract

Sensors are used all around us in various industries, for instance: agricultural, medical,aerospace and automotive. It is important for these industries to have reliable sensor data because the functionality of technologies depends on it. In this work, the industry of inter-est is automotive, specifically in the field of Cooperative Adaptive Cruise Control (CACC). The control of vehicles depends on measurement data from radars, which are carried by each vehicle in a platoon. If these measurements are faulty, it could affect CACC and cause a crash. This work models the Radio Control (RC) vehicle, implements CACC and aims to identify such faults in the radar measurement data before they could impact the behavior of the platoon. The results are obtained in simulation, comprising the mathematical model of the vehicles, the implemented CACC controller and a virtual radar exposed to 4 different faults, with which the chosen method for fault detection is evaluated. The results of the CACC operating in ideal conditions and with faulty measurement data are depicted. The work ends with analysing the results and concluding, whether the chosen method is capable of identifying the faulty measurement data.