This paper presents CarFASE, an open-source carla-based fault and attack simulation engine that is used to test and evaluate the behavior of autonomous driving stacks in the presence of faults and attacks. Carla is a highly customizable and adaptable simulator for autonomous driving research. In this paper, we demonstrate the application of CarFASE by running fault injection experiments on OpenPilot, an open-source advanced driver assistance system designed to provide a suite of features such as lane keeping, adaptive cruise control, and forward collision warning to enhance the driving experience. A braking scenario is used to study the behavior of OpenPilot in the presence of brightness and salt&pepper faults. The results demonstrate the usefulness of the tool in evaluating the safety attributes of autonomous driving systems in a safe and controlled environment.

A remotely operated road vehicle (RORV) refers to a vehicle operated wirelessly from a remote location. In this paper, we report results from an evaluation of two safety mechanisms: safe braking and disconnection. These safety mechanisms are included in the control software for RORV developed by Roboauto, an intelligent mobility solutions provider. The safety mechanisms monitor the communication system to detect packet transmission delays, lost messages, and outages caused by naturally occurring interference as well as denial-of-service (DoS) attacks. When the delay in the communication channel exceeds certain threshold values, the safety mechanisms are to initiate control actions to reduce the vehicle speed or stop the affected vehicle safely as soon as possible. To evaluate the effectiveness of the safety mechanisms, we exposed the vehicle control software to various communication failures using a software-in-the-loop (SIL) testing environment developed specifically for this study. Our results show that the safety mechanisms behaved correctly for a vast majority of the simulated communication failures. However, in a few cases, we noted that the safety mechanisms were triggered incorrectly, either too early or too late, according to the system specification. 

A remotely operated road vehicle (RORV) refers to a vehicle operated wirelessly from a remote location. In this paper, we report results from an evaluation of two safety mechanisms: safe braking and disconnection. These safety mechanisms are included in the control software for RORV developed by Roboauto, an intelligent mobility solutions provider. The safety mechanisms monitor the communication system to detect packet transmission delays, lost messages, and outages caused by naturally occurring interference as well as denial-of-service (DoS) attacks. When the delay in the communication channel exceeds certain threshold values, the safety mechanisms are to initiate control actions to reduce the vehicle speed or stop the affected vehicle safely as soon as possible. To evaluate the effectiveness of the safety mechanisms, we exposed the vehicle control software to various communication failures using a software-in-the-loop (SIL) testing environment developed specifically for this study. Our results show that the safety mechanisms behaved correctly for a vast majority of the simulated communication failures. However, in a few cases, we noted that the safety mechanisms were triggered incorrectly, either too early or too late, according to the system specification.

This paper presents ComFASE, a communication fault and attack simulation engine. ComFASE is used to identify and evaluate potentially dangerous behaviours of interconnected automated vehicles in the presence of faults and attacks in wireless vehicular networks. ComFASE is built on top of OMNET++ (a network simulator) and integrates SUMO (a traffic simulator) and Veins (a vehicular network simulator). The tool is flexible in modelling different types of faults and attacks and can be effectively used to study the interplay between safety and cybersecurity attributes by injecting cybersecurity attacks and evaluating their safety implications. To demonstrate the tool, we present results from a series of simulation experiments, where we injected delay and denial-of-service attacks on wireless messages exchanged between vehicles in a platooning application. The results show how different variants of attacks influence the platooning system in terms of collision incidents.

In this work, we evaluate the safety of a platoon offour vehicles under jamming attacks. The platooning applicationis provided by Plexe-veins, which is a cooperative drivingframework, and the vehicles in the platoon are equipped withcooperative adaptive cruise control controllers to represent thevehicles’ behavior. The jamming attacks investigated are modeledby extending ComFASE (a Communication Fault and AttackSimulation Engine) and represent three real-world attacks,namely, destructive interference, barrage jamming, and deceptivejamming. The attacks are injected in the physical layer of theIEEE 802.11p communication protocol simulated in Veins (avehicular network simulator). To evaluate the safety implicationsof the injected attacks, the experimental results are classifiedby using the deceleration profiles and collision incidents of thevehicles. The results of our experiments show that jammingattacks on the communication can jeopardize vehicle safety,causing emergency braking and collision incidents. Moreover,we describe the impact of different attack injection parameters(such as, attack start time, attack duration and attack value) onthe behavior of the vehicles subjected to the attacks.

Embedded electronic systems used in vehicles are becoming more exposed and thus vulnerable to different types of faults and cybersecurity attacks. Examples of these systems are advanced driver assistance systems (ADAS) used in vehicles with different levels of automation. Failures in these systems could have severe consequences, such as loss of lives and environmental damages. Therefore, these systems should be thoroughly evaluated during different stages of product development. An effective way of evaluating these systems is through the injection of faults and monitoring their impacts on these systems. In this paper, we present SUFI, a simulation-based fault injector that is capable of injecting faults into ADAS features simulated in SUMO (simulation of urban mobility). Simulation-based fault injection is usually used at early stages of product development, especially when the target hardware is not yet available. Using SUFI we target car-following and lane-changing features of ADAS modelled in SUMO. The results of the fault injection experiments show the effectiveness of SUFI in revealing the weaknesses of these models when targeted by faults and attacks.

Embedded electronic systems used in vehicles are becoming more exposed and thus vulnerable to different types of faults and cybersecurity attacks. Examples of these systems are advanced driver assistance systems (ADAS) used in vehicles with different levels of automation. Failures in these systems could have severe consequences, such as loss of lives and environmental damages. Therefore, these systems should be thoroughly evaluated during different stages of product development. An effective way of evaluating these systems is through the injection of faults and monitoring their impacts on these systems. In this paper, we present SUFI, a simulation-based fault injector that is capable of injecting faults into ADAS features simulated in SUMO (simulation of urban mobility) and analyse the impact of the injected faults on the entire traffic. Simulation-based fault injection is usually used at early stages of product development, especially when the target hardware is not yet available. Using SUFI we target car-following and lane-changing features of ADAS modelled in SUMO. The results of the fault injection experiments show the effectiveness of SUFI in revealing the weaknesses of these models when targeted by faults and attacks.

Embedded electronic systems used in vehicles are becoming more exposed and thus vulnerable to different types of faults and cybersecurity attacks. Examples of these systems are advanced driver assistance systems (ADAS) used in vehicles with different levels of automation. Failures in these systems could have severe consequences, such as loss of lives and environmental damages. Therefore, these systems should be thoroughly evaluated during different stages of product development. An effective way of evaluating these systems is through the injection of faults and monitoring their impacts on these systems. In this paper, we present SUFI, a simulation-based fault injector that is capable of injecting faults into ADAS features simulated in SUMO (simulation of urban mobility) and analyse the impact of the injected faults on the entire traffic. Simulation-based fault injection is usually used at early stages of product development, especially when the target hardware is not yet available. Using SUFI we target car-following and lane-changing features of ADAS modelled in SUMO. The results of the fault injection experiments show the effectiveness of SUFI in revealing the weaknesses of these models when targeted by faults and attacks.