A Retargetable Fault Injection Framework for Safety Validation of Autonomous Vehicles

Fu, Yuting; Terechko, Andrei; Bijlsma, Tjerk; Cuijners, Pieter J. L.; Redegeld, J Jeroen; Ors, Ali Osman

doi:10.1109/icsa-c.2019.00020

Cited by 12 publications

(6 citation statements)

References 9 publications

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“…Regarding the former, the attacker creates adversarial examples or sends malicious signals to fool the ego car's sensor processing models, e.g., perturbing front camera images [16], [17], [18], road signs [19], [20], rendering malicious shapes on the road [21] or billboard [22], spoofing GPS signals [23], spoofing LiDAR signals [24], [25], or influencing both LiDAR and camera inputs [26]. Regarding the latter, an attacker can directly inject faults inside the system to fail it [27], [28], [29], [30], [31], [32], [33], [34], [35]. One issue with this category of methods is the assumption of an attacker's presence.…”

Section: Other Safety Assessment Methodsmentioning

confidence: 99%

A Survey on Scenario-Based Testing for Automated Driving Systems in High-Fidelity Simulation

Zhong¹,

Tang²,

Zhou³

et al. 2021

Preprint

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Automated Driving Systems (ADSS) have seen rapid progress in recent years. To ensure the safety and reliability of these systems, extensive testings are being conducted before their future mass deployment. Testing the system on the road is the closest to real-world and desirable approach, but it is incredibly costly. Also, it is infeasible to cover rare corner cases using such real-world testing. Thus, a popular alternative is to evaluate an ADS's performance in some well-designed challenging scenarios, a.k.a. scenario-based testing. High-fidelity simulators have been widely used in this setting to maximize flexibility and convenience in testing what-if scenarios. Although many works have been proposed offering diverse frameworks/methods for testing specific systems, the comparisons and connections among these works are still missing. To bridge this gap, in this work, we provide a generic formulation of scenario-based testing in high-fidelity simulation and conduct a literature review on the existing works. We further compare them and present the open challenges as well as potential future research directions.

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Section: Other Safety Assessment Methodsmentioning

confidence: 99%

A Survey on Scenario-Based Testing for Automated Driving Systems in High-Fidelity Simulation

Zhong¹,

Tang²,

Zhou³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…To address this issue, many researchers have proposed FI frameworks and tools as a measure to verify vehicle functional safety. For example, a retargetable vehicle-level FI framework capable of automatically injecting various faults into the processor, memory or IO at the runtime was proposed in [ 57 ]. The proposed framework has been validated and demonstrated using an experimental HIL test for autonomous driving, i.e., EcoTwin truck platooning.…”

Section: Background and Related Workmentioning

confidence: 99%

Hardware-in-the-Loop-Based Real-Time Fault Injection Framework for Dynamic Behavior Analysis of Automotive Software Systems

Abboush

Bamal

Knieke

et al. 2022

Sensors

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A well-known challenge in the development of safety-critical systems in vehicles today is that reliability and safety assessment should be rigorously addressed and monitored. As a matter of fact, most safety problems caused by system failures can lead to serious hazards and loss of life. Notwithstanding the existence of several traditional analytical techniques used for evaluation based on specification documents, a complex design, with its multivariate dynamic behavior of automotive systems, requires an effective method for an experimental analysis of the system’s response under abnormal conditions. Simulation-based fault injection (FI) is a recently developed approach to simulate the system behavior in the presence of faults at an early stage of system development. However, in order to analyze the behavior of the system accurately, comprehensively and realistically, the real-time conditions, as well as the dynamic system model of the vehicle, should be considered. In this study, a real-time FI framework is proposed based on a hardware-in-the-loop (HiL) simulation platform and a real-time electronic control unit (ECU) prototype. The framework is modelled in the MATLAB/Simulink environment and implemented in the HiL simulation to enable the analysis process in real time during the V-cycle development process. With the objective of covering most of the potential faults, nine different types of sensor and actuator control signal faults are injected programmatically into the HiL system as single and multiple faults without changing the original system model. Besides, the model of the whole system, containing vehicle dynamics with the environment system model, is considered with complete and comprehensive behavioral characteristics. A complex gasoline engine system is used as a case study to demonstrate the capabilities and advantages of the proposed framework. Through the proposed framework, transient and permanent faults are injected in real time during the operation of the system. Finally, experimental results show the effects of single and simultaneous faults on the system performance under a faulty mode compared to the golden running mode.

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“…Similarly, Refs. [12][13][14][15] deploy fault simulation to classify faults in automotive systems considering the requirements of ISO 26262. In short, when fault simulation is used alone to classify faults, additional techniques targeting the classification of undetected faults are necessary.…”

Section: Related Workmentioning

confidence: 99%

Automated Identification of Application-Dependent Safe Faults in Automotive Systems-on-a-Chips

et al. 2022

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ISO 26262 requires classifying random hardware faults based on their effects (safe, detected, or undetected) within integrated circuits used in automobiles. In general, this classification is addressed using expert judgment and a combination of tools. However, the growth of integrated circuit complexity creates a huge fault space; hence, this form of fault classification is error prone and time consuming. Therefore, an automated and systematic approach is needed to target hardware fault classification in automotive systems on chips (SoCs), considering the application software. This work focuses on identifying safe faults: the proposed approach utilizes coverage analysis to identify candidate safe faults considering all the constraints coming from the application. Then, the behavior of the application software is modeled so that we can resort to a formal analysis tool. The proposed technique is evaluated on the AutoSoC benchmark running a cruise control application. Resorting to our approach, we could classify 20%, 11%, and 13% of all faults in the central processing unit (CPU), universal asynchronous receiver–transmitter (UART), and controller area network (CAN) as safe faults, respectively. We also show that this classification can increase the diagnostic coverage of software test libraries targeting the CPU and CAN modules by 4% to 6%, increasing the achieved testable fault coverage.

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A Retargetable Fault Injection Framework for Safety Validation of Autonomous Vehicles

Cited by 12 publications

References 9 publications

A Survey on Scenario-Based Testing for Automated Driving Systems in High-Fidelity Simulation

A Survey on Scenario-Based Testing for Automated Driving Systems in High-Fidelity Simulation

Hardware-in-the-Loop-Based Real-Time Fault Injection Framework for Dynamic Behavior Analysis of Automotive Software Systems

Automated Identification of Application-Dependent Safe Faults in Automotive Systems-on-a-Chips

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