Experimental Resilience Assessment of An Open-Source Driving Agent

Abstract

Autonomous vehicles (AV) depend on the sensors like RADAR and camera for the perception of the environment, path planning, and control. With the increasing autonomy and interactions with the complex environment, there have been growing concerns regarding the safety and reliability of AVs. This paper presents a Systems-Theoretic Process Analysis (STPA) based fault injection framework to assess the resilience of an open-source driving agent, called openpilot, under different environmental conditions and faults affecting sensor data. To increase the coverage of unsafe scenarios during testing, we use a strategic software fault-injection approach where the triggers for injecting the faults are derived from the unsafe scenarios identified during the high-level hazard analysis of the system. The experimental results show that the proposed strategic fault injection approach increases the hazard coverage compared to random fault injection and, thus, can help with more effective simulation of safety-critical faults and testing of AVs. In addition, the paper provides insights on the performance of openpilot safety mechanisms and its ability in timely detection and recovery from faulty inputs.

10 pages, 7 figures

References in corpus (6)

• DeepXplore: Automated Whitebox Testing of Deep Learning Systems
• Real time Detection of Lane Markers in Urban Streets
• Robust Physical-World Attacks on Deep Learning Models
• DeepTest: Automated Testing of Deep-Neural-Network-driven Autonomous Cars
• Learning a Driving Simulator
• AVFI: Fault Injection for Autonomous Vehicles

Cited by in corpus (7)

• Software Engineering for AI-Based Systems: A Survey
• Testing and verification of neural-network-based safety-critical control software: A systematic literature review
• A Survey on Scenario-Based Testing for Automated Driving Systems in High-Fidelity Simulation
• Kayotee: A Fault Injection-based System to Assess the Safety and Reliability of Autonomous Vehicles to Faults and Errors
• Who is in Control? Practical Physical Layer Attack and Defense for mmWave based Sensing in Autonomous Vehicles
• Validation Frameworks for Self-Driving Vehicles: A Survey
• Runtime Stealthy Perception Attacks against DNN-based Adaptive Cruise Control Systems