AVFI: Fault Injection for Autonomous Vehicles

Abstract

Autonomous vehicle (AV) technology is rapidly becoming a reality on U.S. roads, offering the promise of improvements in traffic management, safety, and the comfort and efficiency of vehicular travel. With this increasing popularity and ubiquitous deployment, resilience has become a critical requirement for public acceptance and adoption. Recent studies into the resilience of AVs have shown that though the AV systems are improving over time, they have not reached human levels of automation. Prior work in this area has studied the safety and resilience of individual components of the AV system (e.g., testing of neural networks powering the perception function). However, methods for holistic end-to-end resilience assessment of AV systems are still non-existent.

arXiv commentPublished in: 2018 48th Annual IEEE/IFIP International Conference on Dependable Systems and Networks Workshops (DSN-W)

References in corpus (1)

• DeepXplore: Automated Whitebox Testing of Deep Learning Systems

Cited by in corpus (6)

• Machine Learning Testing: Survey, Landscapes and Horizons
• Experimental Resilience Assessment of An Open-Source Driving Agent
• 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
• Validation Frameworks for Self-Driving Vehicles: A Survey
• Characterizing and Improving the Resilience of Accelerators in Autonomous Robots