Analyzing and Improving Resilience and Robustness of Autonomous Systems

Analyzing and Improving Resilience and Robustness of Autonomous Systems

연구 분야: Artificial Intelligence

학회: ICCAD '22: Proceedings of the 41st IEEE/ACM International Conference on Computer-Aided Design

Autonomous systems have reached a tipping point, with a myriad of self-driving cars, unmanned aerial vehicles (UAVs), and robots being widely applied and revolutionizing new applications. The continuous deployment of autonomous systems reveals the need for designs that facilitate increased resiliency and safety. The ability of an autonomous system to tolerate, or mitigate against errors, such as environmental conditions, sensor, hardware and software faults, and adversarial attacks, is essential to ensure its functional safety. Application-aware resilience metrics, holistic fault analysis frameworks, and lightweight fault mitigation techniques are being proposed for accurate and effective resilience and robustness assessment and improvement. This paper explores the origination of fault sources across the computing stack of autonomous systems, discusses the various fault impacts and fault mitigation techniques of different scales of autonomous systems, and concludes with challenges and opportunities for assessing and building next-generation resilient and robust autonomous systems.