Toward Ensuring Safety for Autonomous Driving Perception: Standardization Progress, Research Advances, and Perspectives

Abstract

Perception systems play a crucial role in autonomous driving by reading the sensory data and providing meaningful interpretation of the operating environment for decision-making and planning. Guaranteeing a safe perception performance is the foundation for high-level autonomy, so that we can hand over the driving and monitoring tasks to the machine with ease. With the motivation of improving the perception systems' safety, this survey analyzes and reviews the current achievements of safety-related standards and definitions, sensory modeling, and metrics for perception tasks in autonomous driving applications. Furthermore, it covers the generic categorization of potential failures and causal analysis in perception tasks, correlates the effect with the scenario modelling choices, and highlights major triumphs and noted limitations encountered by current research efforts. The new safety challenges laid out by the information exchange stage of the connected autonomous vehicle application have also been summarized. The open research questions and future directions are outlined to welcome researchers and practitioners to this exciting domain.