Quality Assurance for Autonomous Driving Systems: A Software Engineering Perspective
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Quality assurance for Autonomous Driving Systems (ADS) has been long recognized as a notoriously challenging but crucial task which requires substantial domain-specific knowledge and engineering efforts to bridge the last gap of further deploying the state-of-the-art ADS methodologies to safety, reliability and security-concerned practical applications. Therefore, in this tutorial, we would like to provide a high-level overview of our work in advancing the quality assurance of ADS. This tutorial aims to introduce the solutions and frameworks to tackle the quality challenges of ADS from two aspects: 1) a complete quality analysis pipeline for AI components in ADS, from unit level to system level, and 2) a series of quality assurance frameworks for AI-enabled Cyber-physical Systems (CPS) specialized for ADS. In particular, this first part will present the works for quality analysis of ADS, including robustness benchmarking of AI-enabled sensor fusion systems, testing of simulation- based ADS, risk assessment from data distribution and uncertainty, and repair methods for AI components. The second part will summarize the works of trustworthy ADS from the CPS perspective, including the CPS benchmark, ensemble method for AI-controller fusion, AI-aware testing methods and LLM-enabled approaches for planning and design of AI components. The third part will introduce the recent advances in applying LLM for autonomous driving, including taking LLM-centric decision-making using language as an interface and opportunities in applying LLM for cross-modal test generation.