为自动驾驶环境感知的AI系统设计实时监控方法，解决缺乏完整需求规范的问题。
A Method for the Runtime Validation of AI-based Environment Perception in Automated Driving System
- 基于摄像头和激光雷达双模感知，设计运行时功能监控机制。
- 在实验室模型车环境中通过场景化测试验证监控有效性。
- 适合关注自动驾驶安全验证的工程师与研究人员参考。
环境感知是自动驾驶系统（ADS）执行动态驾驶任务的基础环节。当前主流的汽车安全标准（ISO 26262 和 ISO 21448）依赖全面的需求规格来确保系统可严格测试并符合安全要求。然而，基于人工智能（AI）的感知系统缺乏完整的规范，主要依赖大规模数据集进行训练。本文提出一种针对双模态AI感知（分别基于摄像头与激光雷达）的功能运行时监控方法。为评估该监控器的适用性，我们在受控实验室环境中使用模型车进行了基于场景的定性评估，并讨论了结果以分析其性能及在真实应用中的潜力。
原文摘要 · Abstract (English)
Environment perception is a fundamental part of the dynamic driving task executed by Autonomous Driving Systems (ADS). Artificial Intelligence (AI)-based approaches have prevailed over classical techniques for realizing the environment perception. Current safety-relevant standards for automotive systems, International Organization for Standardization (ISO) 26262 and ISO 21448, assume the existence of comprehensive requirements specifications. These specifications serve as the basis on which the functionality of an automotive system can be rigorously tested and checked for compliance with safety regulations. However, AI-based perception systems do not have complete requirements specification. Instead, large datasets are used to train AI-based perception systems. This paper presents a function monitor for the functional runtime monitoring of a two-folded AI-based environment perception for ADS, based respectively on camera and LiDAR sensors. To evaluate the applicability of the function monitor, we conduct a qualitative scenario-based evaluation in a controlled laboratory environment using a model car. The evaluation results then are discussed to provide insights into the monitor's performance and its suitability for real-world applications.
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