通过行为覆盖评估，提升自动驾驶仿真测试的全面性与真实性。
Assessing behaviour coverage in a multi-agent system simulation for autonomous vehicle testing
- 构建多智能体仿真框架，量化驾驶场景与交互行为覆盖度。
- 提出基于MPC的行人代理，优化测试场景的真实性和挑战性。
- 适合自动驾驶测试团队和仿真系统开发者参考。
随着自动驾驶技术的发展，确保系统的安全性和可靠性至关重要。为此，需要全面的测试方法来评估自动驾驶车辆在多样化复杂现实场景中的表现。本研究聚焦于面向自动驾驶测试的多智能体系统仿真中的行为覆盖分析，提出一种系统化的方法来衡量和评估仿真环境中的行为覆盖程度。通过定义一组驾驶场景与智能体交互模式，评估仿真对自动驾驶相关行为的覆盖广度。研究结果表明，行为覆盖对验证自动驾驶系统有效性与鲁棒性具有重要意义。通过行为覆盖指标分析与基于覆盖的测试，识别出仿真框架中需改进的关键区域。因此，本文提出一种基于模型预测控制（MPC）的行人智能体，其目标函数设计旨在生成更具挑战性的测试场景，同时提升行为真实性。该研究为自动驾驶测试领域提供了仿真环境中系统行为综合评估的洞见，研究成果有助于通过严谨的测试方法提升自动驾驶系统的安全性、可靠性和性能。
原文摘要 · Abstract (English)
As autonomous vehicle technology advances, ensuring the safety and reliability of these systems becomes paramount. Consequently, comprehensive testing methodologies are essential to evaluate the performance of autonomous vehicles in diverse and complex real-world scenarios. This study focuses on the behaviour coverage analysis of a multi-agent system simulation designed for autonomous vehicle testing, and provides a systematic approach to measure and assess behaviour coverage within the simulation environment. By defining a set of driving scenarios, and agent interactions, we evaluate the extent to which the simulation encompasses a broad range of behaviours relevant to autonomous driving. Our findings highlight the importance of behaviour coverage in validating the effectiveness and robustness of autonomous vehicle systems. Through the analysis of behaviour coverage metrics and coverage-based testing, we identify key areas for improvement and optimization in the simulation framework. Thus, a Model Predictive Control (MPC) pedestrian agent is proposed, where its objective function is formulated to encourage \textit{interesting} tests while promoting a more realistic behaviour than other previously studied pedestrian agents. This research contributes to advancing the field of autonomous vehicle testing by providing insights into the comprehensive evaluation of system behaviour in simulated environments. The results offer valuable implications for enhancing the safety, reliability, and performance of autonomous vehicles through rigorous testing methodologies.
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