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.

翻译：随着自动驾驶技术的进步，确保这些系统的安全性和可靠性变得至关重要。因此，全面的测试方法对于评估自动驾驶车辆在各种复杂现实场景中的性能至关重要。本研究聚焦于为自动驾驶车辆测试设计的多智能体系统仿真的行为覆盖分析，并提出了一种系统性的方法来度量和评估仿真环境内的行为覆盖。通过定义一组驾驶场景和智能体交互，我们评估了仿真在多大程度上涵盖了与自动驾驶相关的广泛行为。我们的研究结果凸显了行为覆盖在验证自动驾驶车辆系统有效性和鲁棒性方面的重要性。通过对行为覆盖度量和基于覆盖的测试进行分析，我们确定了仿真框架中需要改进和优化的关键领域。因此，本文提出了一种模型预测控制（MPC）行人智能体，其目标函数被设计为鼓励\textit{有趣}的测试，同时促使其行为比先前研究的其他行人智能体更具真实性。这项研究通过提供对模拟环境中系统行为全面评估的见解，有助于推动自动驾驶车辆测试领域的发展。研究结果为通过严格的测试方法提升自动驾驶车辆的安全性、可靠性和性能提供了有价值的启示。