Autonomous vehicles (AVs) are rapidly advancing and are expected to play a central role in future mobility. Ensuring their safe deployment requires reliable interaction with other road users, not least pedestrians. Direct testing on public roads is costly and unsafe for rare but critical interactions, making simulation a practical alternative. Within simulation-based testing, adversarial scenarios are widely used to probe safety limits, but many prioritise difficulty over realism, producing exaggerated behaviours which may result in AV controllers that are overly conservative. We propose an alternative method, instead using a cognitively inspired pedestrian model featuring both inter-individual and intra-individual variability to generate behaviourally plausible adversarial scenarios. We provide a proof of concept demonstration of this method's potential for AV control optimisation, in closed-loop testing and tuning of an AV controller. Our results show that replacing the rule-based CARLA pedestrian with the human-like model yields more realistic gap acceptance patterns and smoother vehicle decelerations. Unsafe interactions occur only for certain pedestrian individuals and conditions, underscoring the importance of human variability in AV testing. Adversarial scenarios generated by this model can be used to optimise AV control towards safer and more efficient behaviour. Overall, this work illustrates how incorporating human-like road user models into simulation-based adversarial testing can enhance the credibility of AV evaluation and provide a practical basis to behaviourally informed controller optimisation.

翻译：自动驾驶车辆（AVs）正迅速发展，预计将在未来交通中发挥核心作用。确保其安全部署需要与其他道路使用者（尤其是行人）进行可靠交互。在公共道路上直接测试对于罕见但关键的交互而言成本高昂且不安全，使得仿真成为一种实用的替代方案。在基于仿真的测试中，对抗场景被广泛用于探测安全极限，但许多方法优先考虑难度而非真实性，产生夸大的行为，可能导致自动驾驶控制器过于保守。我们提出了一种替代方法，使用一个具有个体间和个体内变异性的认知启发式行人模型，以生成行为上合理的对抗场景。我们通过闭环测试和自动驾驶控制器调优，提供了该方法在自动驾驶控制优化中潜力的概念验证演示。我们的结果表明，用类人模型替换基于规则的CARLA行人模型，能产生更真实的间隙接受模式和更平滑的车辆减速。不安全交互仅发生在特定行人和条件下，突显了人类变异性在自动驾驶测试中的重要性。该模型生成的对抗场景可用于优化自动驾驶控制，以实现更安全、更高效的行为。总体而言，这项工作阐明了将类人道路使用者模型纳入基于仿真的对抗测试，如何能增强自动驾驶评估的可信度，并为行为导向的控制器优化提供实用基础。