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。为确保其安全部署，必须实现与其他道路使用者（尤其是行人）的可靠交互。在公共道路上直接测试对于罕见但关键的交互场景成本高昂且不安全，因此仿真成为实用的替代方案。在基于仿真的测试中，对抗场景被广泛用于探索安全边界，但许多方法过于追求难度而牺牲真实性，产生夸张的行为模式，可能导致自动驾驶控制器过于保守。我们提出一种替代方法，采用具有个体间与个体内变异性的认知启发式行人模型，以生成行为可信的对抗场景。我们通过闭环测试和自动驾驶控制器调优，提供了该方法在控制优化潜力的概念验证。结果表明，用类人模型替代基于规则的CARLA行人模型能产生更真实的间隙接受模式和平滑的车辆减速曲线。不安全交互仅出现在特定行人个体及条件下，这凸显了人类行为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策略，以实现更安全高效的行为。总体而言，本研究表明将类人道路使用者模型融入基于仿真的对抗测试，能够提升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践基础。