结合规避与制动反应的安全参考模型用于自动驾驶车辆
Combining Evasive and Braking Reactions for Safety Reference Models in Automated Vehicles
浏览论文内容
中文总结 AI 辅助
本文提出混合模糊安全模型（FSM-H），整合纵向制动与横向规避转向，模拟人类决策过程，在切入场景中扩大可预防性包络，为ADS安全基准测试提供透明参考。
中文摘要 AI 辅助
对谨慎且胜任的人类驾驶员的计算模型对于基于场景的自动驾驶系统（ADS）评估至关重要。然而，大多数现有的安全参考模型主要关注纵向制动，忽视了规避转向在人类碰撞避免中的作用。本文提出了一种混合模糊安全模型（FSM-H），该模型在统一的行为框架内整合了纵向缓解和横向规避。制动组件由主动模糊安全（PFS）指标控制，该指标表示纵向安全裕度的侵蚀，而转向组件由变道临界模糊安全（CFS-LC）驱动，该指标捕捉横向冲突严重性和机动可行性。一个有限状态架构模拟了从标称驾驶到制动，并在必要时到规避转向的顺序升级过程，并纳入了感知-反应时间和车道检查延迟以反映人类决策过程。该模型在重建的高临界性切入场景中进行了评估，并与仅制动和仅转向的参考策略进行了比较。结果表明，混合方法扩大了可预防性包络，同时保持了行为合理性和计算可行性。所提出的框架提供了一个透明且可解释的人类参考模型，适用于基于仿真的ADS安全基准测试和监管评估。
英文摘要
Computational models of careful and competent human drivers are essential for scenario-based evaluation of automated driving systems (ADS). However, most existing safety reference models primarily focus on longitudinal braking, neglecting the role of evasive steering in human collision avoidance. This paper proposes a hybrid Fuzzy-Safety Model (FSM-H) that integrates longitudinal mitigation and lateral avoidance within a unified behavioral framework. The braking component is governed by Proactive Fuzzy Safety (PFS) metrics, representing the erosion of longitudinal safety margins, while the steering component is driven by Criticality Fuzzy Safety for lane-change (CFS-LC), capturing lateral conflict severity and maneuver feasibility. A finite-state architecture models the sequential escalation from nominal driving to braking and, when necessary, to evasive steering, incorporating perception-reaction time and lane-check delays to reflect human decision processes. The model is evaluated in reconstructed high-criticality cut-in scenarios and compared with braking-only and steering-only reference strategies. Results show that the hybrid approach expands the preventability envelope while maintaining behavioral plausibility and computational tractability. The proposed framework provides a transparent and explainable human reference model suitable for simulation-based ADS safety benchmarking and regulatory assessment.
发表机构
- Joint Research Centre for the European Commission（欧盟委员会联合研究中心）
机构由 AI 辅助整理，请以论文原文为准。
