当停止失效：通过人机交互自动驾驶重新思考安全交通系统的最小风险条件
When Stopping Fails: Rethinking Minimal Risk Conditions through Human-Interactive Autonomous Driving for Safe Transportation Systems
AI总结：
分析自动驾驶车辆因停止行为导致的交通阻塞、应急干扰等事故，提出需从被动策略转向人机交互自主性，增强感知、规划与控制能力。
摘要：
自动驾驶车辆（AV）越来越多地部署在城市环境中，但其安全框架仍主要围绕碰撞避免和最小风险条件（MRC）行为设计，例如在不确定性出现时减速或停止。尽管这能有效降低即时碰撞风险，但实际部署表明，仅靠停止并不能保证安全融入人类主导的道路交通系统。市政报告和公共记录显示，AV的降级行为可能阻塞交通、干扰应急响应操作，并为乘客和行人带来可达性挑战。本文分析了涉及AV停止行为和人机交互失败的公开记录事件。我们根据当前AV架构中感知、规划和控制的局限性对这些事件进行分类。利用这一分类法，我们识别出现有安全范式中的关键缺陷，特别是缺乏解释人类权威、响应多模态指令以及适应动态、社会规范交通条件的机制。随后，我们回顾了支持人机交互感知、语言基础和可达性感知规划以及通过远程引导和遥操作辅助控制的新兴研究方向。分析强调了增强当前AV安全框架以具备与人类主体和基础设施合作交互能力的必要性。这些发现表明，AV在城市中的可靠部署需要从被动降级策略转向人机交互自主性。
英文摘要：
Autonomous vehicles (AVs) are increasingly deployed in urban environments, yet their safety frameworks remain primarily designed around collision avoidance and minimal risk condition (MRC) behaviors such as slowing or stopping when uncertainty arises. Although effective in reducing immediate crash risk, real-world deployments indicate that stopping alone does not guarantee safe integration into human-governed roadway systems. Incidents reported by municipalities and public records show that AV fallback behaviors can obstruct traffic, interfere with emergency response operations, and create accessibility challenges for passengers and pedestrians. This paper presents an analysis of publicly documented incidents involving AV stopping behavior and human-AV interaction failures. We categorize these incidents according to limitations in perception, planning, and control within current AV architectures. Using this taxonomy, we identify key gaps in existing safety paradigms, particularly the lack of mechanisms for interpreting human authority, responding to multimodal instructions, and adapting to dynamic, socially regulated traffic conditions. We then review emerging research directions that support human-interactive perception, language-grounded and accessibility-aware planning, and assisted control through remote guidance and teleoperation. The analysis highlights the need to augment current AV safety frameworks with capabilities that enable cooperative interaction with human agents and infrastructure. These findings suggest that reliable urban deployment of AVs requires moving beyond passive fallback strategies toward human-interactive autonomy.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
