PRISM：面向自主交通系统主动安全的智能体多模型架构
PRISM: An Agentic Multi-Model Architecture for Proactive Safety in Autonomous Transportation Systems
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中文总结 AI 辅助
PRISM是一种智能体多模型安全架构，通过逆碰撞概率建模等技术，在1296个场景上验证了其在自主交通系统主动安全及弱势道路使用者风险降低方面的性能。
中文摘要 AI 辅助
自主智能交通系统运行于复杂城市环境中，其安全性取决于车辆行为、环境条件及 vulnerable road users（VRU，弱势道路使用者，如行人与骑行者）之间的交互。多数高级驾驶辅助系统（ADAS）采用仅在危险出现后才激活的反应式机制，美国弱势道路使用者死亡人数上升凸显了这一关键局限。本研究引入PRISM（Proactive Risk Intelligence and Safety Management，主动风险智能与安全管理），一种从反应式碰撞规避转向主动持续风险管理的智能体多模型安全架构。PRISM采用逆碰撞概率建模，将二元碰撞分类器转换为动态可解释的安全分数。三个分别处理轨迹运动学、环境风险及弱势道路使用者交互的专用模型并行运行，由包含强化学习、上下文记忆及特征级归因的推理层进行协调。该系统提供四个层级的分级安全干预，从静默监控到紧急警报不等。与具有静态阈值的基于规则的系统不同，PRISM可实时动态调整安全参数。在来自三个自然驾驶数据集的1296个场景上完成验证，无需针对特定数据集重新训练，该系统获得的平均安全分数为100分制中的68分，将77.6%的场景归类为 advisory（ advisory 级），标记的险情率为3.8%，11%的场景升级为干预或紧急响应。特征归因始终将轨迹风险与弱势道路使用者距离识别为主要安全因素。PRISM提供了一个统一可解释的框架，聚焦于密集城市环境中弱势道路使用者风险降低的主动交通安全。
英文摘要
Autonomous and intelligent transportation systems operate in complex urban environments where safety depends on interactions among vehicle behavior, environmental conditions, and vulnerable road users (VRUs) such as pedestrians and cyclists. Most advanced driver assistance systems (ADAS) employ reactive mechanisms that activate only after hazards have emerged, a critical limitation underscored by rising VRU fatalities in the United States. This study introduces PRISM (Proactive Risk Intelligence and Safety Management), an agentic multi-model safety architecture that transitions from reactive crash avoidance to proactive, continuous risk management. PRISM employs inverse crash-probability modeling to convert binary crash classifiers into dynamic, interpretable safety scores. Three specialized models addressing trajectory kinematics, environmental risk, and VRU interaction operate concurrently, coordinated by a reasoning layer incorporating reinforcement learning, contextual memory, and feature-level attribution. The system provides graduated safety interventions across four tiers, from silent monitoring to emergency alerts. Unlike rule-based systems with static thresholds, PRISM dynamically adjusts safety parameters in real time. Validated across 1,296 scenarios from three naturalistic driving datasets without dataset-specific retraining, the system yielded a mean safety score of 68 out of 100, classified 77.6% of scenarios as advisory, and flagged a near-miss rate of 3.8%, with 11% of scenarios escalating to intervention or emergency response. Feature attribution consistently identified trajectory risk and VRU proximity as primary safety factors. PRISM provides a unified, interpretable framework for proactive transportation safety with emphasis on VRU risk reduction in dense urban environments.
发表机构
- American Center for Mobility（美国移动出行中心）
机构由 AI 辅助整理，请以论文原文为准。
