用AI预测多车交互，精准计算碰撞时间以提升复杂路况安全评估
AI2-Active Safety: AI-enabled Interaction-aware Active Safety Analysis with Vehicle Dynamics
- 结合车辆动力学与超图神经网络，建模多车交互行为
- 生成概率化高保真碰撞时间分布，比传统方法更贴近真实路况
- 适合自动驾驶系统安全验证，尤其适用于高速复杂交通场景
本文提出一种基于AI的交互感知主动安全分析框架，考虑多车群体交互。该框架采用融合道路坡度因素的自行车模型精确捕捉车辆动态；同时构建基于超图的AI模型，预测周围交通流的概率轨迹。通过融合两者，将车辆间距建模为三维道路表面下的随机常微分方程解，得到高保真代理安全指标（如碰撞时间，TTC）。利用四阶龙格-库塔积分与AI推理相结合的随机数值方法，生成概率加权的高保真碰撞时间（HF-TTC）分布，可反映多智能体复杂动作与行为不确定性。在高速公路数据集上，相较传统恒速假设及非交互感知方法，本框架显著提升了复杂交通环境下的安全感知能力。
原文摘要 · Abstract (English)
This paper introduces an AI-enabled, interaction-aware active safety analysis framework that accounts for groupwise vehicle interactions. Specifically, the framework employs a bicycle model-augmented with road gradient considerations-to accurately capture vehicle dynamics. In parallel, a hypergraph-based AI model is developed to predict probabilistic trajectories of ambient traffic. By integrating these two components, the framework derives vehicle intra-spacing over a 3D road surface as the solution of a stochastic ordinary differential equation, yielding high-fidelity surrogate safety measures such as time-to-collision (TTC). To demonstrate its effectiveness, the framework is analyzed using stochastic numerical methods comprising 4th-order Runge-Kutta integration and AI inference, generating probability-weighted high-fidelity TTC (HF-TTC) distributions that reflect complex multi-agent maneuvers and behavioral uncertainties. Evaluated with HF-TTC against traditional constant-velocity TTC and non-interaction-aware approaches on highway datasets, the proposed framework offers a systematic methodology for active safety analysis with enhanced potential for improving safety perception in complex traffic environments.
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