发表机构
University of Tennessee at Chattanooga（田纳西大学查塔努加分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究针对城市交叉路口危险问题，提出PRISA框架，利用隐私保护、低光鲁棒的路边传感器，通过自动整理训练数据训练轨迹预测模型，进行连续运动预测和双重替代安全评估，经实验验证其在交叉路口安全监测方面具有实际可行性。
AI 中文摘要
城市交叉路口是道路网络中最危险的地点之一，对车辆和行人、骑自行车者等弱势道路使用者构成重大风险。多智能体交互的复杂性需要能够在冲突升级为碰撞之前进行预测的连续实时监测系统。我们提出了PRISA，这是一个模块化的基础设施激光雷达框架，利用隐私保护、低光鲁棒的路边传感器进行长期交通观测和边缘实时风险检测。该框架包括两个核心组件：传感与感知层以及即插即用的风险评估模块。风险评估模块会自动从累积的感知输出中整理特定地点的训练数据，以训练轨迹预测模型而无需人工标注。然后，它使用训练好的模型进行连续运动预测和双重替代安全评估，使用碰撞时间（TTC）评估纵向冲突，使用预测侵入后时间（PPET）评估交叉和涉及弱势道路使用者的交互。PRISA在公共R-LiViT数据集上进行了评估，并部署在田纳西州查塔努加一个实时信号控制交叉路口的NVIDIA Jetson AGX Thor上。基于PPET的评估在2.4秒预测范围内的端到端延迟为194毫秒，基于TTC的检测和感知保持在实时限制内，证明了主动式多智能体交叉路口安全监测的实际可行性。
英文摘要
Urban intersections are among the most hazardous locations in road networks, posing significant risks to vehicles and vulnerable road users (VRUs) such as pedestrians and cyclists. The complexity of multi-agent interactions demands continuous, real-time monitoring systems capable of anticipating conflicts before they escalate into crashes. We present PRISA, a modular infrastructure LiDAR framework leveraging privacy-preserving, low-light-robust roadside sensors for long-term traffic observation and real-time risk detection at the edge. The framework comprises two core components: a sensing and perception layer and a plug-and-play risk assessment module. The latter automatically curates site-specific training data from accumulated perception outputs to train a trajectory prediction model without manual annotation. It then deploys the trained model for continuous motion forecasting and dual surrogate safety evaluation, using Time-to-Collision (TTC) for longitudinal conflicts and Predicted Post-Encroachment Time (PPET) for crossing and VRU-involved interactions. PRISA is evaluated on the public R-LiViT dataset and deployed on an NVIDIA Jetson AGX Thor at a live signalized intersection in Chattanooga, Tennessee. PPET-based assessment operates at 194~ms end-to-end latency over a 2.4-second predictive horizon, with TTC-based detection and perception remaining within real-time constraints, demonstrating practical feasibility for proactive multi-agent intersection safety monitoring.
CommentsAccepted for publication at the 2026 IEEE 29th International Conference on Intelligent Transportation Systems (ITSC 2026). 8 pages, 2 figures
