这篇论文提出PRISA，用路边LiDAR自动学习轨迹预测，无需人工标注就能在边缘实时评估碰撞风险，延迟仅194毫秒，已在真实路口跑通。
PRISA框架利用路边LiDAR传感器实现隐私保护、低光照鲁棒的交通监测和实时风险检测。其风险评估模块自动从感知数据中训练轨迹预测模型，无需手动标注，并部署在NVIDIA Jetson AGX Thor上。在R-LiViT数据集上的评估显示，PPET评估在2.4秒预测时域内端到端延迟为194毫秒，TTC检测满足实时约束。该框架已在田纳西州查塔努加的真实信号交叉口部署，验证了主动式多智能体交叉口安全监测的可行性。
PRISA: Proactive Infrastructure LiDAR Framework for Intersection Safety Assessment
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.