PRISA: Proactive Infrastructure LiDAR Framework for Intersection Safety Assessment
2026-07-17 • Machine Learning
Machine Learning
AI summaryⓘ
The authors present PRISA, a system that uses special roadside sensors to watch traffic at busy intersections and spot possible dangers before accidents happen. It works by automatically learning from traffic patterns without needing people to label the data. PRISA predicts where vehicles and pedestrians might move next and uses two safety measures to detect risks in real-time. The system was tested successfully on real traffic data and a live intersection, showing it can work quickly enough to help improve traffic safety.
LiDARTrajectory PredictionTime-to-Collision (TTC)Predicted Post-Encroachment Time (PPET)Edge ComputingMulti-agent SystemsRisk AssessmentUrban IntersectionsReal-time MonitoringAutonomous Sensing
Authors
Tam Bang, Hussam Abubakr, Emiliano de la Garza Villarreal, Truc Phuong Nguyen, Austin Harris, Toru Hirano, Mina Sartipi, Yunfei Xu, Hoang H. Nguyen
Abstract
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.