PRISA: Proactive Infrastructure LiDAR Framework for Intersection Safety Assessment

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.

Accepted for publication at the 2026 IEEE 29th International Conference on Intelligent Transportation Systems (ITSC 2026). 8 pages, 2 figures

Machine Learning (cs.LG); Systems and Control (eess.SY)

arXiv:2607.16156 [cs.LG]

(or arXiv:2607.16156v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2607.16156 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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