PRISA: Proactive Infrastructure LiDAR Framework for Intersection Safety Assessment

2026-07-17 · Source: Machine Learning · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems, Internet of Things (IoT) & Connected Devices · Depth: Expert, quick

Summary

PRISA is a modular infrastructure LiDAR framework designed to enhance urban intersection safety by proactively detecting multi-agent conflicts. It leverages privacy-preserving, low-light-robust roadside sensors for long-term traffic observation and real-time risk detection at the edge. The framework integrates a sensing and perception layer with a plug-and-play risk assessment module. This module automatically curates site-specific training data from perception outputs to train a trajectory prediction model without manual annotation. Subsequently, it deploys this model for continuous motion forecasting and dual surrogate safety evaluation, utilizing Time-to-Collision (TTC) for longitudinal conflicts and Predicted Post-Encroachment Time (PPET) for crossing and vulnerable road user interactions. Evaluated on the R-LiViT dataset and deployed on an NVIDIA Jetson AGX Thor at a live signalized intersection in Chattanooga, Tennessee, PRISA demonstrates practical feasibility with PPET-based assessment operating at 194 ms latency over a 2.4-second predictive horizon, while TTC detection remains real-time.

Key takeaway

For AI Engineers developing smart city infrastructure, PRISA offers a robust framework for proactive intersection safety. You should consider integrating infrastructure LiDAR with automated data curation to train real-time trajectory prediction models. This approach, validated with 194 ms latency for PPET-based assessment, enables continuous multi-agent conflict detection, significantly improving urban road network safety without manual annotation overhead.

Key insights

PRISA uses infrastructure LiDAR and self-curated data to proactively assess intersection safety with real-time conflict prediction.

Principles

• Proactive conflict anticipation enhances road safety.
• Automated data curation can train prediction models.
• Dual surrogate safety metrics improve assessment.

Method

PRISA's method involves a sensing/perception layer, followed by a risk assessment module that self-curates training data, trains a trajectory prediction model, and then deploys it for continuous motion forecasting using TTC and PPET.

In practice

• Deploy roadside LiDAR for continuous traffic monitoring.
• Automate training data generation for trajectory models.
• Use TTC and PPET for comprehensive conflict detection.

Topics

• Infrastructure LiDAR
• Intersection Safety
• Trajectory Prediction
• Surrogate Safety Metrics
• Real-time Monitoring
• NVIDIA Jetson AGX Thor

Best for: Computer Vision Engineer, AI Scientist, AI Engineer, Machine Learning Engineer, Research Scientist

Related on AIssential

• On Exploring Input Resolution Scaling For Anytime LiDAR Object Detection — Dynamically scaling LiDAR input resolution with a single DNN model enables efficient anytime 3D object detection for real-time systems.
• Computer Vision-Based Worker Safety Compliance — Computer vision provides continuous, AI-driven monitoring to enhance worker safety and compliance.
• TraRA: Trajectory-level Recognition Aggregation for Video Text Spotting in Urban Surveillance — TraRA improves video text spotting by aggregating temporal and multimodal cues for robust trajectory-level recognition.
• Integrating Object Detection, LiDAR-Enhanced Depth Estimation, and Segmentation Models for Railway Environments — A modular framework integrates neural networks and LiDAR for railway obstacle detection and distance estimation.
• Learning collision risk proactively from naturalistic driving data at scale — GSSM proactively learns collision risk from naturalistic driving data, outperforming baselines in accuracy and timeliness.
• An Integrated Roadside Sensing and Communication Framework for Vulnerable Road User Safety at Signalized Intersections — Multi-modal sensing and edge analytics significantly improve Vulnerable Road User safety at signalized intersections.

See Counsel's argued verdicts on the open AI decisions leaders are weighing →

Open in AIssential →

Editorial summary, takeaway, and curation by AIssential. Original article published by Machine Learning.