Multimodal 3D Perception System for Active Safety at Accident-Prone Locations

Principal Investigator(s):

Rusheng Zhang, Assistant Research Scientist – University of Michigan Transportation Research Institute (UMTRI)
Henry Liu, Director – Center for Connected and Automated Transportation (CCAT)
Director – University of Michigan Transportation Research Institute (UMTRI)
Director – Mcity
Professor of Civil and Environmental Engineering – The University of Michigan

Project Abstract:
Accident-prone intersections in Michigan continue to account for a disproportionate share of severe crashes, highlighting the need for infrastructure-based active safety capabilities that can detect, anticipate, and mitigate imminent conflicts in real time. This project develops and demonstrates a full-stack roadside sensing and warning system that integrates LiDAR and wide-area cameras with edge computing, cloud-based data management, and V2X communications. By fusing complementary sensor modalities using Bird’s Eye View (BEV) fusion and conflict prediction, the system aims to improve detection accuracy, 3D localization, robustness under varied conditions, and early identification of potential collisions. The resulting system will generate timely, targeted safety warnings via roadside units (RSUs) and will be validated through staged data collection, model training, and controlled field testing at Mcity, establishing a scalable technical foundation for future deployment at high-risk intersections and similar safety-critical locations.

Institution(s): University of Michigan Transportation Research Institute

Award Year: 2025

Research Focus: Safety

Project Form(s):