Cooperative Driving Automation
MSight: Infrastructure-based Perception System for Cooperative Driving Automation
A full-stack cooperative perception system that closes the loop between roadside perception and V2X communication for cooperative driving automation
The algorithm is deep learning (DL) based and offers state-of-the-art performance with high accuracy and low latency
Production-ready edge-cloud infrastructure for large-scale deployment
Roundabout trajectory dataset contains the vehicle trajectory data perceived by the MSight roadside perception system deployed at the two-lane roundabout at the intersection of State St. and W. Ellsworth Rd. in Ann Arbor, Michigan.
MSight: Traffic Conflict and Crash Detection
A learning-based conflict identification algorithm from video collected at the two-lane roundabout at the intersection of State St. and W. Ellsworth Rd. in Ann Arbor, Michigan.
Provide guidance to traffic agency for crash-prone location identification and crash prevention
Empower research on crash and conflict mechanism
Enable naturalistic driving environment building
ROudanbout traffic COnflict (ROCO) Dataset is a collection of traffic conflict events. Each event captures a 30-second duration of the conflict. The dataset provides the trajectories of the conflicts, along with information about the reason, time, and effect of conflict.
The U.S. Department of Transportation’s Federal Highway Administration (FHWA) awarded a $9.95 million Advanced Transportation and Congestion Management Technologies Deployment (ATCMTD) grant to the University of Michigan for the Smart Intersections: Paving the Way for a National CAV Deployment Project. Prof. Henry Liu is the PI for this project.
News from Traffic Technology Today
News from Michigan Engineering
News from the University Record
News from Detroit's ABC station
News from CEE Department U of M
1. Zhang R., Zou Z., Shen S., Liu H.X. (2022)Design, Implementation, and Evaluation of a Roadside Cooperative Perception System. Transportation Research Record. June 2022. doi:10.1177/03611981221092402 [PDF]
2. Zou. Z., Zhang R., Shen, S., Pandey G., Chakravarty P., Parchami A., H. X., and Liu, H. X. (2022). Real-time Full-stack Traffic Scene Perception for Autonomous Driving with Roadside Cameras. Presented at ICRA 2022. [PDF]