Radar system tested for highway traffic detection and tracking accuracy
A Systematic Evaluation of Infrastructure-Based Radar System for Highway Traffic Monitoring
Computer Vision and Pattern Recognition
Summary
Traffic monitoring using roadside radar can see cars from far away but hasn’t been thoroughly tested in real traffic. This study creates a dataset comparing radar to drone footage to see how well radar spots and follows cars on a busy highway. The radar finds most cars but misses some, especially in traffic jams or at longer distances. While it tracks vehicles fairly well when detected, it tends to underestimate how many vehicles are on the road. The authors also share their dataset publicly to help others improve these systems.
What this means in practice
- •For traffic management teams: Improve traffic flow estimates by understanding radar detection limits on highways under varying conditions.
- •For road infrastructure planners: Use validated radar data to support decisions on sensor deployment for long-range vehicle tracking on highways.
Authors
Tianheng Zhu, Woei-chyi Chang, Alamss Riaz, Sogand Hasanzadeh, Yiheng Feng
Abstract
Infrastructure-based radar systems offer robust and long-range solutions for traffic monitoring, yet their detection and tracking performance under real-world conditions remains insufficiently evaluated. This study introduces DRaT (Drone and Radar Trajectories), a dual-modality dataset of naturalistic vehicle trajectories collected at a highway merging segment in Fort Worth, Texas, to systematically assess radar sensing performance against drone-derived ground truth. The performance is evaluated at three levels: individual vehicle detection, trajectory tracking, and macroscopic traffic parameter estimation. For individual vehicle detection, the radar achieves an overall precision of 78% and a recall of 57%, with degraded performance under congested traffic conditions and at longer distances. At the trajectory level, the radar demonstrates reasonably strong tracking performance (IDF1 = 0.699), maintaining reliable vehicle identities when tracks are successfully established. For macroscopic traffic flow metrics, the radar accurately estimates space-mean speed (MAPE < 4%) but underestimates density and volume by approximately 23% due to missed detections. The paper also discusses practical deployment considerations and potential downstream applications of roadside radar sensing systems. To support reproducible research on infrastructure-based sensing systems, we have open-sourced the DRaT dataset on Zenodo: https://zenodo.org/records/20171110.