# A Systematic Evaluation of Infrastructure-Based Radar System for Highway Traffic Monitoring

arXiv: https://arxiv.org/abs/2609.27143 · HTML: https://mlsanity.com/paper/2609.27143/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 19 references that are in the map.
- First posted on arXiv 22 Sep 2026.

## Builds on (most cited first, 5 of 5)

- [Center-based 3D Object Detection and Tracking](https://mlsanity.com/paper/2006.11275/) (2020)
- [BoT-SORT: Robust Associations Multi-Pedestrian Tracking](https://mlsanity.com/paper/2206.14651/) (2022)
- [Advanced computer vision for extracting georeferenced vehicle trajectories from drone imagery](https://mlsanity.com/paper/2411.02136/) (2024)
- [Vehicle-group-based Crash Risk Prediction and Interpretation on Highways](https://mlsanity.com/paper/2402.12415/) (2024)
- [Empowering Microscopic Traffic Simulators with Realistic Perception using Surrogate Sensor Models](https://mlsanity.com/paper/2508.02858/) (2025)

## Abstract (arXiv)

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.