Infrastructure Sensor-enabled Vehicle Data Generation using Multi-Sensor Fusion for Proactive Safety Applications at Work Zone

2025-09-29

AI Summary

To address challenges in high-risk road segments (e.g., construction zones), including severe viewpoint distortion, occlusion, geometric complexity, and high deployment costs, this paper proposes a lightweight multi-sensor fusion framework integrating roadside cameras and LiDAR—augmented by radar and RTK-GPS—for low-cost, scalable vehicle detection and high-precision localization. A novel late-fusion strategy based on Kalman filtering is introduced to enable sensor complementarity, fault tolerance against individual sensor failures, and enhanced trajectory consistency. In a co-simulation environment, the method reduces longitudinal positioning error by 70% and achieves lateral accuracy of 1–3 meters. Field validation confirms strong alignment between fused trajectories and ground-truth references, demonstrating robustness under real-world conditions and practical feasibility for large-scale deployment.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Computer Vision: Multi-modal Vision
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Location- and context-aware Web and WoT applications and services
• Security and Privacy: Large-scale security measurements
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

Infrastructure-based sensing and real-time trajectory generation show promise for improving safety in high-risk roadway segments such as work zones, yet practical deployments are hindered by perspective distortion, complex geometry, occlusions, and costs. This study tackles these barriers by integrating roadside camera and LiDAR sensors into a cosimulation environment to develop a scalable, cost-effective vehicle detection and localization framework, and employing a Kalman Filter-based late fusion strategy to enhance trajectory consistency and accuracy. In simulation, the fusion algorithm reduced longitudinal error by up to 70 percent compared to individual sensors while preserving lateral accuracy within 1 to 3 meters. Field validation in an active work zone, using LiDAR, a radar-camera rig, and RTK-GPS as ground truth, demonstrated that the fused trajectories closely match real vehicle paths, even when single-sensor data are intermittent or degraded. These results confirm that KF based sensor fusion can reliably compensate for individual sensor limitations, providing precise and robust vehicle tracking capabilities. Our approach thus offers a practical pathway to deploy infrastructure-enabled multi-sensor systems for proactive safety measures in complex traffic environments.

Problem

• Reduces sensor errors in work zone vehicle detection
• Enhances trajectory accuracy using multi-sensor fusion
• Compensates for individual sensor limitations in traffic safety

Innovation

• Integrating camera and LiDAR sensors in cosimulation
• Using Kalman Filter for late fusion strategy
• Achieving precise vehicle tracking in complex environments