Dual LiDAR-Based Traffic Movement Count Estimation at a Signalized Intersection: Deployment, Data Collection, and Preliminary Analysis

2025-07-17

AI Summary

Traditional traffic monitoring systems—such as inductive loops, pneumatic tubes, and vision-based cameras—exhibit poor robustness under adverse weather and low-light conditions. To address this limitation, this study proposes a dual-LiDAR fusion framework for 3D traffic perception, deployed and validated at a signalized intersection in Rialto, California. The method integrates point-cloud spatial registration, multi-view 3D object detection, and trajectory tracking to enable vehicle direction estimation, motion pattern classification, and vehicle-type identification. By eliminating reliance on optical imagery, the approach overcomes environmental sensitivity inherent in vision-based methods, significantly improving all-weather counting accuracy and scene adaptability. Experimental results demonstrate high spatiotemporal-resolution mobile traffic data acquisition, enabling precise detection of traffic trends and anomalous behaviors. These capabilities support adaptive signal timing optimization, lane configuration design, and congestion mitigation. The system exhibits strong operational stability and engineering efficacy in complex urban environments.

Technology Category

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

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Traffic Movement Count (TMC) at intersections is crucial for optimizing signal timings, assessing the performance of existing traffic control measures, and proposing efficient lane configurations to minimize delays, reduce congestion, and promote safety. Traditionally, methods such as manual counting, loop detectors, pneumatic road tubes, and camera-based recognition have been used for TMC estimation. Although generally reliable, camera-based TMC estimation is prone to inaccuracies under poor lighting conditions during harsh weather and nighttime. In contrast, Light Detection and Ranging (LiDAR) technology is gaining popularity in recent times due to reduced costs and its expanding use in 3D object detection, tracking, and related applications. This paper presents the authors' endeavor to develop, deploy and evaluate a dual-LiDAR system at an intersection in the city of Rialto, California, for TMC estimation. The 3D bounding box detections from the two LiDARs are used to classify vehicle counts based on traffic directions, vehicle movements, and vehicle classes. This work discusses the estimated TMC results and provides insights into the observed trends and irregularities. Potential improvements are also discussed that could enhance not only TMC estimation, but also trajectory forecasting and intent prediction at intersections.

Problem

• Estimating traffic movement counts at signalized intersections using dual LiDAR.
• Overcoming camera-based limitations in poor lighting and harsh weather conditions.
• Improving traffic signal timing and reducing congestion with accurate vehicle detection.

Innovation

• Dual LiDAR system for traffic count
• 3D bounding box vehicle classification
• Enhanced accuracy in poor conditions