Modular architecture for traffic monitoring systems using 3D LiDAR sensors

Urban growth has generated an increasing demand for robust and accurate traffic monitoring systems. Traditional technologies such as inductive loops and computer vision have limitations under adverse environmental conditions and in high-density traffic scenarios. This work proposes a modular architecture for traffic monitoring based exclusively on 3D Light Detection and Ranging (LiDAR) sensors, which stand out for their high accuracy and resilience to light and weather variations. The proposed architecture consists of eight independent modular levels. Its main innovations include optimized methods for background subtraction, real-time detection algorithms using machine learning with subsampling techniques, a multi-object tracking system that preserves vehicle identity in the face of occlusions, and a decision tree-based classifier that assigns vehicle type based on geometric characteristics. The solution was validated in real conditions on the M-13 motorway in Madrid (Spain) over more than 100 h of data recordings, using an instrumented gantry with a 3D LiDAR that supervises two-lane roadway, evaluating critical attributes for traffic management such as vehicle detection, classification, and tracking. The proposed design facilitates scalability and compatibility with various sensors, enabling advanced applications in traffic monitoring, cooperative connected vehicle (vehicle-to-infrastructure) contexts, high-level automated driving, and smart highways.