Real-time point-cloud-based vehicle fleet monitoring system on the edge, edge computing, and deep learning technique

This is the authors' abstract. We don't add key points for this paper.

Intelligent Transportation Systems (ITS) increasingly rely on real-time 3-Dimensional (3D) data for vehicle dynamics, making edge computing crucial for timely and scalable 3D object detection, vehicle tracking, and counting. This paper introduces a real-time vehicle fleet monitoring system operating at 10 Hertz with a latency of 62.71 ms using advanced hardware and software. The primary objective of “fleet monitoring” is accurate vehicle counting and classification across diverse vehicle types. The hardware consists of a multi-beam flash Light Detection and Ranging (LiDAR), and an edge computing device. The software architecture is comprised of four modules: (1) a Graphics Processing Unit (GPU)-accelerated sensor interface module, which effectively processes high-density LiDAR point clouds by filtering out irrelevant background data and extracting regions of interest (RoIs); (2) Dynamic Voxelization Detector (DV-Det), a 3D object detection model which identifies and categorizes various vehicle types. It demonstrated exceptional performance, achieving 75 Hertz on the Karlsruhe Institute of Technology and Toyota Technological Institute (KITTI) dataset and surpassing bird’s eye view (BEV)-based methods in all evaluation metrics; (3) A-Baseline-for-3D-Multi-Object-Tracking (AB3DMOT) algorithm, a robust multi-vehicle tracking module; (4) a specialized multi-vehicle counting algorithm, tailored for accurate vehicle enumeration in 3D environments. Field experiments validate the system’s capability to perform fine-grain vehicle classification with high precision and recall, achieving at least 80 Mean Average Precision (mAP) at a 3D Intersection-over-Union (IoU) threshold of 0.5 for most vehicle classes, along with a recall of 100% and a precision of at least 80% for the majority of the 10 fine-grain vehicle classes.