Cost-effective Vehicle Recognition System in Challenging Environment Empowered by Micro-Pulse LiDAR and Edge AI
Vehicle recognition and classification are critical for a number of traffic applications, e.g., traffic signal control, traffic flow modeling, tolling, and logistics optimization. Commonly used sensing systems are mainly counted on in-pavement loops or surveillance video cameras, while both of them have their inherent limitations. Leveraging micro high-speed pulse LiDAR mounted overhead of travel lanes, this study proposes Compact LiDAR Empowered Vehicle Enhancing-minority Recognition (CLEVER) system, a real-time cost-effective vehicle detection and classification framework that is empowered by edge Artificial Intelligence (AI). Based on the customized minority-enhancing vehicle classification deep neural network, the CLEVER system outperforms cutting-edge LiDAR-based vehicle classification methods up to 15.98% true-positive rate in classifying ten types of vehicles. Furthermore, by highly integrating the hardware, the pre-processing algorithm and the classification neural network into an edge computing node, the CLEVER system only consumes 10% of the cost in LiDAR systems and works perfectly in a plug-and-play mode with a negligible sub-second inference time (212ms to 459ms). The proposed CLEVER system offers an affordable end-to-end solution that can benefit traffic operators by collecting more accurate and reliable vehicle classification data streams and that can lead to a more efficient and flexible ITS.
