Real-Time Roadside 3-D Spatial Perception With LiDAR AIoT: An Edge-Cloud–Terminal Collaborative Sensing Prototype

Abstract

In vehicle-road cooperation, the advancement of vehicle-side autonomous driving is hindered by bottlenecks, such as limited sensing range, computational power, and environmental adaptability. Collaboration with roadside units is essential for achieving more accurate and complex spatial perception. This study presents a new prototype to enhancing spatial perception in road environments within Augmented Intelligence of Things (AIoT) systems using light detection and ranging (LiDAR) technology. Unlike traditional AIoT systems, which rely on cameras and struggle in complex conditions, the proposed prototype uses an edge-cloud–terminal collaborative sensing model to enhance 3-D spatial perception. A notable feature of this prototype is the integration of the distance and density adaptive filtering (DDAF) method, which ensures efficient point cloud filtering at the edge, with an average F1-score of 96.03% and an average latency of 11.78 ms, demonstrating strong accuracy and low latency across various scenarios. The incorporation of DDAF further improves the mean average precision (mAP) of deep learning-based 3-D object detection on the cloud by 2.34%, reduces processing time by 83.54%, and decreases peak memory usage by 90.18%, facilitating precise 3-D spatial analysis. The final results are displayed in real-time on the terminal for visualization and interaction. The efficacy of this prototype is demonstrated through a real-world case study. This research highlights the role of LiDAR and AIoT in overcoming spatial perception challenges in vehicle-road cooperation, leading to safer, more efficient transportation solutions.