Uncertainty-Aware Multi-Vehicle Detection and Tracking Using Roadside 3D Point Clouds

This paper presents a novel, high-performance multi-vehicle detection and tracking (MVDT) framework to extract vehicle trajectories from roadside 3D point clouds. First, we developed a vehicle detector called the deep evidential occupancy grid model. This model uses PointNet to extract features from vertically organized point cloud pillars, generating a bird’s-eye view (BEV) grid feature map, followed by a 2D convolutional neural network to capture both local and global spatial features. The detection head uses a Dirichlet distribution to gather “evidence” from gathered features that indicate grid occupancy or vacancy, quantifying the model’s “confidence” through a probabilistic representation of uncertainty. An adaptive post-processing method is applied to output detection results with associated uncertainty. Second, we integrated uncertainty into the tracker, developing an uncertainty-aware multi-vehicle tracking (UMVT) model. This model manages the trajectory initialization and termination strategies, enhancing tracking robustness in complex scenarios. Comprehensive experiments on two real-world datasets, DAIR-V2X and V2X-Real, demonstrate that the proposed MVDT framework outperforms state-of-the-art methods in roadside perception, achieving improvements in both detection and tracking tasks. The effectiveness has also been validated using point cloud data from the Shanghai-Nanjing Expressway, showcasing its excellent practical applicability.