Semantic Point Cloud-Based Adaptive Multiple Object Detection and Tracking for Autonomous Vehicles

Abstract

LiDAR-based Multiple Object Detection and Tracking (MODT) is one of the essential tasks in autonomous driving. Since MODT is directly related to the safety of an autonomous vehicle, it is critical to provide reliable information about the surrounding objects. For that reason, we propose a semantic point cloud-based adaptive MODT system for autonomous driving. Semantic point clouds emerge with advances in deep learning-based Point Cloud Semantic Segmentation (PCSS), which assigns semantic information to each point in the point cloud of LiDAR. This semantic information provides several advantages to the MODT system. First, any point corresponding to any static object can be filtered. Because the class information assigned to each point can be directly utilized, filtering is possible without any modeling. Second, the class information of an object can be inferred without any special classification process because the class information is provided from the semantic point cloud. Finally, the clustering and tracking module can consider unique dimensional and dynamic characteristics based on class information. We utilize the Carla simulator and KITTI dataset to verify our method by comparing several existing algorithms. In conclusion, the accuracy of the proposed algorithm is improved by about 176% on average compared to the existing algorithm.

Keywords

• Semantic point cloud
• point cloud semantic segmentation
• multiple object detection and tracking (MODT)
• class-adaptive tracking
• autonomous vehicle
• LiDAR