Likelihood-Field-Model-Based Dynamic Vehicle Detection and Tracking for Self-Driving

Abstract

Dynamic vehicle detection and tracking is crucial for self-driving in urban environments. The main problem of the previous beam-model-based algorithms is that they cannot detect and track dynamic vehicles that are occluded by other objects. In this paper, we develop a novel dynamic vehicle detection and tracking algorithm to solve this problem for our autonomous land vehicle (ALV), which is equipped with a Velodyne LIDAR and a GPS-aid inertial navigation system. For detection, our improved two-dimensional virtual scan is presented to detect the potential dynamic vehicles with a scan differencing operation. Then, for each potential dynamic vehicle, a novel likelihood-field-based vehicle measurement model is proposed to weight its possible poses. Finally, our newly modified scaling series algorithm and the importance sampling technique are adopted to estimate the initial pose and the corresponding velocity for each vehicle, respectively. The scaling series algorithm coupled with a Bayesian filter (SSBF) was previously used to handle the tactile localization problem in static background scenes. For tracking dynamic vehicles, we improve the SSBF by adding the ego-motion compensation so that the improved algorithm is able to update the pose and velocity for each vehicle in dynamic background scenes. Both the quantitative and qualitative experimental results validate the performance of our dynamic vehicle detection and tracking algorithm on the KITTI datasets and the Velodyne data collected by our ALV in dynamic urban environments.