Multiple Vehicle Tracking Based on Labeled Multiple Bernoulli Filter Using Pre-Clustered Laser Range Finder Data
RIS ID
141061
Abstract
1967-2012 IEEE. Multiple vehicle tracking (MVT) system is a prerequisite to path planning and decision making of self-driving cars as it can provide positions of surrounding vehicles. Most of the available approaches belonging to the so called tracking-by-detection approach inevitably bring detection errors into the tracking result. In this study, we proposed a laser range finder (LRF) based track-before-detect MVT algorithm without detection procedure. Moreover, different from the state of the art in track-before-detect approaches using raw data, we applied a pre-clustering procedure to segment the raw data into disjoint clusters to reduce computation demand. Specifically, a clustering algorithm named iterative nearest point search (INPS) which can even handle the partial occlusion situations that are challenging for traditional clustering algorithms was designed for the pre-clustering procedure. Furthermore, a detailed cluster-to-target measurement model was proposed to describe the difference between cluster and hypothesis vehicle. Finally, we integrated the measurement model into the labeled multi-Bernoulli filter with particle implementation. Simulations and experiments show that the proposed MVT algorithm provides more accurate estimates of vehicle number and position in comparison with conventional methods.
Publication Details
K. Dai, Y. Wang, Q. Ji, H. Du & C. Yin, "Multiple Vehicle Tracking Based on Labeled Multiple Bernoulli Filter Using Pre-Clustered Laser Range Finder Data," IEEE Transactions on Vehicular Technology, vol. 68, (11) pp. 10382-10393, 2019.