TY - GEN
T1 - Multiple moving target tracking with hypothesis trajectory model for autonomous vehicles
AU - Mei, Weijie
AU - Xiong, Guangming
AU - Gong, Jianwei
AU - Yong, Zhai
AU - Chen, Huiyan
AU - Di, Huijun
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/7/2
Y1 - 2017/7/2
N2 - Detecting and tracking moving objects is a key technology for autonomous driving vehicle in dynamic urban environment. In this paper, we present a hybrid system of multiple moving target tracking (MMTT) for autonomous driving vehicles using 3D-Lidar sensor. To detect targets, geometric model-based method is adopted in the hybrid system. In further, we propose a novel hypothesis trajectory model to detect moving targets. The targets detected by the two methods are fused under Extend Kalman Filter. Besides, a bipartite graph model-based method with the Hungary algorithm is presented to optimize data association matrix. Our tracking algorithm is tested on school road and Beijing's 3rd ring road using an experimental vehicle with velodyne-32-E. And, the experiment results illustrate the hybrid method performs well in real time.
AB - Detecting and tracking moving objects is a key technology for autonomous driving vehicle in dynamic urban environment. In this paper, we present a hybrid system of multiple moving target tracking (MMTT) for autonomous driving vehicles using 3D-Lidar sensor. To detect targets, geometric model-based method is adopted in the hybrid system. In further, we propose a novel hypothesis trajectory model to detect moving targets. The targets detected by the two methods are fused under Extend Kalman Filter. Besides, a bipartite graph model-based method with the Hungary algorithm is presented to optimize data association matrix. Our tracking algorithm is tested on school road and Beijing's 3rd ring road using an experimental vehicle with velodyne-32-E. And, the experiment results illustrate the hybrid method performs well in real time.
KW - Extend Kalman Filter
KW - autonomous driving vehicle
KW - data association
KW - hypothesis trajectory model
KW - multiple moving target tacking
UR - http://www.scopus.com/inward/record.url?scp=85046291229&partnerID=8YFLogxK
U2 - 10.1109/ITSC.2017.8317845
DO - 10.1109/ITSC.2017.8317845
M3 - Conference contribution
AN - SCOPUS:85046291229
T3 - IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC
SP - 1
EP - 6
BT - 2017 IEEE 20th International Conference on Intelligent Transportation Systems, ITSC 2017
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 20th IEEE International Conference on Intelligent Transportation Systems, ITSC 2017
Y2 - 16 October 2017 through 19 October 2017
ER -