A Novel Multi-Sensor Environmental Perception Method Using Low-Rank Representation and a Particle Filter for Vehicle Reversing Safety.
Environmental perception and information processing are two key steps of active safety for vehicle reversing. Single-sensor environmental perception cannot meet the need for vehicle reversing safety due to its low reliability. In this paper, we present a novel multi-sensor environmental perception method using low-rank representation and a particle filter for vehicle reversing safety. The proposed system consists of four main steps, namely multi-sensor environmental perception, information fusion, target recognition and tracking using low-rank representation and a particle filter, and vehicle reversing speed control modules. First of all, the multi-sensor environmental perception module, based on a binocular-camera system and ultrasonic range finders, obtains the distance data for obstacles behind the vehicle when the vehicle is reversing. Secondly, the information fusion algorithm using an adaptive Kalman filter is used to process the data obtained with the multi-sensor environmental perception module, which greatly improves the robustness of the sensors. Then the framework of a particle filter and low-rank representation is used to track the main obstacles. The low-rank representation is used to optimize an objective particle template that has the smallest L-1 norm. Finally, the electronic throttle opening and automatic braking is under control of the proposed vehicle reversing control strategy prior to any potential collisions, making the reversing control safer and more reliable. The final system simulation and practical testing results demonstrate the validity of the proposed multi-sensor environmental perception method using low-rank representation and a particle filter for vehicle reversing safety.
School of Mechanical Engineering, Southwest Jiaotong University, Chengdu 610031, China. zzt@swjtu.edu.cn.
Zutao
Z
Zhang
School of Information Science &amp; Technical, Southwest Jiaotong University, Chengdu 610031, China. liyanjun@my.swjtu.edu.cn.
Yanjun
Y
Li
School of Information Science &amp; Technical, Southwest Jiaotong University, Chengdu 610031, China. fbwwang@163.com.
Fubing
F
Wang
School of Mechanical Engineering, Southwest Jiaotong University, Chengdu 610031, China. meng_guanjun@my.swjtu.edu.cn.
Guanjun
G
Meng
School of Mechanical Engineering, Southwest Jiaotong University, Chengdu 610031, China. wldsalman6@gmail.com.
Waleed
W
Salman
School of Mechanical Engineering, Southwest Jiaotong University, Chengdu 610031, China. Leo_792004@yahoo.com.
Layth
L
Saleem
School of Information Science &amp; Technical, Southwest Jiaotong University, Chengdu 610031, China. zxl627905@163.com.
Xiaoliang
X
Zhang
Department of Industrial &amp; Manufacturing Systems Engineering, Iowa State University, Ames, IA 50011, USA. chbwang@iastate.edu.
Chunbai
C
Wang
School of Mechanical Engineering, Southwest Jiaotong University, Chengdu 610031, China. ghu@home.swjtu.edu.cn.
Guangdi
G
Hu
School of Transportation &amp; Logistics, Southwest Jiaotong University, Chengdu 610031, China. liuyugang@swjtu.edu.cn.
Yugang
Y
Liu
adaptive Kalman filter; information fusion; low-rank representation; multi-sensors; particle filter; vehicle reversing control