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Sensors 2016, 16(6), 848; doi:10.3390/s16060848

A Novel Multi-Sensor Environmental Perception Method Using Low-Rank Representation and a Particle Filter for Vehicle Reversing Safety

1
School of Mechanical Engineering, Southwest Jiaotong University, Chengdu 610031, China
2
School of Information Science & Technical, Southwest Jiaotong University, Chengdu 610031, China
3
Department of Industrial & Manufacturing Systems Engineering, Iowa State University, Ames, IA 50011, USA
4
School of Transportation & Logistics, Southwest Jiaotong University, Chengdu 610031, China
These authors contributed equally to this work.
*
Author to whom correspondence should be addressed.
Academic Editor: Felipe Jimenez
Received: 9 March 2016 / Revised: 22 May 2016 / Accepted: 1 June 2016 / Published: 9 June 2016
(This article belongs to the Special Issue Sensors for Autonomous Road Vehicles)

Abstract

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. View Full-Text
Keywords: multi-sensors; information fusion; adaptive Kalman filter; particle filter; low-rank representation; vehicle reversing control multi-sensors; information fusion; adaptive Kalman filter; particle filter; low-rank representation; vehicle reversing control
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Zhang, Z.; Li, Y.; Wang, F.; Meng, G.; Salman, W.; Saleem, L.; Zhang, X.; Wang, C.; Hu, G.; Liu, Y. A Novel Multi-Sensor Environmental Perception Method Using Low-Rank Representation and a Particle Filter for Vehicle Reversing Safety. Sensors 2016, 16, 848.

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