A Novel Vehicle Reversing Speed Control Based on Obstacle Detection and Sparse Representation

In this paper we present a vehicle safety method for reversing speed control based on obstacle detection and sparse representation. The proposed system consists of three main steps, namely, binocular camera obstacle detection and segmentation, obstacle tracking and recognition, and vehicle reversing speed control algorithm. First of all, a binocular camera system is used to detect obstacles as a vehicle is reversing. Using disparity computation and triangulation, we can get all objects' distance information in the rear of a vehicle. Second, the framework of particle filter and sparse representation are used to track and identify the main obstacles such as human or animal bodies, vehicles, or any other objects. Finally, the vehicle reversing speed control algorithm, which controls the electronic throttle opening and automatic braking prior to collisions, makes the reversing control safer and more reliable. This system has been field tested on a Dodge sport utility vehicle by which the final performance evaluation demonstrates the validity of the proposed vehicle reversing speed control.

[1]  Kevin Kok Wai Wong,et al.  Intelligent Automated Guided Vehicle Controller with Reverse Strategy , 2011, J. Adv. Comput. Intell. Intell. Informatics.

[2]  Jean-Yves Bouguet,et al.  Camera calibration toolbox for matlab , 2001 .

[3]  John R. Koza,et al.  A Genetic Approach to Finding a Controller to Back Up a Tractor-Trailer Truck , 1992, 1992 American Control Conference.

[4]  Haibin Ling,et al.  Real time robust L1 tracker using accelerated proximal gradient approach , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[5]  Ran He,et al.  Two-Stage Nonnegative Sparse Representation for Large-Scale Face Recognition , 2013, IEEE Transactions on Neural Networks and Learning Systems.

[6]  Bart Kosko,et al.  Adaptive fuzzy systems for backing up a truck-and-trailer , 1992, IEEE Trans. Neural Networks.

[7]  Jorge L. Martínez,et al.  Steering the Last Trailer as a Virtual Tractor for Reversing Vehicles With Passive On- and Off-Axle Hitches , 2013, IEEE Transactions on Industrial Electronics.

[8]  J. Liang and X.-Y. Peng,et al.  Development and Prospect of Particle Filter , 2010 .

[9]  Rajesh Rajamani,et al.  Model predictive control of transitional maneuvers for adaptive cruise control vehicles , 2004, IEEE Transactions on Vehicular Technology.

[10]  K. Saneyoshi.  Drive assist system using stereo image recognition , 1996, Proceedings of Conference on Intelligent Vehicles.

[11]  Li Bai,et al.  Robust infrared vehicle tracking across target pose change using L1 regularization , 2010, 2010 13th International Conference on Information Fusion.

[12]  Yung-Cheng Chu,et al.  An Efficient Traffic Information Forwarding Solution for Vehicle Safety Communications on Highways , 2012, IEEE Transactions on Intelligent Transportation Systems.

[13]  Woojin Chung,et al.  Reversing Control of a Car with a Trailer Using the Driver Assistance System , 2010, 2010 IEEE Workshop on Advanced Robotics and its Social Impacts.

[14]  Alexander Barth,et al.  Estimating the Driving State of Oncoming Vehicles From a Moving Platform Using Stereo Vision , 2009, IEEE Transactions on Intelligent Transportation Systems.

[15]  Li Bai,et al.  Minimum error bounded efficient ℓ1 tracker with occlusion detection , 2011, CVPR 2011.

[16]  Guo Lie,et al.  Stereo Vision Based Obstacle Avoidance Path-Planning for Cross-Country Intelligent Vehicle , 2009, 2009 Sixth International Conference on Fuzzy Systems and Knowledge Discovery.

[17]  Jian Yang,et al.  Sparse Representation Classifier Steered Discriminative Projection With Applications to Face Recognition , 2013, IEEE Transactions on Neural Networks and Learning Systems.

[18]  Alex Zelinsky,et al.  Learning OpenCV---Computer Vision with the OpenCV Library (Bradski, G.R. et al.; 2008)[On the Shelf] , 2009, IEEE Robotics & Automation Magazine.

[19]  Rafael C. González,et al.  Digital image processing using MATLAB , 2006 .

[20]  Rostam Affendi Hamzah,et al.  Disparity Mapping for Navigation of Stereo Vision Autonomous Guided Vehicle , 2009, 2009 International Conference of Soft Computing and Pattern Recognition.

[21]  Carlos Canudas-de-Wit,et al.  A Safe Longitudinal Control for Adaptive Cruise Control and Stop-and-Go Scenarios , 2007, IEEE Transactions on Control Systems Technology.

[22]  Subhash Challa,et al.  Recent Developments in Vision Based Target Tracking for Autonomous Vehicles Navigation , 2006, 2006 IEEE Intelligent Transportation Systems Conference.

[23]  Jürgen Dickmann,et al.  Results of a Precrash Application Based on Laser Scanner and Short-Range Radars , 2009, IEEE Transactions on Intelligent Transportation Systems.

[24]  Stanley T. Birchfield,et al.  Real-Time Incremental Segmentation and Tracking of Vehicles at Low Camera Angles Using Stable Features , 2008, IEEE Transactions on Intelligent Transportation Systems.

[25]  Haibin Ling,et al.  Robust Visual Tracking and Vehicle Classification via Sparse Representation , 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[26]  B Fleming.  New Automotive Electronics Technologies , 2012 .

[27]  Richard S. Sutton,et al.  The Truck Backer-Upper: An Example of Self-Learning in Neural Networks , 1995 .

[28]  C. Laurgeau,et al.  Vehicle detection combining gradient analysis and AdaBoost classification , 2005, Proceedings. 2005 IEEE Intelligent Transportation Systems, 2005..

[29]  B. Fleming.  New Automotive Electronics Technologies [Automotive Electronics] , 2012, IEEE Vehicular Technology Magazine.

[30]  Jong-Hun Lee,et al.  Stereo vision-based vehicle detection using a road feature and disparity histogram , 2011 .

[31]  M.M. Trivedi,et al.  Human Behavior Based Predictive Brake Assistance , 2006, 2006 IEEE Intelligent Vehicles Symposium.

[32]  Jie Hou,et al.  Measurement of Safe Driving Distance Based on Stereo Vision , 2011, 2011 Sixth International Conference on Image and Graphics.

[33]  Avinash C. Kak,et al.  Vision for Mobile Robot Navigation: A Survey , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[34]  Paolo Grisleri,et al.  Off-Road Path and Obstacle Detection Using Decision Networks and Stereo Vision , 2007, IEEE Transactions on Intelligent Transportation Systems.

[35]  Bart De Schutter,et al.  IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS Editor-In-Chief , 2005 .

[36]  Yuan-Hsiang Chang,et al.  A study of 3D feature tracking and localization using a stereo vision system , 2010, 2010 International Computer Symposium (ICS2010).

[37]  Tiberiu Marita.  Barriers detection method for stereovision-based ACC systems , 2009, 2009 IEEE 5th International Conference on Intelligent Computer Communication and Processing.