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Improved Perception for Automated Vehicle Using Multi-Pose Camera System
Technical Paper
2017-01-1401
ISSN: 0148-7191, e-ISSN: 2688-3627
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English
Abstract
In this paper, a method of improving the automated vehicle’s perception using a multi-pose camera system (MPCS) is presented. The proposed MPCS is composed of two identical colored and high frame-rate cameras: one installed in the driver side and the other in the passenger side. Perspective of MPCS varies depending on the width of vehicle type in which MPCS is installed. To increase perspective, we use the maximum width of the host vehicle as camera to camera distance for the MPCS. In addition, angular positions of the two cameras in MPCS are controlled by two separate electric motor-based actuators. Steering wheel angle, which is available from the vehicle Controller Area Network (CAN) messages, is used to supply information to the actuators to synchronize MPCS camera positions with the host vehicle steering wheel. By synchronizing MPCS with the steering wheel angle, MPCS can track the host vehicle’s steering wheel more closely and then position its individual camera versus the steering wheel to acquire wide-perspective and useful information to feed the host vehicle. Furthermore, in MPCS, stream of images from the two cameras are processed using image fusion and three dimensional (3D) estimation techniques. MPCS composes multiple images from different perspectives, performing depth estimation of the scene and supplying the host vehicle with compounded broad perspective information about the environment around it. Compared with the single cameras or traditional cameras, MPCS can benefit the host vehicle with improved perception about its driving environment. Vision for the host vehicle therefore can be enhanced. The proposed MPCS has been validated by simulation in Matlab Simulink. Experiments have also been performed to confirm effectiveness of the proposed MPCS.
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Nguyen, T., Lull, J., and Vaishnav, S., "Improved Perception for Automated Vehicle Using Multi-Pose Camera System," SAE Technical Paper 2017-01-1401, 2017, https://doi.org/10.4271/2017-01-1401.Also In
References
- Lalonde , J. et al. Single-view obstacle detection for smart backup camera systems 2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops 2012
- Stamenkovic , Z. et al. Rear view camera system for car driving assistance Microelectronics (MIEL), 2012 28th International Conference on 2012
- Polychronopoulos , A. et al. Extended path prediction using camera and map data for lane keeping support Proceedings. 2005 IEEE Intelligent Transportation Systems, 2005 2005
- Htet , K.K.K. , Kiong T.K. , and Xinxin. D. Comprehensive lane keeping system with mono camera Control Conference (ASCC), 2015 10th Asian 2015
- blind spot detection and AP https://forums.tesla.com/forum/forums/blind-spot-detection-and-ap
- Sun , J. et al. Symmetric Stereo Matching for Occlusion Handling Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02 2005 IEEE Computer Society 399 406
- Garcia , E. and Altamirano. L. Multiple camera fusion based on DSmT for tracking objects on ground plane Information Fusion, 2008 11th International Conference on 2008
- Lin , D.T. and Huang. K.Y. Collaborative pedestrian tracking with multiple cameras: Data fusion and visualization The 2010 International Joint Conference on Neural Networks (IJCNN) 2010
- Huang , C.M. and Yang. T.Y. Line detecting and tracking of a mobile robot with multiple RGB-D cameras 2016 IEEE 13th International Conference on Networking, Sensing, and Control (ICNSC) 2016
- Saxena , A. , Chung S.H. , and Ng A.Y. 3-D Depth Reconstruction from a Single Still Image International Journal of Computer Vision 2008 76 1 53 69
- Liu , B. , Gould S. , and Koller. D. Single image depth estimation from predicted semantic labels Computer Vision and Pattern Recognition (CVPR), 2010 IEEE Conference on 2010
- De Cubber , G. et al. Combining Dense Structure from Motion and Visual SLAM in a Behavior-Based Robot Control Architecture International Journal of Advanced Robotic Systems 2010 7 1
- Newcombe , R.A. , Lovegrove S.J. , and Davison A.J. DTAM: Dense tracking and mapping in real-time Proceedings of the 2011 International Conference on Computer Vision 2011 IEEE Computer Society 2320 2327
- Klaus , A. , Sormann M. , and Karner K. Segment-Based Stereo Matching Using Belief Propagation and a Self-Adapting Dissimilarity Measure Proceedings of the 18th International Conference on Pattern Recognition - Volume 03 2006 IEEE Computer Society 15 18