Commenced in January 2007
Paper Count: 32579
Vehicle Position Estimation for Driver Assistance System
Abstract:We present a system that finds road boundaries and constructs the virtual lane based on fusion data from a laser and a monocular sensor, and detects forward vehicle position even in no lane markers or bad environmental conditions. When the road environment is dark or a lot of vehicles are parked on the both sides of the road, it is difficult to detect lane and road boundary. For this reason we use fusion of laser and vision sensor to extract road boundary to acquire three dimensional data. We use parabolic road model to calculate road boundaries which is based on vehicle and sensors state parameters and construct virtual lane. And then we distinguish vehicle position in each lane.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1333146Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1555
 JC McCall, MM Trivedi, "Video-based lane estimation and tracking for driver assistance: survey, system, and evaluation", IEEE transactions on intelligent transportation systems, Vol. 7, pp. 20, 2006.
 Wang, C. Hu, Z. Uchimura, K. "Precise curvature estimation by cooperating with digital road map", IEEE Intelligent Vehicles Symposium, pp. 859-864, 2008.
 Camillo J. Taylor . "A Comparative Study of Vision-Based Lateral Control Strategies for Autonomous Highway Driving", The International Journal of Robotics Research, Vol. 18, No. 5, 442-453, 1999.
 Goldbeck, J. Huertgen, B. "Lane detection and tracking by video sensors", Intelligent Transportation Systems, pp. 74-79, 1999.
 Bertozzi, M. Broggi, A. "GOLD: a parallel real-time stereo vision system for genericobstacle and lane detection", IEEE Transactions on Image Processing, Vol. 7, pp. 62-81, 1998.
 W.S. Wijesoma, K. R. S. Kodagoda,. A. P. Balasurya, and E. K. Teoh. "Laser and camera for road edge and midline detection", Robot Motion and Control, 2001 Proceedings of the Second International Workshop, 269-274, October, 2001.
 Viola and Jones, "Rapid object detection using boosted cascade of simple features", Computer Vision and Pattern Recognition, 2001.
 C. Papageorgiou, M. Oren, and T. Poggio. A general framework for object detection. In International Conference on Computer Vision, 1998.
 Y.Freund and R. E. Schapire. "A decisiontheoretic generalization of on-line learning and an application to boosting." In European Conference on Computational Learning Theory, 1995.