Vehicle Position Estimation for Driver Assistance System
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.
[1] 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.
[2] Wang, C. Hu, Z. Uchimura, K. "Precise curvature estimation by
cooperating with digital road map", IEEE Intelligent Vehicles
Symposium, pp. 859-864, 2008.
[3] 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.
[4] Goldbeck, J. Huertgen, B. "Lane detection and tracking by video
sensors", Intelligent Transportation Systems, pp. 74-79, 1999.
[5] 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.
[6] 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.
[7] Viola and Jones, "Rapid object detection using boosted cascade of simple
features", Computer Vision and Pattern Recognition, 2001.
[8] C. Papageorgiou, M. Oren, and T. Poggio. A general framework for object
detection. In International Conference on Computer Vision, 1998.
[9] 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.
[1] 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.
[2] Wang, C. Hu, Z. Uchimura, K. "Precise curvature estimation by
cooperating with digital road map", IEEE Intelligent Vehicles
Symposium, pp. 859-864, 2008.
[3] 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.
[4] Goldbeck, J. Huertgen, B. "Lane detection and tracking by video
sensors", Intelligent Transportation Systems, pp. 74-79, 1999.
[5] 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.
[6] 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.
[7] Viola and Jones, "Rapid object detection using boosted cascade of simple
features", Computer Vision and Pattern Recognition, 2001.
[8] C. Papageorgiou, M. Oren, and T. Poggio. A general framework for object
detection. In International Conference on Computer Vision, 1998.
[9] 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.
@article{"International Journal of Electrical, Electronic and Communication Sciences:56159", author = "Hyun-Koo Kim and Sangmoon Lee and Ho-Youl Jung and Ju H. Park", title = "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.", keywords = "Vehicle Detection, Adaboost, Haar-like Feature,Road Boundary Detection", volume = "4", number = "8", pages = "1163-5", }