Finding multiple lanes in urban road networks with vision and lidar
Autonomous Robots
Probabilistic lane tracking in difficult road scenarios using stereovision
IEEE Transactions on Intelligent Transportation Systems
Ego-Vehicle Corridors for Vision-Based Driver Assistance
IWCIA '09 Proceedings of the 13th International Workshop on Combinatorial Image Analysis
Lane boundary and curb estimation with lateral uncertainties
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
CyberC3: a prototype cybernetic transportation system for urban applications
IEEE Transactions on Intelligent Transportation Systems
Robotics and Autonomous Systems
Crossing road monitoring system based on adaptive decision for illegal situation
Applied Soft Computing
Probabilistic lane estimation for autonomous driving using basis curves
Autonomous Robots
Lane detection in critical shadow conditions based on double A/D convertors camera
AICI'11 Proceedings of the Third international conference on Artificial intelligence and computational intelligence - Volume Part III
eyeDog: an assistive-guide robot for the visually impaired
Proceedings of the 4th International Conference on PErvasive Technologies Related to Assistive Environments
A new smartphone lane detection system: realizing true potential of multi-core mobile devices
Proceedings of the 4th Workshop on Mobile Video
A real-time system of lane detection and tracking based on optimized RANSAC B-spline fitting
Proceedings of the 2013 Research in Adaptive and Convergent Systems
Keeping the vehicle on the road: A survey on on-road lane detection systems
ACM Computing Surveys (CSUR)
Lane mark segmentation and identification using statistical criteria on compressed video
Integrated Computer-Aided Engineering
Recent progress in road and lane detection: a survey
Machine Vision and Applications
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A lane-detection system is an important component of many intelligent transportation systems. We present a robust lane-detection-and-tracking algorithm to deal with challenging scenarios such as a lane curvature, worn lane markings, lane changes, and emerging, ending, merging, and splitting lanes. We first present a comparative study to find a good real-time lane-marking classifier. Once detection is done, the lane markings are grouped into lane-boundary hypotheses. We group left and right lane boundaries separately to effectively handle merging and splitting lanes. A fast and robust algorithm, based on random-sample consensus and particle filtering, is proposed to generate a large number of hypotheses in real time. The generated hypotheses are evaluated and grouped based on a probabilistic framework. The suggested framework effectively combines a likelihood-based object-recognition algorithm with a Markov-style process (tracking) and can also be applied to general-part-based object-tracking problems. An experimental result on local streets and highways shows that the suggested algorithm is very reliable.