Recursive 3-D Road and Relative Ego-State Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence - Special issue on interpretation of 3-D scenes—part II
CONDENSATION—Conditional Density Propagation forVisual Tracking
International Journal of Computer Vision
Robust Lane Detection and Tracking in Challenging Scenarios
IEEE Transactions on Intelligent Transportation Systems
Track detection for autonomous trains
ISVC'10 Proceedings of the 6th international conference on Advances in visual computing - Volume Part III
Robotics and Autonomous Systems
Keeping the vehicle on the road: A survey on on-road lane detection systems
ACM Computing Surveys (CSUR)
Recent progress in road and lane detection: a survey
Machine Vision and Applications
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Accurate and robust lane results are of great significance in any driving-assistance system. To achieve robustness and accuracy in difficult scenarios, probabilistic estimation techniques are needed to compensate for the errors in the detection of lane-delimiting features. This paper presents a solution for lane estimation in difficult scenarios based on the particle-filtering framework. The solution employs a novel technique for pitch detection based on the fusion of two stereovision-based cues, a novel method for particle measurement and weighing using multiple lane-delimiting cues extracted by grayscale and stereo data processing, and a novel method for deciding upon the validity of the lane-estimation results. Initialization samples are used for uniform handling of the road discontinuities, eliminating the need for explicit track initialization. The resulting solution has proven to be a reliable and fast lane detector for difficult scenarios.