Towards an approach for knowledge-based road detection
Proceedings of the 2005 ACM workshop on Research in knowledge representation for autonomous systems
Cost-effective video filtering solution for real-time vision systems
EURASIP Journal on Applied Signal Processing
An extended hyperbola model for road tracking for video-based personal navigation
Knowledge-Based Systems
Dynamic Tracking System through PSO and Parzen Particle Filter
KES '09 Proceedings of the 13th International Conference on Knowledge-Based and Intelligent Information and Engineering Systems: Part II
Situation assessment for automatic lane-change maneuvers
IEEE Transactions on Intelligent Transportation Systems
A DSP-based lane departure warning system
MMACTEE'06 Proceedings of the 8th WSEAS international conference on Mathematical methods and computational techniques in electrical engineering
A novel system for robust lane detection and tracking
Signal Processing
Adaptative road lanes detection and classification
ACIVS'06 Proceedings of the 8th international conference on Advanced Concepts For Intelligent Vision Systems
A robust lane detection approach based on MAP estimate and particle swarm optimization
CIS'05 Proceedings of the 2005 international conference on Computational Intelligence and Security - Volume Part II
Monte Carlo algorithm for trajectory optimization based on Markovian readings
Computational Optimization and Applications
A novel driving pattern recognition and status monitoring system
PSIVT'06 Proceedings of the First Pacific Rim conference on Advances in Image and Video Technology
Accurate 3D-vision-based obstacle detection for an autonomous train
Computers in Industry
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This paper presents a method designed to track and to recover the three-dimensional (3-D) shape of a road by computer vision. The method is based first upon an accurate detection algorithm which provides a reliable estimation of the roadside in the image. This algorithm works by recursive updating of a statistical model of the lane obtained by an off-line training phase. Once the sides have been located, a reconstruction algorithm computes the vehicle location on its lane, the 3-D shape of the road, and gives both the sides location and their confidence interval for the next image. The detection algorithm then looks for the roadside in this interval in order to limit the computational times, which are about 30-150 ms on a HP workstation.