New Lane Model and Distance Transform for Lane Detection and Tracking

Particle filtering of boundary points is a robust way to estimate lanes. This paper introduces a new lane model in correspondence to this particle filter-based approach, which is flexible to detect all kinds of lanes. A modified version of an Euclidean distance transform is applied to an edge map of a road image from a birds-eye view to provide information for boundary point detection. An efficient lane tracking method is also discussed. The use of this distance transform exploits useful information in lane detection situations, and greatly facilitates the initialization of the particle filter, as well as lane tracking. Finally, the paper validates the algorithm with experimental evidence for lane detection and tracking.

[1]  Mohan M. Trivedi,et al.  Video-based lane estimation and tracking for driver assistance: survey, system, and evaluation , 2006, IEEE Transactions on Intelligent Transportation Systems.

[2]  ZuWhan Kim,et al.  Robust Lane Detection and Tracking in Challenging Scenarios , 2008, IEEE Transactions on Intelligent Transportation Systems.

[3]  Gamini Dissanayake,et al.  Efficient Lane Detection and Tracking in Urban Environments , 2007, EMCR.

[4]  Gamini Dissanayake,et al.  Robust lane detection in urban environments , 2007, 2007 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[5]  Christoph Stiller,et al.  Kalman Particle Filter for lane recognition on rural roads , 2009, 2009 IEEE Intelligent Vehicles Symposium.

[6]  Li Bai,et al.  Robust Road Modeling and Tracking Using Condensation , 2008, IEEE Transactions on Intelligent Transportation Systems.

[7]  Massimo Bertozzi,et al.  GOLD: a parallel real-time stereo vision system for generic obstacle and lane detection , 1998, IEEE Trans. Image Process..

[8]  Kongqiao Wang,et al.  Video object tracking using improved chamfer matching and condensation particle filter , 2008, Electronic Imaging.