Road Boundary Detection using In-vehicle Monocular Camera

When a lane marker such as a white line is not drawn on the road or it’s hidden by snow, it’s important for the lateral motion control of the vehicle to detect the boundary line between the road and the roadside object such as curbs, grasses, side walls and so on. Especially, when the road is covered with snow, it’s necessary to detect the boundary between the snow side wall and the road because other roadside objects are occluded by snow. In this paper, we proposes the novel method to detect the shoulder line of a road including the boundary with the snow side wall from an image of an in-vehicle monocular camera. Vertical lines on an object whose height is different from a road surface are projected onto slanting lines when an input image is mapped to a road surface by the inverse perspective mapping. The proposed method detects a road boundary using this characteristic. In order to cope with the snow surface where various textures appear, we introduce the degree of road boundary that responds strongly at the boundary with the area where slant edges are dense. Since the shape of the snow wall is complicated, the boundary line is extracted by the Snakes using the degree of road boundary as image forces. Experimental results using the KITTI dataset and our own dataset including snow road show the effectiveness of the proposed method.

[1]  Wolfram Burgard,et al.  Efficient deep models for monocular road segmentation , 2016, 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

[2]  Alexei A. Efros,et al.  Recovering Surface Layout from an Image , 2007, International Journal of Computer Vision.

[3]  Ronen Lerner,et al.  Recent progress in road and lane detection: a survey , 2012, Machine Vision and Applications.

[4]  Matthew Turk,et al.  VITS-A Vision System for Autonomous Land Vehicle Navigation , 1988, IEEE Trans. Pattern Anal. Mach. Intell..

[5]  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.

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

[7]  Massimo Bertozzi,et al.  Artificial vision in road vehicles , 2002, Proc. IEEE.

[8]  Ankit Laddha,et al.  Map-supervised road detection , 2016, 2016 IEEE Intelligent Vehicles Symposium (IV).

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

[10]  Ethan Fetaya,et al.  StixelNet: A Deep Convolutional Network for Obstacle Detection and Road Segmentation , 2015, BMVC.

[11]  H.-H. Nagel,et al.  Texture-based segmentation of road images , 1994, Proceedings of the Intelligent Vehicles '94 Symposium.

[12]  Mirko Meuter,et al.  A novel approach to lane detection and tracking , 2009, 2009 12th International IEEE Conference on Intelligent Transportation Systems.

[13]  Arthur Daniel Costea,et al.  Traffic scene segmentation based on boosting over multimodal low, intermediate and high order multi-range channel features , 2017, 2017 IEEE Intelligent Vehicles Symposium (IV).

[14]  Yann LeCun,et al.  Road Scene Segmentation from a Single Image , 2012, ECCV.

[15]  Uwe Franke,et al.  Efficient representation of traffic scenes by means of dynamic stixels , 2010, 2010 IEEE Intelligent Vehicles Symposium.

[16]  Ho Gi Jung,et al.  Noise-resilient road surface and free space estimation using dense stereo , 2013, 2013 IEEE Intelligent Vehicles Symposium (IV).

[17]  Yoshiko Kojima,et al.  CADAS: A multimodal advanced driver assistance system for normal urban streets based on road context understanding , 2013, 2013 IEEE Intelligent Vehicles Symposium (IV).

[18]  Demetri Terzopoulos,et al.  Snakes: Active contour models , 2004, International Journal of Computer Vision.

[19]  Joachim Denzler,et al.  Convolutional Patch Networks with Spatial Prior for Road Detection and Urban Scene Understanding , 2015, VISAPP.

[20]  Wolfgang Förstner,et al.  Curb reconstruction using Conditional Random Fields , 2010, 2010 IEEE Intelligent Vehicles Symposium.

[21]  Yann LeCun,et al.  Semantic Road Segmentation via Multi-scale Ensembles of Learned Features , 2012, ECCV Workshops.

[22]  Markus Enzweiler,et al.  Towards multi-cue urban curb recognition , 2013, 2013 IEEE Intelligent Vehicles Symposium (IV).

[23]  G. Thomas,et al.  FREQUENCY FILTERING AND CONNECTED COMPONENTS CHARACTERIZATION FOR ZEBRA-CROSSING AND HATCHED MARKINGS DETECTION , 2010 .

[24]  Julian Eggert,et al.  Stereo image warping for improved depth estimation of road surfaces , 2013, 2013 IEEE Intelligent Vehicles Symposium (IV).

[25]  Theo Gevers,et al.  3D Scene priors for road detection , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[26]  Jerome Douret,et al.  A Reliable and Robust Lane Detection System based on the Parallel Use of Three Algorithms for Driving Safety Assistance , 2006, MVA.

[27]  Anton Kummert,et al.  ELA - an exit lane assistant for adaptive cruise control and navigation systems , 2010, 13th International IEEE Conference on Intelligent Transportation Systems.

[28]  Christopher M. Kreucher,et al.  LANA: a lane extraction algorithm that uses frequency domain features , 1999, IEEE Trans. Robotics Autom..

[29]  Rahul Mohan,et al.  Deep Deconvolutional Networks for Scene Parsing , 2014, ArXiv.