Visual ego-vehicle lane assignment using Spatial Ray features

Assigning the ego-vehicle to a lane is not only beneficial for navigation but will be an essential element in future Advanced Driver Assistance Systems. This paper describes an approach for ego-lane index estimation using only a monocular camera and no additional sensing equipment like, e.g., the typically employed GPS and Inertial Measurement Unit. Key aspect of the approach are SPatial RAY (SPRAY) features which represent the spatial layout of the road in the visual scene. The proposed method perceives a variety of local visual properties of the scene by means of base classifiers operating on patches extracted from camera images. The spatial arrangement of these local visual properties are captured using SPRAY features. With a boosting classifier trained on these features the ego-lane index is obtained. The system is evaluated on low traffic density and complementary to an object-based approach suitable for heavy traffic. In the conducted experiments, the proposed approach reaches recognition rates of 93% to 97% on individual highway images without applying any kind of temporal filtering.

[1]  Yoav Freund,et al.  A decision-theoretic generalization of on-line learning and an application to boosting , 1995, EuroCOLT.

[2]  Victor L. Knoop,et al.  Automated lane identification using precise point positioning an affordable and accurate GPS technique , 2012, 2012 15th International IEEE Conference on Intelligent Transportation Systems.

[3]  Thanh-Son Dao,et al.  Markov-Based Lane Positioning Using Intervehicle Communication , 2007, IEEE Transactions on Intelligent Transportation Systems.

[4]  J. Little,et al.  Inverse perspective mapping simplifies optical flow computation and obstacle detection , 2004, Biological Cybernetics.

[5]  Andrew W. Fitzgibbon,et al.  Real-time human pose recognition in parts from single depth images , 2011, CVPR 2011.

[6]  Vincent Lepetit,et al.  Fast Ray features for learning irregular shapes , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[7]  Franz Kummert,et al.  Spatial ray features for real-time ego-lane extraction , 2012, 2012 15th International IEEE Conference on Intelligent Transportation Systems.

[8]  Terrence J. Sejnowski,et al.  Slow Feature Analysis: Unsupervised Learning of Invariances , 2002, Neural Computation.

[9]  Daniel Schwarz,et al.  Vehicle localization using cooperative RF-based landmarks , 2011, 2011 IEEE Intelligent Vehicles Symposium (IV).

[10]  Yoav Freund,et al.  A decision-theoretic generalization of on-line learning and an application to boosting , 1997, EuroCOLT.

[11]  J. Friedman.  Special Invited Paper-Additive logistic regression: A statistical view of boosting , 2000 .

[12]  Sanjiv Singh,et al.  The DARPA Urban Challenge: Autonomous Vehicles in City Traffic, George Air Force Base, Victorville, California, USA , 2009, The DARPA Urban Challenge.

[13]  Franz Kummert,et al.  Monocular road segmentation using slow feature analysis , 2011, 2011 IEEE Intelligent Vehicles Symposium (IV).

[14]  Franz Kummert,et al.  A Generic Concept of a System for Predicting Driving Behaviors , 2012, 2012 15th International IEEE Conference on Intelligent Transportation Systems.

[15]  Sergiu Nedevschi,et al.  On-Road Position Estimation by Probabilistic Integration of Visual Cues , 2022 .

[16]  Klaus C. J. Dietmayer,et al.  Localization in digital maps for road course estimation using grid maps , 2012, 2012 IEEE Intelligent Vehicles Symposium.

[17]  Gerd Wanielik,et al.  High-accurate vehicle localization using digital maps and coherency images , 2010, 2010 IEEE Intelligent Vehicles Symposium.

[18]  Sean N. Brennan,et al.  Terrain-based road vehicle localization on multi-lane highways , 2009, 2009 American Control Conference.

[19]  Amnon Shashua,et al.  Off-road Path Following using Region Classification and Geometric Projection Constraints , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).