This content is not included in
your SAE MOBILUS subscription, or you are not logged in.
Robust Traffic Vehicle Lane Change Maneuver Recognition
Technical Paper
2017-01-0110
ISSN: 0148-7191, e-ISSN: 2688-3627
This content contains downloadable datasets
Annotation ability available
Sector:
Language:
English
Abstract
The ability to recognize traffic vehicles’ lane change maneuver lays the foundation for predicting their long-term trajectories in real-time, which is a key component for Advanced Driver Assistance Systems (ADAS) and autonomous automobiles. Learning-based approach is powerful and efficient, such approach has been used to solve maneuver recognition problems of the ego vehicles on conventional researches. However, since the parameters and driving states of the traffic vehicles are hardly observed by exteroceptive sensors, the performance of traditional methods cannot be guaranteed. In this paper, a novel approach using multi-class probability estimates and Bayesian inference model is proposed for traffic vehicle lane change maneuver recognition. The multi-class recognition problem is first decomposed into three binary problems under error correcting output codes (ECOC) framework. With probability estimates from the three binary classifiers, multi-class probability estimates are obtained through paired team comparisons. A sequence of the multi-class probability estimates are then fed into the Bayesian inference model. The Bayesian inference model views the input as sample of a random variable, and the output of the Bayesian inference model is used for the final recognition. A data set which is collected from a real-time driving simulation platform is used for the training of the binary classifiers. Typical samples are used to evaluate the performance of the proposed approach. The experimental results have demonstrated the improvement of robustness when using the proposed approach, and the approach is able to recognize lane change maneuver of the traffic vehicle with an average prediction horizon of 1.51 seconds.
Authors
Citation
Sun, H., Deng, W., Su, C., and Wu, J., "Robust Traffic Vehicle Lane Change Maneuver Recognition," SAE Technical Paper 2017-01-0110, 2017, https://doi.org/10.4271/2017-01-0110.Data Sets - Support Documents
Title | Description | Download |
---|---|---|
Unnamed Dataset 1 |
Also In
References
- Lum H. and Reagan , J. A. Interactive highway safety design model: accident predictive module Public Roads 58 3 1995
- Figueiredo , L. Jesus , I. Machado , J. T. Ferreira , J. and de Carvalho , J. M. Towards the development of intelligent transportation systems Intelligent transportation systems 2001 88 1206 1211
- Lefèvre , S. Vasquez , D. and Laugier , C. A survey on motion prediction and risk assessment for intelligent vehicles ROBOMECH J 1 1 1 14 Jul. 2014
- Kumar , P. Perrollaz , M. Lefèvre , S. and Laugier , C. Learning- based approach for online lane change intention prediction 2013 IEEE Intelligent Vehicles Symposium (IV) 2013 797 802
- Tamke , A. Dang , T. and Breuel , G. A flexible method for criticality assessment in driver assistance systems 2011 IEEE Intelligent Vehicles Symposium (IV) 2011 697 702
- Lee , S. E. Olsen , E. C. and Wierwille , W. W. A comprehensive examination of naturalistic lane-changes 2004
- Klingelschmitt , S. Platho , M. Gro\s s , H.-M. Willert , V. and Eggert , J. Combining behavior and situation information for reliably estimating multiple intentions 2014 IEEE Intelligent Vehicles Symposium Proceedings 2014 388 393
- Aoude , G. S. Desaraju , V. R. Stephens , L. H. and How , J. P. Behavior classification algorithms at intersections and validation using naturalistic data Intelligent Vehicles Symposium (IV), 2011 IEEE 2011 601 606
- Mandalia H. M. and Salvucci , M. D. D. Using support vector machines for lane-change detection Proceedings of the Human Factors and Ergonomics Society Annual Meeting 2005 49 1965 1969
- Streubel T. and Hoffmann , K. H. Prediction of driver intended path at intersections 2014 IEEE Intelligent Vehicles Symposium Proceedings 2014 134 139
- Lefèvre , S. Gao , Y. Vasquez , D. Tseng , E. Bajcsy , R. and Borrelli , F. Lane Keeping Assistance with Learning-Based Driver Model and Model Predictive Control ResearchGate 2014
- Christopher , T. Analysis of dynamic scenes: Application to driving assistance Institut National Polytechnique de Grenoble-INPG 2009
- Aoude , G. S. Threat assessment for safe navigation in environments with uncertainty in predictability Citeseer 2011
- Cortes C. and Vapnik , V. Support-vector networks Mach. Learn 20 3 273 297 1995
- Huang , T.-K. Weng , R. C. and Lin , C.-J. Generalized Bradley-Terry models and multi-class probability estimates J. Mach. Learn. Res 7 Jan 85 115 2006
- LIBSVM -- A Library for Support Vector Machines Online https://www.csie.ntu.edu.tw/~cjlin/libsvm/ 18 Sep 2016