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Analysis of the Correlation between Driver's Visual Features and Driver Intention
ISSN: 0148-7191, e-ISSN: 2688-3627
Published April 02, 2019 by SAE International in United States
This content contains downloadable datasetsAnnotation ability available
Driver behaviors provide abundant information and feedback for future Advanced Driver Assistance Systems (ADAS). Driver’s eye and head may present some typical movement patterns before executing driving maneuvers. It is possible to use driver’s head and eye movement information for predicting driver intention. Therefore, to determine the most important features related to driver intention has attracted widespread research interests. In this paper, a method to analyze the correlation between driver’s visual features and driver intention is proposed, aiming to determine the most representative features for driver intention prediction. Firstly, naturalistic driving experiment is conducted to collect driver’s videos during executing lane keeping and lane change maneuvers. Then, driver’s head and face visual features are extracted from those videos. By using boxplot and independent samples T-test, features which have significant correlation with driver intention are found. Finally, the random forest algorithm is used to sequence the correlation importance between driver’s visual features and driver intention. The results show that driver’s eye horizontal movement and head yaw movement play the most important role in driver intention estimation among all related visual features. This research can be used to support features selection during driver intention estimation and leads to improvement of traffic safety.
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CitationZhang, L., Xiao, W., and Meng, D., "Analysis of the Correlation between Driver's Visual Features and Driver Intention," SAE Technical Paper 2019-01-1229, 2019, https://doi.org/10.4271/2019-01-1229.
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- Wang, J., Liu, G., Li, K., and Lian, X., “Study on Collision Warning Technology under Complex Road Conditions,” Highway and Transportation Research and Development 22(4):132-135, 2005, doi:10.3969/j.issn.1002-0268.2005.04.035.
- Kumar, P., Perrollaz, M., Lefèvre, S., and Laugier, C., "Learning-Based Approach for Online Lane Change Intention Prediction," in presented at IEEE Intelligent Vehicles Symposium 2013, Australia, June 23-26, 2013.
- Dogan, Ü., Edelbrunner, J., and Iossifidis, I., "Autonomous Driving: A Comparison of Machine Learning Techniques by Means of the Prediction of Lane Change Behavior," in presented at IEEE International Conference on Robotics and Biomimetics 2011, Thailand, Dec. 7-11, 2011.
- Kasper, D., Weidl, G., Dang, T., Breuel, G. et al., “Object-Oriented Bayesian Networks for Detection of Lane Change Maneuvers,” IEEE Intelligent Transportation System Magazine 4(3):19-31, 2012, doi:10.1109/MITS.2012.04.2203229.
- Rigas, G., Katsis, C., Bougia, P., and Fotiadis, D., "A Reasoning-Based Framework for Car Driver’s Stress Prediction," in presented at 16th Mediterranean Conference on Control and Automation 2008, France, June 25-27, 2008.
- Ji, B., "Research on Driving Behavior Prediction Method based on Driver’s Visual Characteristics," Ph.D. thesis, Traffic Environment and Safety Technology Department, Jilin University, Changchun, 2014.
- Doshi, A. and Trivedi, M., “On the Role of Eye Gaze and Head Dynamics in Predicting Driver’s Intent to Change Lanes,” IEEE Transactions on Intelligent Transportation System 10(3):453-462, 2009, doi:10.1109/TITS.2009.2026675.
- Lethaus, F. and Rataj, J., “Do Eye Movement Reflect Driving Maneuvers,” IEEE Transactions on Intelligent Transportation System 1(3):199-204, 2007, doi:10.1049/IET-ITS.20060058.
- Shen, Z., Probability Theory and Mathematical Statistics Third Edition (Shanghai: Shanghai Jiao Tong University Press, 2011), 119-123. ISBN:978-7-313-02024-6.
- Genuer, R., Poggi, J., and Malot, C., “Variable Selection Using Random Forests,” Pattern Recognition Letters 31(14):2225-2236, 2010, doi:10.1016/J-PATREC.2010.03.014.
- Berndt, H., Emmert, J., and Dietmayer, K., "Continuous Driver Intention Recognition with Hidden Markov Models," in presented at 11th Conference on Intelligent Transportation System, China, Oct. 12-15, 2008.