This content is not included in
your SAE MOBILUS subscription, or you are not logged in.
Detection of Driver’s Cognitive States Based on LightGBM with Multi-Source Fused Data
Technical Paper
2022-01-0066
ISSN: 0148-7191, e-ISSN: 2688-3627
Annotation ability available
Sector:
Language:
English
Abstract
According to the statistics of National Highway Traffic Safety Administration, driver’s cognitive distraction, which is usually caused by drivers using mobile phones, has become one of the main causes of traffic accidents. To solve this problem and guarantee the safety of man-vehicle-road system, the most critical work is to improve the accuracy of driver’s cognitive state detection. In this paper, a novel driver’s cognitive state detecting method based on LightGBM (Light Gradient Boosting Machine) is proposed. Firstly, cognitive distraction experiments of making calls are carried out on a driving simulator to collect vehicle states, eye tracking and EEG (electron encephalogram) data simultaneously and feature extraction is conducted. Then a classifier considering road and individual characteristics used for detecting cognitive states is trained based on LightGBM algorithm, with 3 predefined cognitive states including concentration, ordinary distraction and extreme distraction. Finally, the proposed algorithm is compared with 8 other algorithms. Comparing results show LightGBM outperforms the 8 methods in accuracy, precision, recall, F1 value and macro-AUC (Area Under Curve) value. Meanwhile, the detecting performance using multi-source fused data is better than using only one or two types of data. 25 most important features are extracted using SHAP (SHapley Additive exPlanations) theory, and the interpretability of the model is improved. This cognitive state detecting method with multi-source fused data provides insights into the evaluation of driving risk, and can be applied to the design of in-vehicle auxiliary distraction warning system to reduce traffic accidents.
Recommended Content
Authors
Topic
Citation
Li, J., Liu, Y., Ji, X., and Tao, S., "Detection of Driver’s Cognitive States Based on LightGBM with Multi-Source Fused Data," SAE Technical Paper 2022-01-0066, 2022, https://doi.org/10.4271/2022-01-0066.Also In
References
- Cicchino , J.B. and McCartt , A.T. Critical Older Driver Errors in a National Sample of Serious U.S. Crashes Accident Analysis & Prevention 80 2015 211 219 10.1016/j.aap.2015.04.015
- Liu , Y.C. Comparative Study of the Effects of Auditory, Visual and Multimodality Displays on Drivers’ Performance in Advanced Traveller Information Systems Ergonomics 44 4 2001 425 442 10.1080/00140130010011369
- Birrell , S.A. and Young , M.S. The Impact of Smart Driving Aids on Driving Performance and Driver Distraction Transportation Research Part F: Traffic Psychology and Behavior 14 6 2011 484 493 10.1016/j.trf.2011.08.004
- Son , J. and Park , M. Detection of Cognitive and Visual Distraction Using Radial Basis Probabilistic Neural Networks International Journal of Automotive Technology 19 5 2018 935 940 10.1007/s12239-018-0090-4
- Kim , S.L. and Yang , J.H. Evaluation of the Effects of Driver Distraction Part 1: Based on Simulator Experiments 2018 IEEE International Conference on Systems, Man, and Cybernetics (Smc):1081-1086 2018 10.1109/Smc.2018.00191
- Kountouriotis , G.K. , Wilkie , R.M. , Gardner , P.H. , and Merat , N. Looking and Thinking When Driving: The Impact of Gaze and Cognitive Load on Steering Transportation Research Part F-Traffic Psychology and Behaviour 34 2015 108 121 10.1016/j.trf.2015.07.012
- Osman , O.A. , Hajij , M. , Karbalaieali , S. , and Ishak , S. A Hierarchical Machine Learning Classification Approach for Secondary Task Identification from Observed Driving Behavior Data Accident Analysis & Prevention 123 2019 274 281 10.1016/j.aap.2018.12.005
- Ebnali , M. , Ahmadnezhad , P. , Shateri , A. , Mazloumi , A. et al. The Effects of Cognitively Demanding Dual-task Driving Condition on Elderly People’s Driving Performance; Real Driving Monitoring Accident Analysis and Prevention 94 2016 198 206 10.1016/j.aap.2016.05.016
- Miyaji , M. , Kawanaka , H. , and Oguri , K. Study on Effect of Adding Pupil Diameter as Recognition Features for Driver’s Cognitive Distraction Detection 2010 7th International Symposium on Communication Systems, Networks & Digital Signal Processing (CSNDSP 2010) 2010 10.1109/CSNDSP16145.2010.5580383
- Demberg , V. , Sayeed , A. , Mahr , A. , and Müller , C. Measuring Linguistically-induced Cognitive Load During Driving Using the ConTRe Task Proceedings of the 5th International Conference on Automotive User Interfaces and Interactive Vehicular Applications Eindhoven, Netherlands 2013 10.1145/2516540.2516546
- Miyaji , M. , Kawanaka , H. , and Oguri , K. Driver’s Cognitive Distraction Detection Using Physiological Features by the Adaboost 2009 12th International IEEE Conference on Intelligent Transportation Systems Oct 4-7, 2009 10.1109/ITSC.2009.5309881
- Wong , J.T. and Huang , S.H. Attention Allocation Patterns in Naturalistic Driving Accident Analysis and Prevention 58 2013 140 147 10.1016/j.aap.2013.04.033
- Harada , T. , Mori , K. , Yoshizawa , A. , and Iwasaki , H. SS3: A Design of the Cognitive Process Model for a Car Driver considering Quantitatively Expressed Distraction 2014 IEEE 13th International Conference on Cognitive Informatics & Cognitive Computing (ICCI-CC):273-280 2014
- Hsieh , L. , Young , R.A. , Bowyer , S.M. , Moran , J.E. et al. Conversation Effects on Neural Mechanisms Underlying Reaction Time to Visual Events while Viewing a Driving Scene: fMRI Analysis and Asynchrony Model Brain Research 1251 2009 162 175 10.1016/j.brainres.2008.10.002
- Andreas , S. , Treder , M. , Simon , M. , Willmann , S. et al. EEG Alpha Spindles and Prolonged Brake Reaction Times During Auditory Distraction in an On-road Driving Study Accident Analysis & Prevention 62 2014 110 118 10.1016/j.aap.2013.08.026
- Wang , S. , Zhang , Y. , Wu , C. , Darvas , F. et al. Online Prediction of Driver Distraction Based on Brain Activity Patterns IEEE Transactions on Intelligent Transportation Systems 16 1 2015 136 150 10.1109/TITS.2014.2330979
- Nabaraj , D. , Nandagopal , N. , Cocks , B. , Vijayalakshmi , R. et al. TVAR Modeling of EEG to Detect Audio Distraction During Simulated Driving Journal of Neural Engineering 11 3 2015 036012 10.1088/1741-2560/11/3/036012
- Liao , Y. , Li , S.E. , Wang , W.J. , Wang , Y. et al. Detection of Driver Cognitive Distraction: A Comparison Study of Stop-Controlled Intersection and Speed-Limited Highway IEEE Transactions on Intelligent Transportation Systems 17 6 2016 1628 1637 10.1109/Tits.2015.2506602
- Liang , Y.L. and John , D. Lee, “A hybrid Bayesian Network Approach to Detect Driver Cognitive Distraction,” Transportation Research Part C: Emerging Technologies 38 2014 146 155 10.1016/j.trc.2013.10.004
- Mühlbacher-Karrer , S. , Mosa , A.H. , Faller , L. , Ali , M. et al. A Driver State Detection System—Combining a Capacitive Hand Detection Sensor with Physiological Sensors IEEE Transactions on Instrumentation and Measurement 66 4 2017 624 636 10.1109/TIM.2016.2640458
- Yan , C. , Zhang , B. , and Coenen , F. Driving Posture Recognition by a Hierarchal Classification System with Multiple Features 2014 7th International Congress on Image and Signal Processing October 14-16, 2014 10.1109/CISP.2014.7003754
- Hua , Q. , Jin , L.S. , Jiang , B.G. , and Xie , X. Effect of Cognitive Distraction on Physiological Measures and Driving Performance in Traditional and Mixed Traffic Environments Journal of Advanced Transportation 2021 2021 1 17 10.1155/2021/6739071
- Ma , Y.F. , Yin , Z.S. , and Nie , L.Z. Driver Distraction Detection with a Two-stream Convolutional Neural Network SAE Technical Paper 2020-01-1039 2020 10.4271/2020-01-1039
- Ke , G.L. , Meng , Q. , Finley , T. , Wang , T.F. et al. LightGBM: A Highly Efficient Gradient Boosting Decision Tree Advances in Neural Information Processing Systems 30 Nips 2017 2017 30 http://papers.nips.cc/paper/6907-lightgbm-a-highly-efficientgradient-boosting-decision-tree.pdf
- Chen , T.Q. and Guestrin , C. XGBoost: A Scalable Tree Boosting System Kdd’16: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining:785-794 2016 10.1145/2939672.2939785
- Caird , J.K. , Willness , C.R. , Steel , P. , and Scialfa , C. A Metaanalysis of the Effects of Cell Phones on Driver Performance Accident Analysis and Prevention 40 4 2008 1282 1293 10.1016/j.aap.2008.01.009
- Fraser , J.L. and Jovanis , P.P. 2013 https://www.mautc.psu.edu/docs/PSU-2010-04.pdf
- Pickrell , T.M. and Li , H.(.R.). Driver Electronic Device Use in 2016 Washington, DC National Highway Traffic Safety Administration 2017
- Eisenman , R.L. A Profit-Sharing Interpretation of Shapley Value for N-Person Games Behavioral Science 12 5 396 & 1967 10.1002/bs.3830120506
- Lundberg , S.M. and Lee , S.I. A Unified Approach to Interpreting Model Predictions Proceedings of the 31st International Conference on Neural Information Processing Systems (NIPS 2017) 2017 4768 4777
- Strumbelj , E. and Kononenko , I. Explaining Prediction Models and Individual Predictions with Feature Contributions Knowledge and Information Systems 41 3 2014 647 665 10.1007/s10115-013-0679-x