This content is not included in
your SAE MOBILUS subscription, or you are not logged in.
Extracting Situations with Uneasy Driving in NDS-Data
Technical Paper
2014-01-0450
ISSN: 0148-7191, e-ISSN: 2688-3627
Annotation ability available
Sector:
Language:
English
Abstract
Different types of driver workload are suggested to impact driving performance. Operating a vehicle in a situation where the driver feel uneasy is one example of driver workload. In this study, passenger car driving data collected with Naturalistic Driving Study (NDS) data acquisition equipment was analyzed, aiming to identify situations corresponding to a high driver's subjective rating of ‘unease’. Data from an experimental study with subjects driving a passenger car in normal traffic was used. Situations were rated by the subjects according to experienced ‘unease’, and the Controller Area Network (CAN) data from the vehicle was used to describe the driving conditions and identify driving patterns corresponding to the situations rated as ‘uneasy’. These driving patterns were matched with the data in a NDS database and the method was validated using video data.
Two data mining approaches were applied. The first was based on an ensemble classifier on general variables derived from the CAN-data to predict the subjective rating of segments of the data. The second used hierarchical clustering with a distance metric based on the principal variance components over segments. The ensemble classifier explained a large proportion of the variance when adjusting for driver and route. The hierarchical clustering method performed well, distinct clusters corresponding to a high driver subjective rating could be obtained.
Identifying situations with increased driver workload in NDS data is a complex task addressing a large variation of traffic situations and driver experiences. The proposed method is a first approach to help address this topic using data mining.
Recommended Content
Authors
Citation
Karlsson, T., Lindman, M., Kovaceva, J., Svanberg, B. et al., "Extracting Situations with Uneasy Driving in NDS-Data," SAE Technical Paper 2014-01-0450, 2014, https://doi.org/10.4271/2014-01-0450.Also In
References
- Klauer , S.G. , Dingus , T. A. , Neale , V. L. , Sudweeks , J.D. et al. The Impact of Driver Inattention On Near-Crash/Crash Risk: An Analysis Using the 100-Car Naturalistic Driving Study Data NHTSA April 2006
- NHTSA National Motor Vehicle Crash Causation Survey: Report to Congress NHTSA July 2008
- De Waard , D. The measurements of drivers' mental workload Groningen University, Traffic Research Center 1996
- Miller , S Literature review: Workload Measures University of Iowa 2001
- Fuller , R. Towards a general theory of driver behavior Accid. Anal. Prev. 37 Dublin 2005
- Östlund , J. , Nilsson , L. , Carsten , O. , Merat , N. et al. HASTE Deliverable 2: HMI and Safety-Related Driver Performance Institute for Transportation Studies, University of Leeds 2005
- Zhang , Y. , Owechko , Y. , Zhang , J. Driver Cognitive Workload Estimation: A Data-driven Perspective IEEE Intelligent Transportation Systems Conference Washington, D.C. 2004
- Wong , J. , Huang , S. Modeling Driver Mental Workload for Accident Causation and Prevention Journal of the Eastern Asia Society for Transportation Studies 8 2009
- Yang , Y. , Reimer , B. , Mehler , B. & Dobres , J. 2013 A Field Study Assessing Driving Performance, Visual Attention, Heart Rate and Subjective Ratings in Response to Two Types of Cognitive Workload Proceedings of the 7th International Driving Symposium on Human Factors in Driver Assessment, Training and Vehicle Design Bolton Landing New York
- Wynn , T. , Richardson , J. Comparison of Subjective Workload Ratings and Performance Measures of a Reference IVIS Task Proceedings of European Conference on Human Centred Design for Intelligent Transport Systems 2008 Lyon
- Healey , J. , Picard , R. Detecting Stress During Real-World Driving Tasks Using Physiological Sensors IEEE Trans. on Intelligent Transportation Systems 6 2 June 2005
- Kohlmorgen , J. , Dornhege , G. , Braun , M. , BLankertz , B. et al. Improving Human Performance in a Real Operating Environment through Real-Time Mental Workload Detection, Toward Brain-Computer Interfacing MIT Press 2007
- Bärgman J. , Gellerman H. , Kovaceva J. , Nisslert R. et al. On data security and analysis platforms for analysis of naturalistic driving data Proceedings of the 8th European Congress and Exhibition on Intelligent Transport Systems and Services June 2011 Lyon
- Dozza , M. , Bärgman , J. Lee , J. Chunking: A procedure to improve naturalistic data analysis Accid. Anal. Prev. 2012
- Wang , Y. Chen , H. Use of Percentiles and Z-Scores in Anthropometry, Handbook of Anthropometry Springer 2012
- Breiman , L. , Friedman , J.H. , Olshen , R.A. , and Stone , C.I. Classification and regression trees Belmont, Calif. Wadsworth 1984
- Sutton C. Classification and Regression Trees, Bagging and Boosting. Handbook of Statistics 24 2005
- Hastie , T. , Tibshirani , R. , Friedman , J. The Elements of Statistical Learning: Data Mining, Inference, and Prediction Springer 2011
- Spiegel , S. , Gaebler , J. , Lommatzsch , A. , De Luca , E. et al. Pattern Recognition and Classification for Multivariate Time Series DAI-Labor, Technische Universitaet Berlin
- Guyon , I. , Elisseeff , A An Introduction to Variable and Feature Selection Journal of Machine Learning Research 3 2003
- Lay , D. Linear Algebra and Its Applications Pearson 2010
- Hill , S. , Ivacchia , H. , Byers , J. Comparison of Four Subjective Workload Rating Scales Human Factors: The Journal of the Human Factors and Ergonomics Society Sagepub 1992