Using Context to Identify Difficult Driving Situations in Unstructured Environments
We present a context-based machine-learning approach for identifying difficult driving situations using sensor data that is readily available in commercial vehicles. The goal of this system is improve vehicle safety by alerting drivers to potentially dangerous situations. The context-based approach is a two-step learning process by first performing unsupervised learning to discover meaningful regularities, or “contexts,” in the vehicle data and then performing supervised learning, mapping the current context to a measure of driving difficulty. To validate the benefit of this approach, we collected driving data from a set of experiments involving both on-road and off-road driving tasks in unstructured environments. We demonstrate that context recognition greatly improves the performance of identifying difficult driving situations and show that the driving-difficulty system achieves a human level of performance on cross-validation data.
KeywordsArea Under Curve Commercial Vehicle Controller Area Network Unstructured Environment Vehicle Data
Unable to display preview. Download preview PDF.
- 1.NHTSA: Traffic safety facts: 2006 data. Technical Report DOT HS 810 807, U.S. Department of Transportation, National Highway Traffic Safety Administration (2007) Google Scholar
- 2.Klauer, S., Dingus, T., Neale, V., Sudweeks, J., Ramsey, D.: The impact of driver inattention on near-crash/crash risk: An analysis using the 100-car naturalistic driving study data. Technical Report DOT-HS-810-594, National Highway Traffic Safety Administration (2006)Google Scholar
- 3.Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification, 2nd edn. Wiley-Interscience, New York (2001)Google Scholar
- 6.Montemerlo, M., Thrun, S., Dahlkamp, H., Stavens, D., Strohband, S.: Winning the DARPA Grand Challenge with an AI robot. In: Proceedings of the AAAI National Conference on Artificial Intelligence (2006)Google Scholar
- 8.Fletcher, L., Apostoloff, N., Petersson, L., Zelinsky, A.: Driver assistance systems based on vision in and out of vehicles. In: Proceedings of the IEEE Intelligent Vehicles Symposium (2003)Google Scholar
- 9.Miller, B.W., Hwang, C.H., Torkkola, K., Massey, N.: An architecture for an intelligent driver assistance system. In: Proceesings of the IEEE Intelligent Vehicles Symposium, pp. 639–644 (2003)Google Scholar
- 10.Himberg, J., Korpiaho, K., Mannila, H., Tikanmaki, J., Toivonen, H.T.: Time series segmentation for context recognition in mobile devices. In: IEEE International Conference on Data Mining (2001)Google Scholar
- 11.Boutell, M., Luo, J.: Incorporating temporal context with content for classifying image collections. In: Proceedings of the 17th International Conference on Pattern Recognition, vol. 2, pp. 947–950 (2004)Google Scholar
- 12.Dixon, K.R., Lippitt, C.E., Forsythe, J.C.: Supervised machine learning for modeling human recognition of vehicle-driving situations. In: Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (2005)Google Scholar
- 14.Kohlmorgen, J., Dornhege, G., Braun, M., Blankertz, B., Muller, K.R., Curio, G., Hagemann, K., Bruns, A., Schrauf, M., Kincses, W.: Improving human performance in a real operating environment through real-time mental workload detection. In: Toward Brain-Computer Interfacing, pp. 409–422. MIT Press, Cambridge (2007)Google Scholar
- 15.Fletcher, R.: Practical Methods of Optimization, 2nd edn. Wiley Interscience, New York (1987)Google Scholar