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Critical Driving Scenarios Extraction Optimization Method Based on China-FOT Naturalistic Driving Study Database
ISSN: 0148-7191, e-ISSN: 2688-3627
Published August 07, 2018 by SAE International in United States
This content contains downloadable datasetsAnnotation ability available
Due to the differences in traffic situations and traffic safety laws, standards for extraction of critical driving scenarios (CDSs) vary from different countries and areas around the world. To maintain the characteristic variables under the Chinese typical CDSs, this paper uses the three-layer detection method to extract and detect CDSs in the Natural Driving Data from China-FOT project which executing under the real traffic situation in China. The first layer of detection is mainly based on the feature distributions which deviate from normal driving situations. These distributions associated with speed and longitudinal acceleration/lateral acceleration/yaw rate also quantify the critical levels classification. The second layer of detection based on the rate of brake pressure (Pressure peak/Time difference) and the relevant variables to TTC’s (Time to Collision) trigger, Pressure peak means the maximum value on brake pressure curve, Time difference means the difference between Pressure peak time and Hard breaking time (Time when driver starts to make emergency brake). The second layer could make corrections to the critical levels. The third layer of detection considers the effect of vehicle speed and make quantification of critical levels. The results show the accuracy (ACC) of detection under three-layer method makes greater optimization compared to other methods which analyze single variable. After the first two layer detections ACC achieves 69.71% while after the third layer detection ACC achieves 85.10%, 780 CDSs are extracted from these data. The results of this paper could provide a basis for the classification of CDSs from Natural Driving Data in China and causation mechanism of CDSs.
CitationZeng, Y., Zhu, X., Ma, Z., and Sun, X., "Critical Driving Scenarios Extraction Optimization Method Based on China-FOT Naturalistic Driving Study Database," SAE Technical Paper 2018-01-1628, 2018, https://doi.org/10.4271/2018-01-1628.
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- Neale, V.L. et al., “The 100 Car Naturalistic Driving Study, Phase I-Experimental Design,” No. HS-809 536, 2002, doi:10.1037/e733252011-001.
- Sayer, J.R. et al., “Integrated Vehicle-Based Safety Systems Light-Vehicle Field Operational Test Key Findings Report,” 2011, doi:10.1016/j.annemergmed.2011.05.030.
- Victor, T. et al., “Sweden-Michigan Naturalistic Field Operational Test (Semifot) Phase 1: Final Report,” SAFER Report 2, 2010.
- Etemad, A. and Kessler, C., “euroFOT-European Large-Scale Field Operational Test on In-Vehicle Systems,” 4. Tagung Fahrerassistenz, 2010.
- Menhour, L., Charara, A., and Lechner, D., “Switched LQR/H∞ Steering Vehicle Control to Detect Critical Driving Situations,” Control Engineering Practice 24:1-14, 2014, doi:10.1016/j.conengprac.2013.11.007.
- Benmimoun, M. and Eckstein, L., “Detection of Critical Driving Situations for Naturalistic Driving Studies by Means of an Automated Process,” Journal of Intelligent Transportation and Urban Planning 2(1):11-21, 2014, doi:10.18005/ITUP0201002.
- Feng, Q., “The Research on Control Algorithm of Risk Based on Typical Near-Crash Traffic Situation,” Quality and Standardization 10:51-54, 2014, doi:10.3969/j.issn.2095-0918.2014.10.019.
- Li, L. et al., “Typical Traffic Risk Scenarios Related to Pedal Cyclists,” Journal of Tongji University (Natural Science) 42(7):1082-1087, 2014, doi:10.3969/j.issn.0253-374x.2014.07.015.
- Bagdadi, O. and Várhelyi, A., “Development of a Method for Detecting Jerks in Safety Critical Events,” Accident Analysis & Prevention 50:83-91, 2013, doi:10.1016/j.aap.2012.03.032.
- Zhang, K., “The Research on Automatic Processing Method of Critical Driving Scenarios Based on FOT Database,” Unpublished Master’s Dissertation, The School of Automobile Engineering, Tongji University, 2016.
- Xue, W., SPSS Statistical Analysis and Application (Electronics Industry Press, 2004), 78-273. ISBN:9787121069666.
- Reason, J., Human Error (Cambridge University Press, 1990), 1-192. ISBN:0-521-31419-4.