This content is not included in your SAE MOBILUS subscription, or you are not logged in.
Research on the Model of Safety Boundary Condition Based on Vehicle Intersection Conflict and Collision
ISSN: 0148-7191, e-ISSN: 2688-3627
Published April 2, 2019 by SAE International in United States
This content contains downloadable datasetsAnnotation ability available
Because of the high frequency and serious consequences of traffic accidents in the intersection area, it is of great significance to study the vehicle conflict and collision scenarios of the intersection area. Due to few actual crash accidents occurring in naturalistic driving studies data or field operational tests data, the data of traffic accident database should be also used to analyze the intersection conflict and collision. According to the China Field Operation Test (China-FOT) database and the China in Depth Accident Study (CIDAS) database, the distribution feature of the respective intersection scenario type is obtained from the data analysis. Based on the intersection scenario type, two characters of intersection conflict and collision, the environmental character and the vehicle dynamic character, are used to analyze for the integration process of intersection conflict and collision. The environmental character contains several parameters, including environment type, weather and time, which has a strong influence on the collision accidents. Through the environmental parameters analyzing, the distribution model of environmental character is obtained. The vehicle dynamic character includes two vehicle dynamic parameters, TTC (Time to Collision) and EPET (Estimating Post Encroachment Time), which are used to analyze the intersection conflict and collision scenarios. The risk evaluation method of China Field Operation Test data and logistic regression method are used to establish the vehicle dynamic model of intersection conflict and collision according to the TTC and EPET parameters of vehicle braking time. Using the environmental character distribution model and vehicle dynamic model, the intersection boundary safety condition model is established. Based on the China Field Operation Test database and the China in Depth Accident Study database, the safety boundary condition of the two kinds of intersection scenarios are obtained, which is used to automatically generate the real intersection conflict and collision test scenarios for vehicle simulation study.
CitationWu, B., Zhu, X., Liao, M., and Liu, R., "Research on the Model of Safety Boundary Condition Based on Vehicle Intersection Conflict and Collision," SAE Technical Paper 2019-01-0132, 2019, https://doi.org/10.4271/2019-01-0132.
Data Sets - Support Documents
|[Unnamed Dataset 1]|
|[Unnamed Dataset 2]|
|[Unnamed Dataset 3]|
- CIDAS: China In-Depth Accident Study, http://www.cidas.cn/.
- Sun, X., Zhu, X., Zhang, K., Lin, L. et al., Automatic Detection Method Research of Incidents in China-FOT Database (IEEE, 2016).
- Sun, X., Zhu, X., Li, L., Ma, Z., Study of Causation Mechanism and Dynamic Feature for Typical Rear End Situations in China-FOT, in ICV 2016 - IET International Conference on Intelligent and Connected Vehicles, Sept. 23, 2016)
- Li, L., Zhu, X., and Ma, Z., “Driver Braking Behaviour under near-Crash Scenarios,” International journal of vehicle safety 7(3-4):374-389, 2014.
- Zhou, X., Design and Development of Vehicle Navigation System Based on Intersection Safety [D] (Shandong University of Technology, 2013).
- Ma, X., Zhu, X., Ma, Z., Intersection Classification and Analysis of Traffic Violation Behavior in Naturalistic Driving Study, in Proceeding of the 14thInternational Forum of Automotive Traffic Safety, 2017, 20-33
- Bärgman, J., Smith, K., and Werneke, J., “Quantifying Drivers’ Comfort-Zone and Dread-Zone Boundaries in Left Turn across Path/Opposite Direction (LTAP/OD) Scenarios,” Transportation Research Part F: Traffic Psychology and Behaviour 35:170-184, 2015.
- Chan, C.Y., “Characterization of Driving Behaviors Based on Field Observation of Intersection Left-Turn Across-Path Scenarios[J],” IEEE Transactions on Intelligent Transportation Systems 7(3):322-331, 2006.
- Scanlon, J. M., Sherony. R., and Gabler, H. C., “Preliminary Potential Crash Prevention Estimates for an Intersection Advanced Driver Assistance System in Straight Crossing Path Crashes [C],” in Intelligent Vehicles Symposium. IEEE, 2016, 1135-1140.
- National Automotive Sampling System NASS, General Estimates System (GES) Analytical User’s Manual 1988-2015 (Washington, DC: National Highway Traffic Safety Administration (NHTSA), 2016).
- Summala, H., “Towards Understanding Motivational and Emotional Factors in Driver Behaviour: Comfort through Satisficing [M],” Modelling Driver Behaviour in Automotive Environments 189-207, 2007.
- Antin, J., Lee, S., Hankey, J., et al. Design of the In-Vehicle Driving Behavior and Crash Risk Study: In Support of the SHRP 2 Naturalistic Driving Study. SHRP2 Report, 2011.
- Treat, J. R., Mcdonald, N. S., Shinar, D. et al., Tri-Level Study of the Cause of Traffic Accidents [J], 1979.