Abstract: To prevent the dangerous driving behavior of cut-ins inserting into the gap between the ego car and the lead car, a vehicle control strategy for accelerating was proposed. According to the following safety distance model, considering the following characteristics of time headway and parking distance, a following safety distance model and an anti-cut-ins safety distance model were established based on three types of driver. A vehicle speed controller based on the variable safety distance control strategy was established by the Sigmoid function. When another vehicle was detected to cut-in, the anti-cut-ins safety distance model was selected, otherwise the following safety distance model was selected. The MATLAB/Simulink and CarSim co-simulation platform was used to carry out experiments. The results show that the vehicle control strategy based on three types of driver′s variable safety distance and anti-cut-ins is feasible.
蒋浩, 赵又群, 林棻, 张雯盺. 基于3种类型驾驶员变安全距离防夹塞变道的车辆控制策略
[J]. 江苏大学学报(自然科学版), 2023, 44(6): 644-650.
JIANG Hao, ZHAO Youqun, LIN Fen, ZHANG Wenxin. Vehicle control strategy of anti-cut-ins through changing safety distance based on three types of driver
[J]. Journal of Jiangsu University(Natural Science Eidtion)
, 2023, 44(6): 644-650.
WANG X S, YANG M M, HURWITZ D. Analysis of cut-in behavior based on naturalistic driving data[J]. Accident Analysis & Prevention, 2019, 124(1): 127-137.
KIM S,WANG J M,GUENTHER D,et al. Analysis of human driver behavior in highway cut-in scenarios[C]∥SAE World Congress Experience Detroit, 2017: 4271-4275.
[4]
SULTAN B, BRACKSTONE M, WATERSON B, et al. Modeling the dynamic cut-in situation[J]. Transportation Research Record, 2002, 1803(1): 45-51.
[5]
GUO Y S, SUN Q Y, FU R, et al. Improved car-following strategy based on merging behavior prediction of adjacent vehicle from naturalistic driving data[J]. IEEE Access, 2019, 7: 44258-44268.
[6]
REMMEN F, CARA I, DE GELDER E, et al. Cut-in scenario prediction for automated vehicles[C]∥ IEEE International Conference on Vehicular Electronics and Safety.Piscataway,USA:IEEE, 2018:1-7.
[7]
DANG R N, FANG Z, WANG J Q, et al. Analysis of Chinese driver′s lane change characteristic based on real vehicle tests in highway[C]∥Proceedings of the 16th International IEEE Annual Conference on Intelligent Transportation Systems. Piscataway,USA:IEEE, 2013:1917-1722.
[8]
KIM J, KUM D. Collision risk assessment algorithm via lane-based probabilistic motion prediction of surrounding vehicles[J]. IEEE Transactions on Intelligent Transportation Systems, 2018, 19(9): 2965-2976.
[9]
LIAO Y, WANG W J, YU J Y, et al. Brake behavior analysis in low-speed vehicle cut-in condition[C]∥Proceedings of the 16th Automotive Safety Technology Conference of the Society of Automotive Engineers of China.Beijing, China, 2013:7-12.
[10]
HU M J, LIAO Y, WANG W J, et al. Decision tree-based maneuver prediction for driver rear-end risk-avoidance behaviors in cut-in scenarios[J]. Journal of Advanced Transportation,DOI:10.1155/2017/7170358.
[11]
MURPHEY Y L, MILTON R, KILIARIS L. Driver′s style classification using jerk analysis[C]∥2009 IEEE Workshop on Computational Intelligence in Vehicles and Vehicular Systems. Piscataway,USA:IEEE, 2009: 23-28.