Modelling Drivers' Lane-Changing Decision Behaviour Based on Social Force
YANG Da1,2, SU Gang1, WU Danhong1, XIONG Mingqiang1, PU Yun1
1. School of Transportation and Logistics, Southwest Jiaotong University, Chengdu 610031, China; 2. Traffic Management Research Institute of the Ministry of Public Security, Wuxi 214151, China
Abstract：In order to establish a simpler lane-changing decision-making model and consider the interaction between the lane-changing vehicle and the vehicles of the target lane, on the basis of the traditional methods of lane-changing utility and selection of safety gap, an initiative lane-changing decision-making model for drivers based on the social model by combining the social force following model with the lane-changing model was proposed. First, taking the following force in the social force model as the utility function of each driveway, a lane-changing decision-making utility model for the target lane selection was established. Second, considering the vehicle's longitudinal safety in the lane-changing process, the following force was used to build utility model of interaction between the lane-changing vehicle and the vehicles of the target lane to constrain the selection of safety gap. Finally, the proposed model was calibrated and validated by using next generation simulation (NGSIM) data and genetic algorithm function in MATLAB genetic algorithms toolbox for drivers' lane-changing decision-making behaviours (keeping following, changing to right lane, changing to left lane). The study results show that the lane-changing decision-making model based on social force could identify accurately the drivers'initiative lane-changing decision-making behaviour. The recognition rates of the optimal parameters in calibration data for drivers' lane-changing decision-making behaviours (keeping following, changing to right lane, changing to left lane) reached 93.44%, 93.14%, and 90.77%, respectively, and the recognition rates of the lane-changing decision-making behaviours in calibration data reached 86.16%, 80.00%, and 80.27%. Each recognition rate in calibration data and validation data was above 80.00%, and the overall recognition rates reached 92.66% and 83.28%, respectively.
杨达, 苏刚, 吴丹红, 熊明强, 蒲云. 基于社会力的驾驶员换道决策行为建模[J]. 西南交通大学学报, 2018, 53(4): 791-797.
YANG Da, SU Gang, WU Danhong, XIONG Mingqiang, PU Yun. Modelling Drivers' Lane-Changing Decision Behaviour Based on Social Force. Journal of SouthWest JiaoTong University, 2018, 53(4): 791-797.
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