Drivers' Lane Choice Model Based on Mixed Fuzzy Multi-person and Multi-objective Non-cooperative Game
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摘要: 建立汽车安全驾驶辅助系统（包括安全驾驶预警系统）是保证交通安全的有效手段.准确预测车辆集群态势是汽车安全辅助驾驶的前提，车道选择是车辆集群态势发生转移最为根本的原因，也是交通流理论研究的基本内容.以往研究没有综合考虑车辆集群复杂态势下各运动实体特征及其操控者类型，以及多个车道间车辆的冲突对车道选择的影响.为此，本文综合考虑各运动实体特征及其操控者类型，基于混合模糊多人多目标非合作博弈方法，建立城市快速路基本路段上的驾驶员车道选择模型.通过分析各方驾驶员在不同车道选择策略下的收益，确定换道博弈的Nash均衡，得到驾驶员最优车道选择策略.研究结果表明：基于混合模糊多人多目标非合作博弈方法建构的驾驶员车道选择模型，其预测准确率可达到85.2%.Abstract: Vehicle safety driving assistance system (including driving safety alerting system) is an effective means to ensure traffic safety while accurate prediction of vehicle cluster situation is the premise of automobile safety assistant driving system. The drivers' lane choice process is not only the root cause of the transformation of vehicle cluster situation, but also the basic topic of traffic flow research. Previous studies do not synthetically consider the characteristics of individual traffic entities and the types of manipulators under the complex vehicle cluster situation, neither the influence of vehicles conflicts under multiple lanes on lane choice is taken into account. Therefore, in this paper, considering the types of vehicle manipulators and the characteristics of each movement entity, the model of drivers' lane choice on basic segment of urban expressway is built based on mixed fuzzy multi-person and multi-objective non-cooperative game. Drivers' profits under the different combinations of lane choice behaviors are analyzed, Nash equilibrium in the game process is confirmed, and the drivers' optimal lane choice strategy in a dynamic game is obtained. The results show that the model's prediction accuracy of lane change reaches 85.2%.1) 本文责任编委 董海荣
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图 1 三车道场景目标车所处车辆集群态势图
Fig. 1 Vehicle cluster situation for target vehicle under the three-lane condition
图 2 三车道场景驾驶员车道选择博弈分析图
Fig. 2 Game analysis of drivers' lane choice behavior under three-lane condition
图 5 动态人车环境信息采集系统组成
Fig. 5 Dynamic human-vehicle-environment information acquisition systems
图 7 保守型、普通型、激进型驾驶员换道频率模拟结果
Fig. 7 Simulation results of lane changing frequency for conservative, common, and radical drivers
图 8 换道次数仿真值与实际测量值对比图
Fig. 8 Comparison of simulation and measured values in lane-changing times
图 9 左车道、中间车道和右车道利用率仿真值与实际测量值对比图
Fig. 9 Comparison of simulation and measured values for left, middle, and right lanes in lane occupancy rate
表 1 不同类型驾驶员感知各分区域车辆对目标车综合作用力的贡献率
Table 1 Contribution rates of integrated force from different types of drivers in sub-area to the target vehicle
目标车位置 目标车位于左车道 目标车位于中间车道 目标车位于右车道 贡献率驾驶员类型 $\xi _i^{ql}$ $\xi _i^{hl}$ $\xi _i^{lyq}$ $\xi _i^{lyq}$ $\xi _i^{gyq}$ $\xi _i^{gyh}$ $\xi _i^q $ $\xi _i^h $ $\xi _i^{zq} $ $\xi _i^q $ $\xi _i^h $ $\xi _i^{zq} $ $\xi _i^{ql} $ $\xi _i^{hl} $ $\xi _i^{lyq} $ $\xi _i^{lyq} $ $\xi _i^{gyq} $ $\xi _i^{gyh} $ 激进型 0.244 0.161 0.186 0.226 0.086 0.097 0.219 0.152 0.166 0.219 0.152 0.166 0.244 0.161 0.186 0.226 0.086 0.097 普通型 0.251 0.201 0.181 0.193 0.081 0.093 0.226 0.187 0.152 0.226 0.187 0.152 0.251 0.201 0.181 0.193 0.081 0.093 保守型 0.265 0.236 0.173 0.168 0.076 0.082 0.241 0.191 0.111 0.241 0.191 0.111 0.265 0.236 0.173 0.168 0.076 0.082 
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表 2 冲突收益语义项与三角形模糊数之间的对应关系
Table 2 Correspondence between semantic driving conflict items and triangular fuzzy numbers
冲突收益评价语义项 三角形模糊数 冲突收益评价语义项 三角形模糊数 很差 (0, 0, 0.2) 较好 (0.6, 0.7, 0.8) 差 (0, 0.1, 0.3) 好 (0.7, 0.8, 0.9) 较差 (0, 0.2, 0.4) 很好 (0.8, 0.9, 1) 中等 (0.3, 0.5, 0.7) 非常好 (0.9, 1, 1)
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表 3 不同类型驾驶员对各收益的模糊权重情况
Table 3 Fuzzy weight of the beneflt for difierent types of drivers
驾驶员类型 模糊权重 行车安全收益${\not{\omega }}_i^1 $ 行车时间收益${\not{\omega }}_i^2$ 冲突收益${\not{\omega }}_i^3 $ 激进型 0.3 0.5 0.2 普通型 0.35 0.4 0.25 保守型 0.4 0.3 0.3
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表 4 实验可采集数据
Table 4 Collected experiment data types
数据 目标车与周围车辆相对距离(m) 目标车与周围车辆相对速度(m/s) 左前车 左后车 前车 后车 右前车 右后车 左前车 左后车 前车 后车 右前车 右后车 代码 $\Delta d_2 $ $\Delta d_3 $ $\Delta d_4 $ $\Delta d_5$ $\Delta d_6 $ $\Delta d_7 $ $\Delta v_2 $ $\Delta v_3 $ $\Delta v_4 $ $\Delta v_5 $ $\Delta v_6 $ $\Delta v_7 $
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表 5 车道选择模型参数标定
Table 5 Parameter calibration of lane choice model
参数 $v_1 $ $v_2 $ $v_3 $ $v_4 $ $d_1 $ $d_2 $ $d_3 $ $d_4 $ 数值 激进型 -3.2 -1.7 1.7 3.2 8.3 22.4 36.2 50.5 普通型 -4.8 -2.5 2.5 4.8 12.6 25.8 40.1 60.5 保守型 -8.7 -4.5 4.5 8.7 22.7 35.3 50.5 70.5
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表 6 道路实车实验结果
Table 6 Verification results of actual driving experiment
驾驶员编号 预测次数 预测结果与实测结果对比 准确率(%) 相符次数 不符次数 1 80 71 9 88.8 2 80 68 12 85.0 3 80 67 13 83.8 4 80 69 11 86.3 5 80 65 15 81.3 6 80 67 13 83.8 7 80 70 10 87.5 8 80 68 12 85.0 均值 80 68.125 11.875 85.16
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表 7 驾驶模拟实验结果
Table 7 Verification results of virtual driving experiment
驾驶员编号 预测次数 预测结果与实测结果对比 准确率(%) 相符次数 不符次数 1 80 70 10 87.5 2 80 69 11 86.3 3 80 66 14 82.5 4 80 71 9 88.8 5 80 67 13 83.8 6 80 70 10 87.5 7 80 65 15 81.3 8 80 68 12 85.0 均值 80 68.25 11.75 85.31
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表 8 微观仿真结果与实测数据对比分析表
Table 8 Comparative analysis of microscopic simulation and measured values
评价指标 实测值 模拟1结果 误差(%) 平均速度(m/s) 10.2 10.66 4.51 平均密度(辆/km) 19.3 18.52 4.04 平均延误(s) 7.2 6.93 3.75
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