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摘要: 为宏观刻画自动驾驶专用车道上的网联自动驾驶车辆(CAV)下匝道的行为，提出了混合交通流下基于安全风险的CAV下匝道换道决策模型；该模型将换道间隙选择过程抽象为成功换道或不成功换道的伯努利试验，并在此基础上建立了基于交通流理论的车辆换道成功率计算方法；提出了耦合换道安全与效率的下匝道换道决策成本函数，其中安全与效率的权重参数根据不同的驾驶模式确定，从而确定CAV最优的换道意图生成点，为CAV换道提供指令。数值分析结果表明：CAV下匝道成功率由换道准备距离、交通需求和CAV渗透率共同决定，成本函数随着CAV渗透率的变化出现明显的拐点；交通量为2 400 veh·h-1时，CAV的最佳换道意图生成点为距离下匝道入口1 km处；当交通量增加至4 000 veh·h-1时，最佳换道意图生成点为距离下匝道入口2.5 km处；当交通量大于6 400 veh·h-1时，需要提高CAV的侵略性才能高效驶出高速公路；成本函数随着CAV渗透率的增大先下降再升高，若渗透率低于拐点渗透率，则增加换道准备距离可以降低成本函数，若渗透率高于拐点渗透率，则需通过减小换道准备距离降低成本函数。仿真结果表明：交通需求和渗透率对车辆下匝道的安全性影响显著，渗透率由30%提升至60%，碰撞时间最大降幅为76.23%。Abstract: A safety risk-based lane-changing decision model of connected and automated vehicles (CAVs) driving off the ramp in mixed traffic flow was proposed to macroscopically characterize the behaviors of CAVs driving off the ramp in the CAV lane. The model abstracted the lane-changing gap selection process into Bernoulli experiments of successful or unsuccessful lane-changing, and a lane-changing success rate formula was set up based on the traffic flow theory. A driving off ramp lane-changing decision cost function considering lane-changing safety and efficiency was proposed, in which the weight parameters of safety and efficiency were determined based on different driving modes, so as to determined the optimal lane-changing intention generation point for CAVs and provided instructions for CAV lane-changing. Numerical analysis results show that the success rate of CAV driving off the ramp is determined by the preparation distance of lane-changing, traffic demand, and CAV penetration rate. The cost function has an obvious inflection point with the change of CAV penetration rate. When the traffic volume is 2 400 veh·h-1, the optimal lane-changing intention generation point for CAVs is 1 km away from the entrance of the off-ramp. When the traffic volume increases to 4 000 veh·h-1, the optimal lane-changing intention generation point is 2.5 km away from the entrance of the off-ramp. When the traffic volume is greater than 6 400 veh·h-1, the CAV needs to increase aggressiveness to drive off the highway efficiently. The cost function first decreases and then increases as the CAV penetration rate increases. If the penetration rate is lower than the inflection point penetration rate, the cost function can be reduced by increasing the preparation distance of lane-changing. If the penetration rate is higher than the inflection point penetration rate, reducing the preparation distance of lane-changing is necessary to reduce the cost function. Simulated results show that the traffic safety of driving off the ramp significantly influenced by the traffic demand and CAV penetration rate. The time-to-collision reduces to 76.23%, with the penetration rate increasing from 30% to 60%. 2 tabs, 8 figs, 31 refs.
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Key words:
- CAV /
- lane-changing decision model /
- cost function /
- mixed traffic flow /
- numerical simulation /
- CAV penetration rate
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图 2 下匝道成功率数值分析结果
Figure 2. Numerical analysis results of success rates in driving off ramp
图 7 成本函数参数敏感性分析结果
Figure 7. Analysis results of cost function parameters sensitivities
表 1 各车道属性参数
Table 1. Parameters of each lane
车道编号 最低限速/
(m·s-1)最高限速/
(m·s-1)通行能力/
(veh·h-1)tg/s tc/s 1 16.7 22.2 2.0 8 2 22.2 27.8 1.8 8 3 27.8 33.3 1.5 8 4 27.8 33.3 3 400 1.1 8 
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表 2 TIT降低百分比
Table 2. Percentage reductions of TIT
% p D=2 400
veh·h-1D=3 200
veh·h-1D=4 000
veh·h-1D=4 800
veh·h-1D=5 600
veh·h-1D=6 400
veh·h-10.4 69.64 27.29 14.41 14.04 53.56 16.68 0.5 87.06 61.36 39.85 45.61 66.07 22.29 0.6 95.52 90.66 90.64 57.02 69.23 30.11 0.7 99.11 96.70 92.22 82.46 74.77 44.53 0.8 99.55 98.90 96.32 87.72 83.42 77.23 0.9 99.75 99.45 97.48 92.11 96.49 92.72 1.0 99.89 99.85 98.11 97.28 96.89 96.43
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