车路协同环境下群体车辆诱导与协同运行方法

上官伟; 庞晓宇; 李秋艳; 柴琳果

doi:10.19818/j.cnki.1671-1637.2022.03.005

车路协同环境下群体车辆诱导与协同运行方法

doi: 10.19818/j.cnki.1671-1637.2022.03.005

1.
北京交通大学电子信息工程学院，北京 100044
2.
北京交通大学轨道交通控制与安全国家重点实验室，北京 100044

基金项目:

国家重点研发计划 2018YFB1600600

民航机场群智慧运营重点实验室开放基金课题 KLAGI020180901

国家自然科学基金项目 61773049

详细信息

作者简介:
上官伟(1979-)，男，陕西乾县人，北京交通大学教授，工学博士，从事交通信息工程及控制研究

中图分类号: U491
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- 被引次数: 0
出版历程
- 收稿日期: 2021-12-16
- 刊出日期: 2022-06-25

Guidance and cooperative operation method for group vehicles in vehicle-infrastructure cooperative environment

1.
School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing 100044, China
2.
State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing 100044, China

Funds:

National Key Research and Development Program of China 2018YFB1600600

Open Research Fund for Key Laboratory of Intelligent Operation of Civil Aviation Airport Group KLAGI020180901

National Natural Science Foundation of China 61773049

More Information

Author Bio:
SHANGGUAN Wei(1979-), male, professor, PhD, wshg@bjtu.edu.cn

摘要

摘要: 为解决城市发展带来的交通拥堵问题，发掘道路交通的潜力，提高车路协同环境下车辆在路网中的行驶效率，面向群体车辆提出了一种诱导优化方法和协同控制策略；在车辆诱导分配方面，在起始点和目的地之间的可达路径中，以交通效率最优、车辆排放最小为目标，设计了基于道路饱和度、车辆行程时间和延误的群体车辆分配规则，建立了群体车辆诱导分配优化模型，并用多目标非支配排序遗传算法-Ⅱ(NSGA-Ⅱ)和多目标粒子群优化算法进行求解；在车辆协同运行控制策略方面，基于引力场思想建立了多车协同运行模型，并提出了多车协同加减速策略；通过仿真验证比较了不同网联自动驾驶车辆(CAV)渗透率下的车辆诱导优化结果，同时仿真了车辆协同加减速策略，并将诱导优化方法和协同控制策略进行了联合仿真。仿真结果表明：多目标诱导分配方法可以提升车辆速度和环境效益，且群体车辆平均速度与CAV渗透率正相关；在四车组队行驶环境中，车辆协同加减速策略能够将车辆在加速和减速时的初始平均加速度分别提高15.0%和8.2%，让车辆快速达到目标速度，保障行车安全；在联合仿真环境中，路网群体车辆的加速度平均提高了11.6%，速度平均提高了1.6%，碳氧化合物排放量减少约4.9%。由此可见，提出的方法能够提高路网通行效率，降低车辆能源消耗，减少对环境造成的不良影响。
- 智能交通 /
- 车路协同 /
- 群体智能 /
- 交通流诱导 /
- 多目标优化 /
- 协同控制策略
Abstract: To solve the traffic congestion problem caused by urban development, explore the potential of road traffic, and improve the driving efficiency of vehicles in the road network in vehicle-infrastructure cooperative environments, a guidance optimization method and a cooperative contral strategy for group vehicles were proposed. For the vehicle guidance allocation, the group vehicles allocation rules based on the road saturation, vehicle travel time, and delay were designed with the goals of optimal traffic efficiency and minimum vehicle emissions by the feasible path between the starting point and the destination. An optimization model for the group vehicles guidance allocation was built and solved by the multi-objective non-dominated sorting genetic algorithms-Ⅱ (NSGA-Ⅱ) and the multi-objective particle swarm optimization algorithm. Regarding the strategy for the vehicle cooperative operation control, a multi-vehicle cooperative operation model based on the idea of the gravitational field was created, and a multi-vehicle cooperative acceleration and deceleration strategy was proposed. The results of vehicle guidance optimization under different penetration rates of connected and automated vehicle (CAV) were compared through the simulation verification. The vehicle cooperative acceleration and deceleration strategy was simulated, and the guidance optimization method and the cooperative control strategy were co-simulated. Simulation results show that the multi-objective guidance allocation method can improve the vehicle speed and environmental benefits, and the average speed of the group vehicles is positively correlated with the CAV penetration rate. In the four-car group driving environment, the cooperative acceleration and deceleration strategy can increase the initial average acceleration of the vehicle by 15.0% and 8.2% respectively, during the acceleration and deceleration. The vehicle can quickly reach the target speed, and the safety of the vehicles can thereby be ensured. In the co-simulation environment, the accelerations of the group vehicles in the road network increase by 11.6% on average, their speeds increase by 1.6% on average, and their carbon-oxygen compound emissions reduce by about 4.9%. Therefore, the proposed method can be employed to improve the traffic efficiency of the road network, reduce the energy consumption of vehicles, and lower the adverse impact on the environment. 2 tabs, 10 figs, 31 refs.
- intelligent transportation /
- vehicle-infrastructure cooperation /
- group intelligence /
- traffic flow guidance /
- multi-objective optimization /
- cooperative control strategy

HTML全文

图 1 方法流程

Figure 1. Method flow

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图 2 多车协同运行

Figure 2. Multi-vehicle cooperative operation

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图 3 仿真路网

Figure 3. Simulation road network

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图 4 NSGA-Ⅱ帕累托前沿

Figure 4. NSGA-Ⅱ Pareto fronts

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图 5 多目标粒子群优化算法帕累托前沿

Figure 5. Pareto fronts of multi-objective particle swarm optimization algorithm

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图 6 诱导前后车辆的平均速度、二氧化碳排放量、一氧化碳排放量对比

Figure 6. Comparison of vehicle average speeds, carbon dioxide emissions and carbon monoxide emissions before and after guidance

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图 7 不同CAV渗透率条件下200辆车的平均速度

Figure 7. Average speeds of 200 vehicles with different CAV penetration rates

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图 8 车辆协同加速运行的加速度对比

Figure 8. Acceleration comparison of vehicle cooperative acceleration operation

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图 9 车辆协同减速运行的减速度对比

Figure 9. Deceleration comparison of vehicle cooperative deceleration operation

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图 10 联合仿真结果对比

Figure 10. Comparison of co-simulation results

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表 1 道路拥挤程度和服务水平

Table 1. Road congestion and service levels

服务水平	S	拥挤程度
一级	(0, 0.6]	顺畅
二级	(0.6, 0.8]	轻微拥堵
三级	(0.8, 1.0]	拥堵
四级	>1.0	严重拥堵

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表 2 路径组成

Table 2. Path compositions

路径标号	路径组成
1	1-2-3-6-9
2	1-2-5-6-9
3	1-4-5-6-9
4	1-4-7-6-9
5	1-4-7-8-9

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