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浙江大学学报(工学版)
Journal of ZheJiang University (Engineering Science)  2022, Vol. 56 Issue (1): 118-127    DOI: 10.3785/j.issn.1008-973X.2022.01.013
    
Decision-making method of autonomous vehicles for right of way on road segments
Ning-bo CAO1(),Li-ying ZHAO2,*()
1. College of Transportation Engineering, Chang'an University, Xi’an 710061, China
2. School of Economics and Management, Xi’an University of Technology, Xi’an 710048, China
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Abstract  

A decision-making method for the right of way based on acceptable gap model and negotiation theory was developed in order to solve the decision-making problem of autonomous vehicles for the right of way on road segments and improve the efficiency and stability of traffic flow. Various factors were comprehensively considered to model the perceived risk based on the acceptable gap model. Perceived risk was divided into low risk, medium risk and high risk. The potential behavior strategies of pedestrians and autonomous vehicles under different combinations of above factors were analyzed by comprehensively considering the impact of risk, personality (radical and conservative) and waiting time on pedestrians’ behaviors. The negotiation theory was used to model the process of decision-making for the right of way based on these behavior strategies. The model was simulated and verified by using Python and SUMO for ten hours. The simulation of three models (conservative model, Gupta model and our model) was conducted. When pedestrian generation frequency was 15 s, the average travel time of autonomous vehicles was 661.5, 399.5 and 327.6 s respectively; the average delay was 618, 336 and 260.7 s respectively; the total traffic volume was 6 699, 10 583 and 11 568 vehicles respectively. When the pedestrian generation frequency was 30 s, the average travel time of autonomous vehicles was 643.5, 311.7 and 81.9 s respectively; the average delay was 599.9, 244.4 and 6.5 s; the total traffic volume was 6 879, 11 741 and 11 971 vehicles respectively. The introduction of decision-making model helps to reduce the travel time and delay of the autonomous vehicles, and increase the traffic volume.

Key words： traffic engineering and traffic management      autonomous vehicle      decision-making model      negotiation model      pedestrian      perceived risk     
Received: 14 February 2021      Published: 05 January 2022
CLC:  U 491  
Fund:  陕西省自然科学基础研究计划资助项目(2021JQ-279)；陕西省教育厅重点科学研究计划资助项目(21JZ005)；西安理工大学青年教师研究资助项目（256081921）
Corresponding Authors: Li-ying ZHAO     E-mail: 819868226@qq.com;lyzhao@xaut.edu.cn
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Ning-bo CAO
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Cite this article:

Ning-bo CAO,Li-ying ZHAO. Decision-making method of autonomous vehicles for right of way on road segments. Journal of ZheJiang University (Engineering Science), 2022, 56(1): 118-127.

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https://www.zjujournals.com/eng/10.3785/j.issn.1008-973X.2022.01.013     OR     https://www.zjujournals.com/eng/Y2022/V56/I1/118


路段环境自动驾驶汽车通行权决策方法

为了解决路段自动驾驶汽车的通行权决策问题，提高交通流的运行效率和稳定性，基于可接受间隙模型和谈判理论构建路段自动驾驶汽车通行权决策模型. 综合考虑多种因素，基于可接受间隙模型对行人风险进行建模，将行人风险划分为低风险、中风险和高风险. 综合考虑风险、性格（激进和保守）和等待时间等对行人行为的影响，分析不同因素组合下行人和自动驾驶汽车可能采取的行为策略，基于该行为策略，利用谈判理论对自动驾驶汽车的通行权决策过程进行建模. 利用Python联合SUMO开源交通仿真软件对模型进行验证，仿真持续10 h. 3个模型的（保守模型、Gupta模型和本文模型）仿真结果表明，当行人产生频率为15 s时，自动驾驶汽车的平均行驶时间分别为661.5、399.5和327.6 s，平均延误时间分别为618 s、336 s和260.7 s，总流量分别为6 699辆、10 583辆和11 568辆. 当行人产生频率为30 s时，自动驾驶汽车的平均行驶时间分别为643.5、311.7和81.9 s，平均延误时间分别为599.9、244.4和6.5 s，总流量分别为6 879辆、11 741辆和11 971辆. 通行权决策方法的加入有助于降低自动驾驶汽车的行驶时间和延误，提升流量.


关键词： 交通工程与交通管理,  自动驾驶汽车,  通行权决策,  谈判模型,  行人,  感知风险 
Fig.1 Scenario of interaction between pedestrian and vehicle on road section
$ {\text{PRv}}(t) $ 自动驾驶汽车和行人的对应行为
$ 0 \leqslant {\text{PRv}}(t) \leqslant {G_1} $ 无需通行权决策，行人和自动驾驶汽车均取得通行权. 行人继续加速，直至达到最大速度；自动驾驶汽车继续保持当前的运动状态，随时监视行人的行为.
$ {G_1} < {\text{PRv}}(t) \leqslant {G_2} $ 自动驾驶汽车启动通行权决策过程如下：1）如果自动驾驶汽车无法停车（自动驾驶汽车发出警告），则自动驾驶汽车获得通行权，优先通过；2）若行人保守且等待时间小于20 s，则自动驾驶汽车获得通行权，优先通过；3）若行人保守且等待时间大于20 s，则行人获得通行权，优先过街，自动驾驶汽车减速/停车；4）若行人激进，则自动驾驶汽车减速，行人获得通行权，优先通过.
$ {G_2} < {\text{PRv}}(t) \leqslant 1 $ 无需通行权决策. 不管行人是保守的还是激进的，自动驾驶汽车获得通行权，加速，直至达到最大速度，行人让行.
Tab.1 Corresponding behavior strategy of autonomous vehicles
Fig.2 Flowchart diagram of proposed decision-making model
Fig.3 Simulation scenario of self-driving vehicles and pedestrians
Fig.4 Travel time of vehicles when generating one pedestrian every 15 seconds
Fig.5 Travel time of vehicles when generating one pedestrian every 30 seconds
Fig.6 Delay of vehicles when generating one pedestrian every 15 seconds
Fig.7 Delay of vehicles when generating one pedestrian every 30 seconds
Fig.8 Throughput of vehicles of three models
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