Decision-making method of autonomous vehicles for right of way on road segments

被引：0
|
作者：
Cao N.-B. [1 ]
Zhao L.-Y. [2 ]
机构：
[1] College of Transportation Engineering, Chang'an University, Xi'an
[2] School of Economics and Management, Xi'an University of Technology, Xi'an
来源：
Zhejiang Daxue Xuebao (Gongxue Ban)/Journal of Zhejiang University (Engineering Science) | 2022年 / 56卷 / 01期
关键词：
Autonomous vehicle; Decision-making model; Negotiation model; Pedestrian; Perceived risk; Traffic engineering and traffic management;
D O I：
10.3785/j.issn.1008-973X.2022.01.013
中图分类号：
学科分类号：
摘要：
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. Copyright ©2022 Journal of Zhejiang University (Engineering Science). All rights reserved.
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收藏
页码：118 / 127
页数：9
相关论文
共 21 条
  • [1] AMIRNAZMIAFSHAR E, TEZCAN H O., An analysis of pedestrian waiting time at uncontrolled crosswalks using discrete choice model, International Conference on Transportation and Development, pp. 25-38, (2020)
  • [2] ZHANG C, CHEN F, WEI Y., Evaluation of pedestrian crossing behavior and safety at uncontrolled mid-block crosswalks with different numbers of lanes in China, Accident Analysis and Prevention, 123, pp. 263-273, (2019)
  • [3] LI B., A model of pedestrians' intended waiting times for street crossings at signalized intersections, Transportation Research Part B: Methodological, 51, pp. 17-28, (2013)
  • [4] GUO H, WANG W, GUO W, Et al., Reliability analysis of pedestrian safety crossing in urban traffic environment, Safety Science, 50, 4, pp. 968-973, (2012)
  • [5] KOH P P, WONG Y D., Gap acceptance of violators at signalized pedestrian crossings, Accident Analysis and Prevention, 62, pp. 178-185, (2014)
  • [6] SOATHONG A, CHOWDHURY S, WILSON D, Et al., Investigating the motivation for pedestrians ' risky crossing behaviour at urban mid-block road sections, Travel Behaviour and Society, 22, pp. 155-165, (2021)
  • [7] MERLINO S, MONDADA L., Crossing the street: how pedestrians interact with cars, Language and Communication, 65, pp. 131-147, (2019)
  • [8] BREMNER P, LEONARDS U., Iconic gestures for robot avatars, recognition and integration with speech
  • [9] CHEN H, COHEN R, DAUTENHAHN K, Et al., Autonomous vehicle visual signals for pedestrians: experiments and design recommendations, 2020 IEEE Intelligent Vehicles Symposium, pp. 1819-1826, (2020)
  • [10] KIM K, KIM Y, KIM H, Et al., Method and device for signaling present driving intention of autonomous vehicle to humans by using various V2X-enabled application
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