Decision-Making for Autonomous Driving in Uncertain Environment

被引：0

作者：

Fu X. ^{[1
]}

Cai Y. ^{[1
]}

Chen L. ^{[1
]}

Wang H. ^{[2
]}

Liu Q. ^{[2
]}

机构：

[1] Institute of Automotive Engineering, Jiangsu University, Zhenjiang

[2] School of Automotive and Traffic Engineering, Jiangsu University, Zhenjiang

来源：

Qiche Gongcheng/Automotive Engineering | 2024年 / 46卷 / 02期

关键词：

autonomous vehicles; complex network; decision-making; POMDP;

D O I：

10.19562/j.chinasae.qcgc.2024.02.003

中图分类号：

学科分类号：

摘要：

In the context of real-world driving environments, due to the perturbation of perception data and the unpredictable behavior of other traffic participants, rational decision-making in highly interactive and intricate driving scenarios considering the impact of uncertainty factors is one of the main concerns that decision-making and planning systems for autonomous vehicles must address. A behavioral decision-making method for autonomous vehicles navigating in uncertain environments is proposed in this paper. To mitigate the impact of uncertainty, the behavioral decision-making process is transformed into a partially observable Markov decision process(POMDP). Furthermore, to tackle the computational complexity of the POMDP model, the complex network theory is applied for the first time for dynamically modeling the microscopic driving environment surrounding the autonomous vehicle, which allows for the effective characterization of interaction relationship between vehicle nodes and the scientific selection of significant vehicle nodes, guiding the autonomous vehicle′s decision-making process, enabling precise identification of critical vehicle nodes, and pruning the decision space. The effectiveness of the proposed method is verified in a simulation environment, and the experimental results show that the proposed method has higher computational efficiency, superior performance, and enhanced flexibility in comparison to existing state-of-the-art behavioral decision-making methods. © 2024 SAE-China. All rights reserved.

引用

页码：211 / 221

页数：10

共 27 条

[1]

SCHWARTING W, ALONSO−MORA J, RUS D., Planning and decision−making for autonomous vehicles[J], Annual Review of Control, Robotics, and Autonomous Systems, 1, 1, (2018)

[2]

LIKMETA A, METELLI A M, TIRINZONI A, Et al., Combining reinforcement learning with rule−based controllers for transparent and general decision−making in autonomous driving[J], Robotics and Autonomous Systems, 131, (2020)

[3]

ZHAO L, ICHISE R, SASAKI Y, Et al., Fast decision making using ontology−based knowledge base, Proceedings of the 2016 IEEE Intelligent Vehicles Symposium(IV), (2016)

[4]

VACEK S, GINDELE T, ZOLLNER J M, Et al., Using case− based reasoning for autonomous vehicle guidance, Proceedings of the 2007 IEEE/RSJ International Conference on Intelligent Robots and Systems, (2007)

[5]

CHEN C, SEFF A, KORNHAUSER A, Et al., DeepDriving: learning affordance for direct perception in autonomous driving [C], Proceedings of the 2015 IEEE International Conference on Computer Vision(ICCV), (2015)

[6]

DESJARDINS C, CHAIB−DRAA B., Cooperative adaptive cruise control:a reinforcement learning approach[J], IEEE Transactions on Intelligent Transportation Systems, 12, 4, pp. 1248-1260, (2011)

[7]

SHU H, LIU T, MU X, Et al., Driving tasks transfer using deep reinforcement learning for decision−making of autonomous vehicles in unsignalized intersection[J], IEEE Transactions on Vehicular Technology, 71, 1, (2022)

[8]

MNIH V, KAVUKCUOGLU K, SILVER D, Et al., Human−level control through deep reinforcement learning[J], Nature, 518, 7540, (2015)

[9]

VITELLI M, NAYEBI A., Carma:a deep reinforcement learning approach to autonomous driving, (2016)

[10]

FU Y, LI C, YU F R, Et al., Hybrid autonomous driving guidance strategy combining deep reinforcement learning and expert system [J], IEEE Transactions on Intelligent Transportation Systems, 23, 8, pp. 11273-11286, (2022)

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