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Decision-Making for Autonomous Driving in Uncertain Environment
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Xinke Fu1, Yingfeng Cai1, Long Chen1, Hai Wang2, Qingchao Liu2
Automotive Engineering | 2024, 46(2) : 211 - 221
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Automotive Engineering | 2024, 46(2): 211-221
Feature Topic:Key Technologies on Intelligent and Connected Vehicles
Decision-Making for Autonomous Driving in Uncertain Environment
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Xinke Fu1, Yingfeng Cai1, Long Chen1, Hai Wang2, Qingchao Liu2
Affiliations
  • 1. Institute of Automotive Engineering,Jiangsu University,Zhenjiang 212013
  • 2. School of Automotive and Traffic Engineering,Jiangsu University,Zhenjiang 212013
Published: 2024-02-25 doi: 10.19562/j.chinasae.qcgc.2024.02.003
Outline
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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.

autonomous vehicles  /  decision-making  /  POMDP  /  complex network
Xinke Fu, Yingfeng Cai, Long Chen, Hai Wang, Qingchao Liu. Decision-Making for Autonomous Driving in Uncertain Environment[J]. Automotive Engineering, 2024 , 46 (2) : 211 -221 . DOI: 10.19562/j.chinasae.qcgc.2024.02.003
Year 2024 volume 46 Issue 2
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Article Info
doi: 10.19562/j.chinasae.qcgc.2024.02.003
  • Receive Date:2023-05-12
  • Online Date:2025-07-20
  • Published:2024-02-25
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History
  • Received:2023-05-12
  • Revised:2023-07-31
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Affiliations
    1. Institute of Automotive Engineering,Jiangsu University,Zhenjiang 212013
    2. School of Automotive and Traffic Engineering,Jiangsu University,Zhenjiang 212013
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https://castjournals.cast.org.cn/joweb/qcygc/EN/10.19562/j.chinasae.qcgc.2024.02.003
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表12种不同金属材料的力学参数

Family
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Number of
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Number of
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鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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