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Human-like Decision Making for Autonomous Vehicles at the Intersection Using Inverse Reinforcement Learning.
Wu, Zheng; Qu, Fangbing; Yang, Lin; Gong, Jianwei.
Afiliação
  • Wu Z; Vehicle Engineering Department, Inner Mongolia Technical College of Mechanics and Electrics, Huhehot 010070, China.
  • Qu F; School of Mechanical and Engineering, Beijing Institute of Technology, Beijing 100081, China.
  • Yang L; School of Mechanical and Engineering, Beijing Institute of Technology, Beijing 100081, China.
  • Gong J; School of Mechanical and Engineering, Beijing Institute of Technology, Beijing 100081, China.
Sensors (Basel) ; 22(12)2022 Jun 14.
Article em En | MEDLINE | ID: mdl-35746281
With the rapid development of autonomous driving technology, both self-driven and human-driven vehicles will share roads in the future and complex information exchange among vehicles will be required. Therefore, autonomous vehicles need to behave as similar to human drivers as possible, to ensure that their behavior can be effectively understood by the drivers of other vehicles and be more in line with the cognition of humans on driving behavior. Therefore, this paper studies the evaluation function of human drivers, using the method of inverse reinforcement learning, aiming for the learned behavior to better imitate the behavior of human drivers. At the same time, this paper proposes a semi-Markov model, to extract the intentions of surrounding related vehicles and divides them into defensive and cooperative, leading the vehicle to adopt a reasonable response to different types of driving scenarios.
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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Condução de Veículo / Acidentes de Trânsito Limite: Humans Idioma: En Ano de publicação: 2022

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Condução de Veículo / Acidentes de Trânsito Limite: Humans Idioma: En Ano de publicação: 2022