Intelligent vehicle driving decision-making model based on variational AutoEncoder network and deep reinforcement learning
In this paper, an end-to-end driving decision-making model is proposed for intelligent vehicle, utilizing a Variational AutoEncoder (VAE) network and Deep Reinforcement Learning to address the challenges in complex and dynamical driving environments. Firstly, the traffic environment image features a...
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| Published in: | Expert systems with applications Vol. 268; p. 126319 |
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| Main Authors: | , , , , |
| Format: | Journal Article |
| Language: | English |
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Elsevier Ltd
05.04.2025
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| ISSN: | 0957-4174 |
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| Abstract | In this paper, an end-to-end driving decision-making model is proposed for intelligent vehicle, utilizing a Variational AutoEncoder (VAE) network and Deep Reinforcement Learning to address the challenges in complex and dynamical driving environments. Firstly, the traffic environment image features are extracted by VAE network, which can effectively reduce the amount of data input and improve the learning efficiency. Secondly, the Soft Actor-Critic (SAC) algorithm is improved through the application of TD error value constraints, N-step learning, etc. Then driving risk field and rule constraints are introduced into the improve SAC algorithm. Based on the real-time driving risk field, the skipping frame method can enhance learning efficiency, and the rule constraints can reduce the dangerous actions in the output of the algorithm. In order to verify the effectiveness of the model, in the CARLA simulation platform the models of scenario and algorithm are established, and the simulations are carried out. The results show that using decision-making model built by the proposed algorithm, the average driving distance by the intelligent vehicle has been improved by 91.37%, the average reward value of the task has been increased by 132.04%, the average success rate of the task has been improved by 46.56%, the training time is also significantly reduced. It demonstrated that the proposed decision-making model provides a significant improvement in driving safety and learning efficiency. |
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| AbstractList | In this paper, an end-to-end driving decision-making model is proposed for intelligent vehicle, utilizing a Variational AutoEncoder (VAE) network and Deep Reinforcement Learning to address the challenges in complex and dynamical driving environments. Firstly, the traffic environment image features are extracted by VAE network, which can effectively reduce the amount of data input and improve the learning efficiency. Secondly, the Soft Actor-Critic (SAC) algorithm is improved through the application of TD error value constraints, N-step learning, etc. Then driving risk field and rule constraints are introduced into the improve SAC algorithm. Based on the real-time driving risk field, the skipping frame method can enhance learning efficiency, and the rule constraints can reduce the dangerous actions in the output of the algorithm. In order to verify the effectiveness of the model, in the CARLA simulation platform the models of scenario and algorithm are established, and the simulations are carried out. The results show that using decision-making model built by the proposed algorithm, the average driving distance by the intelligent vehicle has been improved by 91.37%, the average reward value of the task has been increased by 132.04%, the average success rate of the task has been improved by 46.56%, the training time is also significantly reduced. It demonstrated that the proposed decision-making model provides a significant improvement in driving safety and learning efficiency. |
| ArticleNumber | 126319 |
| Author | Wang, Xinkai Wang, Zhengli Liang, Qingwei Meng, Lingyi Wang, Shufeng |
| Author_xml | – sequence: 1 givenname: Shufeng orcidid: 0000-0002-1169-6790 surname: Wang fullname: Wang, Shufeng email: shufengwang@sdust.edu.cn organization: Shandong University of Science and Technology, 579 Qianwangang Road, Huangdao District, Qingdao, Shandong Province, 266590, PR China – sequence: 2 givenname: Zhengli orcidid: 0009-0009-1098-2937 surname: Wang fullname: Wang, Zhengli organization: Shandong University of Science and Technology, 579 Qianwangang Road, Huangdao District, Qingdao, Shandong Province, 266590, PR China – sequence: 3 givenname: Xinkai surname: Wang fullname: Wang, Xinkai organization: Shandong University of Science and Technology, 579 Qianwangang Road, Huangdao District, Qingdao, Shandong Province, 266590, PR China – sequence: 4 givenname: Qingwei surname: Liang fullname: Liang, Qingwei organization: Zhongtong Bus Holding Co., Ltd, No. 261 Huanghe Road, Economic Development Zone, Liaocheng, Shandong Province, 252000, PR China – sequence: 5 givenname: Lingyi surname: Meng fullname: Meng, Lingyi organization: Shandong University of Science and Technology, 579 Qianwangang Road, Huangdao District, Qingdao, Shandong Province, 266590, PR China |
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| SubjectTerms | AutoEncoder Network Driving Decision-making Driving risk field Intelligent Vehicle Rule constraints Soft Actor-Critic algorithm |
| Title | Intelligent vehicle driving decision-making model based on variational AutoEncoder network and deep reinforcement learning |
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