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| Design of energy-saving driving strategy based on proximal policy optimization considering urban transport information |
| QifangLiu1,2,DazhenSun1,HaowenChen1,DongziLi1,PingWang1,2 |
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| (1 Department of Control Science and Engineering, Jilin University, Changchun 130022, Jilin, China
2 State Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun 130022, Jilin, China) |
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| 摘要: |
| Eco-driving has always been an ongoing topic. In urban driving conditions, traffic regulations, other vehicle behaviors, and
special driving scenarios will have a major impact on the energy consumption of autonomous vehicles. As a representative
algorithm of artificial intelligence, reinforcement learning has the ability to perform well under complex tasks. This paper
uses deep reinforcement learning algorithms to design the economical driving strategies of autonomous vehicles in three
driving scenarios: driving at signalized intersection under free traffic flow, car-following on ramps, and driving at signalized
intersection considering queue effects. In the above three driving scenarios, the driving strategy proposed in this paper achieves
economical driving performance while satisfying the driving scenario requirements. |
| 关键词: Eco-driving strategy · Reinforcement learning · Signalized intersection · Car-following |
| DOI：https://doi.org/10.1007/s11768-024-00233-7 |
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| 基金项目:This work was supported in part by the National Natural Science Foundation of China under Grant No. 62073152 and in part by the Jilin Province Science and Technology Development Plan, China under Grant No. 20220201034GX. |
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| Design of energy-saving driving strategy based on proximal policy optimization considering urban transport information |
| Qifang Liu1,2,Dazhen Sun1,Haowen Chen1,Dongzi Li1,Ping Wang1,2 |
| (1 Department of Control Science and Engineering, Jilin University, Changchun 130022, Jilin, China
2 State Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun 130022, Jilin, China) |
| Abstract: |
| Eco-driving has always been an ongoing topic. In urban driving conditions, traffic regulations, other vehicle behaviors, and
special driving scenarios will have a major impact on the energy consumption of autonomous vehicles. As a representative
algorithm of artificial intelligence, reinforcement learning has the ability to perform well under complex tasks. This paper
uses deep reinforcement learning algorithms to design the economical driving strategies of autonomous vehicles in three
driving scenarios: driving at signalized intersection under free traffic flow, car-following on ramps, and driving at signalized
intersection considering queue effects. In the above three driving scenarios, the driving strategy proposed in this paper achieves
economical driving performance while satisfying the driving scenario requirements. |
| Key words: Eco-driving strategy · Reinforcement learning · Signalized intersection · Car-following |
