Code underlying the publication: Safe, Efficient, Comfort, and Energy-Saving Automated Driving Through Roundabout Based on Deep Reinforcement Learning
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This is the code related to the publication:H. Yuan, P. Li, B. Van Arem, L. Kang, H. Farah and Y. Dong, "Safe, Efficient, Comfort, and Energy-Saving Automated Driving Through Roundabout Based on Deep Reinforcement Learning," 2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC), Bilbao, Spain, 2023, pp. 6074-6079, doi: 10.1109/ITSC57777.2023.10422488. keywords: {Road transportation;Deep learning;Energy consumption;Merging;Reinforcement learning;Safety;Testing}, The implementation is based on Python, Stable-Baselines3 (https://stable-baselines3.readthedocs.io/en/master/) and Highway_env simulation environment https://github.com/Farama-Foundation/HighwayEnv. ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~Traffic scenarios in roundabouts pose substantial complexity for automated driving. Manually mapping all possible scenarios into a state space is labor-intensive and challenging. Deep reinforcement learning (DRL) with its ability to learn from interacting with the environment emerges as a promising solution for training such automated driving models. This study explores, employs, and implements various DRL …
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