EcoFollower: An Environment-Friendly Car Following Model Considering Fuel Consumption
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arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| Soggetti: | |
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| _version_ | 1866929452242436096 |
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| author | Zhong, Hui Chen, Xianda Tiu, PakHin Lu, Hongliang Zhu, Meixin |
| author_facet | Zhong, Hui Chen, Xianda Tiu, PakHin Lu, Hongliang Zhu, Meixin |
| contents | To alleviate energy shortages and environmental impacts caused by transportation, this study introduces EcoFollower, a novel eco-car-following model developed using reinforcement learning (RL) to optimize fuel consumption in car-following scenarios. Employing the NGSIM datasets, the performance of EcoFollower was assessed in comparison with the well-established Intelligent Driver Model (IDM). The findings demonstrate that EcoFollower excels in simulating realistic driving behaviors, maintaining smooth vehicle operations, and closely matching the ground truth metrics of time-to-collision (TTC), headway, and comfort. Notably, the model achieved a significant reduction in fuel consumption, lowering it by 10.42\% compared to actual driving scenarios. These results underscore the capability of RL-based models like EcoFollower to enhance autonomous vehicle algorithms, promoting safer and more energy-efficient driving strategies. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_03950 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | EcoFollower: An Environment-Friendly Car Following Model Considering Fuel Consumption Zhong, Hui Chen, Xianda Tiu, PakHin Lu, Hongliang Zhu, Meixin Robotics Artificial Intelligence To alleviate energy shortages and environmental impacts caused by transportation, this study introduces EcoFollower, a novel eco-car-following model developed using reinforcement learning (RL) to optimize fuel consumption in car-following scenarios. Employing the NGSIM datasets, the performance of EcoFollower was assessed in comparison with the well-established Intelligent Driver Model (IDM). The findings demonstrate that EcoFollower excels in simulating realistic driving behaviors, maintaining smooth vehicle operations, and closely matching the ground truth metrics of time-to-collision (TTC), headway, and comfort. Notably, the model achieved a significant reduction in fuel consumption, lowering it by 10.42\% compared to actual driving scenarios. These results underscore the capability of RL-based models like EcoFollower to enhance autonomous vehicle algorithms, promoting safer and more energy-efficient driving strategies. |
| title | EcoFollower: An Environment-Friendly Car Following Model Considering Fuel Consumption |
| topic | Robotics Artificial Intelligence |
| url | https://arxiv.org/abs/2408.03950 |