EcoFollower: An Environment-Friendly Car Following Model Considering Fuel Consumption
<|reference_start|>EcoFollower: An Environment-Friendly Car Following Model Considering Fuel Consumption: 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.<|reference_end|>
@article{zhong2024ecofollower:,
    title={EcoFollower: An Environment-Friendly Car Following Model Considering
  Fuel Consumption},
    author={Hui Zhong, Xianda Chen, PakHin Tiu, Hongliang Lu, Meixin Zhu},
    journal={arXiv preprint arXiv:2408.03950},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.03950},
    primaryClass={cs.RO cs.AI}
}