  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.
