EcoFollower: An Environment-Friendly Car Following Model Considering Fuel Consumption

TL;DR

EcoFollower is an RL-based eco-driving model that reduces fuel consumption by over 10%, improves driving behavior realism, and maintains safety metrics, contributing to greener autonomous vehicle technology.

Contribution

This paper introduces EcoFollower, a reinforcement learning-based car-following model that optimizes fuel efficiency while maintaining realistic and safe driving behaviors.

Findings

Fuel consumption reduced by 10.42% compared to real driving.

EcoFollower closely matches ground truth safety and comfort metrics.

The model outperforms traditional IDM in energy efficiency and realism.

Abstract

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.