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

arXiv: https://arxiv.org/abs/2408.03950 · HTML: https://mlsanity.com/paper/2408.03950/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in RL for autonomous driving and traffic, one of 15 directions in Deep reinforcement learning methods.
- Builds on 9 of its 28 references that are in the map.
- First posted on arXiv 22 Jul 2024.

## Placed in

- Deep reinforcement learning methods / [RL for autonomous driving and traffic](https://mlsanity.com/deeprl/rl-for-autonomous-driving-and-traffic/)

## Builds on (most cited first, 9 of 9)

- [Safe, Efficient, and Comfortable Velocity Control based on Reinforcement Learning for Autonomous Driving](https://mlsanity.com/paper/1902.00089/) (2019)
- [Human-Like Autonomous Car-Following Model with Deep Reinforcement Learning](https://mlsanity.com/paper/1901.00569/) (2019)
- [FollowNet: A Comprehensive Benchmark for Car-Following Behavior Modeling](https://mlsanity.com/paper/2306.05381/) (2023)
- [TransFollower: Long-Sequence Car-Following Trajectory Prediction through Transformer](https://mlsanity.com/paper/2202.03183/) (2022)
- [GenFollower: Enhancing Car-Following Prediction with Large Language Models](https://mlsanity.com/paper/2407.05611/) (2024)
- [EnsembleFollower: A Hybrid Car-Following Framework Based On Reinforcement Learning and Hierarchical Planning](https://mlsanity.com/paper/2308.16008/) (2023)
- [MetaFollower: Adaptable Personalized Autonomous Car Following](https://mlsanity.com/paper/2406.16978/) (2024)
- [EditFollower: Tunable Car Following Models for Customizable Adaptive Cruise Control Systems](https://mlsanity.com/paper/2407.02516/) (2024)
- [Experimental features of emissions and fuel consumption in a car-following platoon](https://mlsanity.com/paper/2305.00750/) (2023)

## Abstract (arXiv)

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.