mlsanityData to 14 Oct 2026

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

arXiv 2408.03950 ↗First posted 22 Jul 2024cs.RO cs.AIcited by 0

On the map: Deep reinforcement learning methods → RL for autonomous driving and traffic

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.

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.

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9 of 28 references.

References · 9

  1. 2019Safe, Efficient, and Comfortable Velocity Control based on Reinforcement Learning for Autonomous Drivingcited by 59
  2. 2019Human-Like Autonomous Car-Following Model with Deep Reinforcement Learningcited by 48
  3. 2023FollowNet: A Comprehensive Benchmark for Car-Following Behavior Modelingcited by 21
  4. 2022TransFollower: Long-Sequence Car-Following Trajectory Prediction through Transformercited by 9
  5. 2024GenFollower: Enhancing Car-Following Prediction with Large Language Modelscited by 8
  6. 2023EnsembleFollower: A Hybrid Car-Following Framework Based On Reinforcement Learning and Hierarchical Planningcited by 6
  7. 2024MetaFollower: Adaptable Personalized Autonomous Car Followingcited by 4
  8. 2024EditFollower: Tunable Car Following Models for Customizable Adaptive Cruise Control Systemscited by 4
  9. 2023Experimental features of emissions and fuel consumption in a car-following platooncited by 1

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  1. 2024A Scoping Review of Energy-Efficient Driving Behaviors and Applied State-of-the-Art AI Methods
  2. 2022Learning Eco-Driving Strategies at Signalized Intersections
  3. 2022Driver Assistance Eco-driving and Transmission Control with Deep Reinforcement Learning
  4. 2022Eco-driving for Electric Connected Vehicles at Signalized Intersections: A Parameterized Reinforcement Learning approach
  5. 2021A Deep Reinforcement Learning Framework for Eco-driving in Connected and Automated Hybrid Electric Vehicles
  6. 2024Mitigating Metropolitan Carbon Emissions with Dynamic Eco-driving at Scale
  7. 2021Energy-Efficient Autonomous Driving Using Cognitive Driver Behavioral Models and Reinforcement Learning
  8. 2023Car-Following Models: A Multidisciplinary Review