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

Hui Zhong, Xianda Chen, PakHin Tiu, Hongliang Lu, Meixin Zhu

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.

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

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.
Paper Structure (15 sections, 10 equations, 6 figures, 1 table)

This paper contains 15 sections, 10 equations, 6 figures, 1 table.

Table of Contents

  1. INTRODUCTION
  2. RELATED WORK
  3. DATA PREPARATION
  4. METHODOLOGY
  5. Intelligent Driver Model (IDM)
  6. DDPG Algorithm
  7. Fuel Consumption
  8. Comfort
  9. Safety and Efficiency
  10. States and Actions
  11. Reward Function
  12. EXPERIMENT
  13. EcoFollower model
  14. Comparison between EcoFollower and IDM model
  15. CONCLUSIONS

Figures (6)

  • Figure 1: A random car-following event and descriptive analysis
  • Figure 2: descriptive analysis of four indicators
  • Figure 3: The training log of reward and collision
  • Figure 4: The test results of safety, comfort, efficiency and fuel consumption in EcoFollower model
  • Figure 5: The test results for safety, comfort, efficiency and fuel consumption in IDM model
  • ...and 1 more figures