Zhong, Hui (The hong kong university of science and techonology (Guangzhou)), CHEN, Xianda (HKUST(GZ)), Tiu, PakHin (The Hong Kong University of Science and Technology (Guangzhou)), Lu, Hongliang (The Hong Kong University of Science and Technology (Guangzhou)), Zhu, Meixin (HKUST)

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

Scheduled for presentation during the Invited Session "AI-Enhanced Safety-Certifiable Autonomous Vehicles" (WeBT3), Wednesday, September 25, 2024, 15:10−15:30, Salon 6

2024 IEEE 27th International Conference on Intelligent Transportation Systems (ITSC), September 24- 27, 2024, Edmonton, Canada

This information is tentative and subject to change. Compiled on October 7, 2024

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.