AI Recommendation Systems for Lane-Changing Using Adherence-Aware Reinforcement Learning
Machine Learning
2025-04-30 v1 Artificial Intelligence
Systems and Control
Systems and Control
Abstract
In this paper, we present an adherence-aware reinforcement learning (RL) approach aimed at seeking optimal lane-changing recommendations within a semi-autonomous driving environment to enhance a single vehicle's travel efficiency. The problem is framed within a Markov decision process setting and is addressed through an adherence-aware deep Q network, which takes into account the partial compliance of human drivers with the recommended actions. This approach is evaluated within CARLA's driving environment under realistic scenarios.
Cite
@article{arxiv.2504.20187,
title = {AI Recommendation Systems for Lane-Changing Using Adherence-Aware Reinforcement Learning},
author = {Weihao Sun and Heeseung Bang and Andreas A. Malikopoulos},
journal= {arXiv preprint arXiv:2504.20187},
year = {2025}
}
Comments
6 pages, 5 figures, conference