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arXiv · 2504.20187

AI Recommendation Systems for Lane-Changing Using Adherence-Aware Reinforcement Learning

Weihao Sun·Heeseung Bang·Andreas A. Malikopoulos
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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.

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BibTeXRIS

Weihao Sun, Heeseung Bang, Andreas A. Malikopoulos. 2025-04-28. AI Recommendation Systems for Lane-Changing Using Adherence-Aware Reinforcement Learning. https://arxiv.org/abs/2504.20187

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