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

Weihao Sun, Heeseung Bang, Andreas A. Malikopoulos
4/28/2025
cs.LGcs.AIeess.SY

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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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Cite this paper

@article{sun2025recommendation,
  title  = {AI Recommendation Systems for Lane-Changing Using Adherence-Aware Reinforcement Learning},
  author = {Weihao Sun and Heeseung Bang and Andreas A. Malikopoulos},
  year   = {2025},
  eprint = {2504.20187},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2504.20187v1}
}

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