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

• URL: http://arxiv.org/abs/2504.20187v1
• Date: Mon, 28 Apr 2025 18:38:39 GMT
• Title: AI Recommendation Systems for Lane-Changing Using Adherence-Aware Reinforcement Learning
• Authors: Weihao Sun, Heeseung Bang, Andreas A. Malikopoulos,
• Abstract summary: We present an adherence-aware reinforcement learning (RL) approach aimed at seeking optimal lane-changing recommendations within a semi-autonomous driving environment.<n>The problem is framed within a Markov decision process setting and is addressed through an adherence-aware deep Q network.
• Score: 4.271235935891555
• License: http://creativecommons.org/licenses/by-sa/4.0/
• 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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