AI Recommendation Systems for Lane-Changing Using Adherence-Aware Reinforcement Learning
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Abstract (arXiv)
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.
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 11 of its 21 references that are in the map. First posted on arXiv 28 Apr 2025.
Builds on
11 of 21 references.
References · 11
- 2017CARLA: An Open Urban Driving Simulatorcited by 2,431
- 2018Automated Speed and Lane Change Decision Making using Deep Reinforcement Learningcited by 36
- 2019Lane Change Decision-making through Deep Reinforcement Learning with Rule-based Constraintscited by 24
- 2023Learning to Make Adherence-Aware Advicecited by 13
- 2023Connected and Automated Vehicles in Mixed-Traffic: Learning Human Driver Behavior for Effective On-Ramp Mergingcited by 7
- 2021A Multi-Agent Deep Reinforcement Learning Coordination Framework for Connected and Automated Vehicles at Merging Roadwayscited by 6
- 2023A Q-learning Approach for Adherence-Aware Recommendationscited by 4
- 2024A Framework for Effective AI Recommendations in Cyber-Physical-Human Systemscited by 4
- 2024Safe Merging in Mixed Traffic with Confidencecited by 4
- 2021Personalized Lane Change Decision Algorithm Using Deep Reinforcement Learning Approachcited by 2
- 2023Stochastic Time-Optimal Trajectory Planning for Connected and Automated Vehicles in Mixed-Traffic Merging Scenarioscited by 1
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Citing papers per year: 2025: 2; 2026: 1.
3 papers.