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

arXiv: https://arxiv.org/abs/2504.20187 · HTML: https://mlsanity.com/paper/2504.20187/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Placed in RL for autonomous driving and traffic, one of 15 directions in Deep reinforcement learning methods.
- Builds on 11 of its 21 references that are in the map.
- First posted on arXiv 28 Apr 2025.

## Placed in

- Deep reinforcement learning methods / [RL for autonomous driving and traffic](https://mlsanity.com/deeprl/rl-for-autonomous-driving-and-traffic/)

## Builds on (most cited first, 11 of 11)

- [CARLA: An Open Urban Driving Simulator](https://mlsanity.com/paper/1711.03938/) (2017)
- [Automated Speed and Lane Change Decision Making using Deep Reinforcement Learning](https://mlsanity.com/paper/1803.10056/) (2018)
- [Lane Change Decision-making through Deep Reinforcement Learning with Rule-based Constraints](https://mlsanity.com/paper/1904.00231/) (2019)
- [Learning to Make Adherence-Aware Advice](https://mlsanity.com/paper/2310.00817/) (2023)
- [A Multi-Agent Deep Reinforcement Learning Coordination Framework for Connected and Automated Vehicles at Merging Roadways](https://mlsanity.com/paper/2109.11672/) (2021)
- [Connected and Automated Vehicles in Mixed-Traffic: Learning Human Driver Behavior for Effective On-Ramp Merging](https://mlsanity.com/paper/2304.00397/) (2023)
- [A Q-learning Approach for Adherence-Aware Recommendations](https://mlsanity.com/paper/2309.06519/) (2023)
- [A Framework for Effective AI Recommendations in Cyber-Physical-Human Systems](https://mlsanity.com/paper/2403.05715/) (2024)
- [Safe Merging in Mixed Traffic with Confidence](https://mlsanity.com/paper/2403.05742/) (2024)
- [Personalized Lane Change Decision Algorithm Using Deep Reinforcement Learning Approach](https://mlsanity.com/paper/2112.13646/) (2021)
- [Stochastic Time-Optimal Trajectory Planning for Connected and Automated Vehicles in Mixed-Traffic Merging Scenarios](https://mlsanity.com/paper/2311.00126/) (2023)

## Built on by (most cited first, 3 of 3)

- [A Communication-Efficient Decentralized Actor-Critic Algorithm](https://mlsanity.com/paper/2510.19199/) (2025)
- [Model-Based Reinforcement Learning Under Confounding](https://mlsanity.com/paper/2512.07528/) (2025)
- [A Decentralized Partially Observable Team Decision Methodology with Delayed Information Sharing](https://mlsanity.com/paper/2609.26783/) (2026)

## 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.