# A Learning-based Discretionary Lane-Change Decision-Making Model with Driving Style Awareness

arXiv: https://arxiv.org/abs/2010.09533 · HTML: https://mlsanity.com/paper/2010.09533/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Oct 2020.

## Built on by (most cited first, 9 of 9)

- [Risk-anticipatory autonomous driving strategies considering vehicles' weights, based on hierarchical deep reinforcement learning](https://mlsanity.com/paper/2401.08661/) (2023)
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- [SwapTransformer: highway overtaking tactical planner model via imitation learning on OSHA dataset](https://mlsanity.com/paper/2401.01425/) (2024)
- [A Survey on Data-Driven Modeling of Human Drivers' Lane-Changing Decisions](https://mlsanity.com/paper/2505.06680/) (2025)
- [Can Users Specify Driving Speed? Bench2Drive-Speed: Benchmark and Baselines for Desired-Speed Conditioned Autonomous Driving](https://mlsanity.com/paper/2603.25672/) (2026)
- [Mix Q-learning for Lane Changing: A Collaborative Decision-Making Method in Multi-Agent Deep Reinforcement Learning](https://mlsanity.com/paper/2406.09755/) (2024)
- [S4TP: Social-Suitable and Safety-Sensitive Trajectory Planning for Autonomous Vehicles](https://mlsanity.com/paper/2404.11946/) (2024)
- [How vehicles change lanes after encountering crashes: Empirical analysis and modeling](https://mlsanity.com/paper/2601.08125/) (2026)
- [STAGE: STyle-controllable Action GEneration for personalized autonomous driving](https://mlsanity.com/paper/2607.29517/) (2026)

## Abstract (arXiv)

Discretionary lane change (DLC) is a basic but complex maneuver in driving, which aims at reaching a faster speed or better driving conditions, e.g., further line of sight or better ride quality. Although many DLC decision-making models have been studied in traffic engineering and autonomous driving, the impact of human factors, which is an integral part of current and future traffic flow, is largely ignored in the existing literature. In autonomous driving, the ignorance of human factors of surrounding vehicles will lead to poor interaction between the ego vehicle and the surrounding vehicles, thus, a high risk of accidents. The human factors are also a crucial part to simulate a human-like traffic flow in the traffic engineering area. In this paper, we integrate the human factors that are represented by driving styles to design a new DLC decision-making model. Specifically, our proposed model takes not only the contextual traffic information but also the driving styles of surrounding vehicles into consideration and makes lane-change/keep decisions. Moreover, the model can imitate human drivers' decision-making maneuvers to the greatest extent by learning the driving style of the ego vehicle. Our evaluation results show that the proposed model almost follows the human decision-making maneuvers, which can achieve 98.66% prediction accuracy with respect to human drivers' decisions against the ground truth. Besides, the lane-change impact analysis results demonstrate that our model even performs better than human drivers in terms of improving the safety and speed of traffic.