# A Survey on Data-Driven Modeling of Human Drivers' Lane-Changing Decisions

arXiv: https://arxiv.org/abs/2505.06680 · HTML: https://mlsanity.com/paper/2505.06680/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 73 references that are in the map.
- First posted on arXiv 10 May 2025.

## Builds on (most cited first, 4 of 4)

- [The highD Dataset: A Drone Dataset of Naturalistic Vehicle Trajectories on German Highways for Validation of Highly Automated Driving Systems](https://mlsanity.com/paper/1810.05642/) (2018)
- [A Learning-based Discretionary Lane-Change Decision-Making Model with Driving Style Awareness](https://mlsanity.com/paper/2010.09533/) (2020)
- [Vehicle Lane Change Prediction based on Knowledge Graph Embeddings and Bayesian Inference](https://mlsanity.com/paper/2312.06336/) (2023)
- [Safe Decision-making for Lane-change of Autonomous Vehicles via Human Demonstration-aided Reinforcement Learning](https://mlsanity.com/paper/2207.00448/) (2022)

## Built on by (most cited first, 2 of 2)

- [When Context Is Not Enough: Modeling Unexplained Variability in Car-Following Behavior](https://mlsanity.com/paper/2507.07012/) (2025)
- [CauTraj: A Causal-Knowledge-Guided Framework for Lane-Changing Trajectory Planning of Autonomous Vehicles](https://mlsanity.com/paper/2512.18703/) (2025)

## Abstract (arXiv)

Lane-changing (LC) behavior, a critical yet complex driving maneuver, significantly influences driving safety and traffic dynamics. Traditional analytical LC decision (LCD) models, while effective in specific environments, often oversimplify behavioral heterogeneity and complex interactions, limiting their capacity to capture real LCD. Data-driven approaches address these gaps by leveraging rich empirical data and machine learning to decode latent decision-making patterns, enabling adaptive LCD modeling in dynamic environments. In light of the rapid development of artificial intelligence and the demand for data-driven models oriented towards connected vehicles and autonomous vehicles, this paper presents a comprehensive survey of data-driven LCD models, with a particular focus on human drivers LC decision-making. It systematically reviews the modeling framework, covering data sources and preprocessing, model inputs and outputs, objectives, structures, and validation methods. This survey further discusses the opportunities and challenges faced by data-driven LCD models, including driving safety, uncertainty, as well as the integration and improvement of technical frameworks.