# A Learning-Based Approach for Lane Departure Warning Systems with a Personalized Driver Model

arXiv: https://arxiv.org/abs/1702.01228 · HTML: https://mlsanity.com/paper/1702.01228/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Feb 2017.

## Built on by (most cited first, 7 of 7)

- [Driver Digital Twin for Online Prediction of Personalized Lane Change Behavior](https://mlsanity.com/paper/2211.01294/) (2022)
- [Interaction-Aware Personalized Vehicle Trajectory Prediction Using Temporal Graph Neural Networks](https://mlsanity.com/paper/2308.07439/) (2023)
- [Accelerating the Evolution of Personalized Automated Lane Change through Lesson Learning](https://mlsanity.com/paper/2405.07543/) (2024)
- [An Open Case-based Reasoning Framework for Personalized On-board Driving Assistance in Risk Scenarios](https://mlsanity.com/paper/2211.12798/) (2022)
- [Reducing Warning Errors in Driver Support with Personalized Risk Maps](https://mlsanity.com/paper/2410.02148/) (2024)
- [Driving Style Recognition Like an Expert Using Semantic Privileged Information from Large Language Models](https://mlsanity.com/paper/2508.13881/) (2025)
- [Open-Ended Instruction Realization with LLM-Enabled Multi-Planner Scheduling in Autonomous Vehicles](https://mlsanity.com/paper/2604.08031/) (2026)

## Abstract (arXiv)

Misunderstanding of driver correction behaviors (DCB) is the primary reason for false warnings of lane-departure-prediction systems. We propose a learning-based approach to predicting unintended lane-departure behaviors (LDB) and the chance for drivers to bring the vehicle back to the lane. First, in this approach, a personalized driver model for lane-departure and lane-keeping behavior is established by combining the Gaussian mixture model and the hidden Markov model. Second, based on this model, we develop an online model-based prediction algorithm to predict the forthcoming vehicle trajectory and judge whether the driver will demonstrate an LDB or a DCB. We also develop a warning strategy based on the model-based prediction algorithm that allows the lane-departure warning system to be acceptable for drivers according to the predicted trajectory. In addition, the naturalistic driving data of 10 drivers is collected through the University of Michigan Safety Pilot Model Deployment program to train the personalized driver model and validate this approach. We compare the proposed method with a basic time-to-lane-crossing (TLC) method and a TLC-directional sequence of piecewise lateral slopes (TLC-DSPLS) method. The results show that the proposed approach can reduce the false-warning rate to 3.07%.