# Evaluation of Lane Departure Correction Systems Using a Stochastic Driver Model

## TL;DR

This paper introduces a low-cost, data-driven framework for evaluating lane departure correction systems by modeling naturalistic driving events with a stochastic driver model and assessing system performance through simulation.

## Contribution

It presents a novel approach using a bounded Gaussian mixture model to simulate lane departure behaviors for system evaluation, reducing data complexity and computational cost.

## Key findings

- The framework accurately reproduces naturalistic lane departure events.
- Simulation results demonstrate effective evaluation of lane departure correction systems.
- The method offers a cost-efficient alternative to real-world testing.

## Abstract

Evaluating the effectiveness and benefits of driver assistance systems is crucial for improving the system performance. In this paper, we propose a novel framework for testing and evaluating lane departure correction systems at a low cost by using lane departure events reproduced from naturalistic driving data. First, 529,096 lane departure events were extracted from the Safety Pilot Model Deployment (SPMD) database collected by the University of Michigan Transportation Research Institute. Second, a stochastic lane departure model consisting of eight random key variables was developed to reduce the dimension of the data description and improve the computational efficiency. As such, we used a bounded Gaussian mixture model (BGM) model to describe drivers' stochastic lane departure behaviors. Then, a lane departure correction system with an aim point controller was designed, and a batch of lane departure events were reproduced from the learned stochastic driver model. Finally, we assessed the developed evaluation approach by comparing lateral departure areas of vehicles between with and without correction controller. The simulation results show that the proposed method can effectively evaluate lane departure correction systems.