# Evaluation of A Semi-Autonomous Lane Departure Correction System Using Naturalistic Driving Data

## TL;DR

This paper introduces an efficient evaluation method for semi-autonomous lane departure correction systems using a stochastic model based on naturalistic driving data, enabling realistic simulation and comparison of system performance.

## Contribution

It proposes a bounded Gaussian mixture model to simulate driver lane departure behavior, reducing computation costs and improving evaluation accuracy for lane departure correction systems.

## Key findings

- The method effectively evaluates lane departure correction systems.
- Simulation results demonstrate the model's ability to replicate real driver behavior.
- The approach offers a computationally efficient evaluation framework.

## Abstract

Evaluating the effectiveness and benefits of driver assistance systems is essential for improving the system performance. In this paper, we propose an efficient evaluation method for a semi-autonomous lane departure correction system. To achieve this, we apply a bounded Gaussian mixture model to describe drivers' stochastic lane departure behavior learned from naturalistic driving data, which can regenerate departure behaviors to evaluate the lane departure correction system. In the stochastic lane departure model, we conduct a dimension reduction to reduce the computation cost. Finally, to show the advantages of our proposed evaluation approach, we compare steering systems with and without lane departure assistance based on the stochastic lane departure model. The simulation results show that the proposed method can effectively evaluate the lane departure correction system.