Lane Departure Prediction Based on Closed-Loop Vehicle Dynamics

TL;DR

This paper introduces a physics-based lane departure prediction algorithm using closed-loop vehicle dynamics and Kalman filtering, demonstrating accurate on-road trajectory prediction for automated lane keeping safety.

Contribution

It presents a novel closed-loop vehicle dynamics model combined with Kalman filtering for probabilistic lane departure prediction, enhancing safety in automated driving systems.

Findings

Accurately predicts vehicle future trajectories in simulation.

Effective on-road performance at speeds of 15-50 km/h.

Capable of probabilistic assessment of lane departure risk.

Abstract

An automated driving system should have the ability to supervise its own performance and to request human driver to take over when necessary. In the lane keeping scenario, the prediction of vehicle future trajectory is the key to realize safe and trustworthy driving automation. Previous studies on vehicle trajectory prediction mainly fall into two categories, i.e. physics-based and manoeuvre-based methods. Using a physics-based methodology, this paper proposes a lane departure prediction algorithm based on closed-loop vehicle dynamics model. We use extended Kalman filter to estimate the current vehicle states based on sensing module outputs. Then a Kalman Predictor with actual lane keeping control law is used to predict steering actions and vehicle states in the future. A lane departure assessment module evaluates the probabilistic distribution of vehicle corner positions and decides whether to initiate a human takeover request. The prediction algorithm is capable to describe the stochastic characteristics of future vehicle pose, which is preliminarily proved in simulated tests. Finally, the on-road tests at speeds of 15 to 50 km/h further show that the pro-posed method can accurately predict vehicle future trajectory. It may work as a promising solution to lane departure risk assessment for automated lane keeping functions.