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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, that is, physics-based and manoeuvre-based methods. Using a physics-based methodology, this article 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 15–50 km/h further show that the proposed method can accurately predict vehicle future trajectory. It may work as a promising solution to lane departure risk assessment for automated lane keeping functions.

Keywords

Trajectory prediction Kalman predictor lane keeping assist automated driving risk assessment

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Lane departure prediction based on closed-loop vehicle dynamics

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