Physics and gaze-based drift trajectory prediction with an online learning framework

This work is the first to address vehicle trajectory prediction under extreme handling conditions relevant to drift-assist ADAS, extending the operational envelope of current trajectory prediction approaches beyond normal/near-linear driving regimes. The proposed framework is based on physics and driver gaze to predict the driver’s desired course during drifting. We introduce kinematics-based models for trajectory prediction to consider the unique vehicle dynamics in drifting including high sideslip and counter steering. Dynamics-based models are also utilized to account for the driver’s desired yaw rate and sideslip angle. Moreover, the driver’s gaze behavior during drifting is analyzed and two gaze-based travel-point and waypoint models are further adopted for trajectory prediction. In order to fuse the predictions from the above models, a t -distribution-based regression is applied to accommodate more outliers in the extreme drifting maneuver. Furthermore, a Gaussian process-based online learning model is deployed using the prediction error of previous timesteps to correct the current prediction according to vehicle and driver states. Driver-in-loop drifting data collected from the driving simulator of Cranfield University is utilized for validation of the effectiveness of the proposed framework.