Learning Coordinated Vehicle Maneuver Motion Primitives from Human Demonstration

Abstract

High-fidelity computational human models provide a safe and cost-efficient method for studying driver experience in vehicle maneuvers and for validation of vehicle design. Compared to passive human models, active human models capable of reproducing the decision-making, as well as vehicle maneuver motion planning and control, will be able to support realistic simulation of human-vehicle interaction. In this paper, we propose an integrated human-vehicle interaction simulation framework which learns vehicle maneuver motion primitives from human drivers, and uses them to compose natural and contextual driving motions. Specifically, we recruited six experienced drivers and recorded their vehicle maneuver motions on a fixed-base driving simulation testbed. We further segmented and classified the collected data based on their similarity in joint coordination. Using a combination of imitation learning methods, we extracted the regularity and variability of vehicle maneuver motions across subjects, and learned the dynamic motion primitives to be used for motion reproduction in simulation. We present an implementation of the framework on lower-extremity joint coordination in pedal activation for longitudinal vehicle control. Our research efforts lead to a motion primitive library which enables planning natural driver motions, and will be integrated with the driving decision-making, motion control, and vehicle dynamics in the proposed framework for simulating human-vehicle interaction.