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Generating physically feasible vehicle trajectories via knowledge-infused variational autoencoder

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Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering

Published online on

Abstract

Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering, Ahead of Print.
Autonomous driving systems require extensive high-quality data for training and validation to ensure safety and reliability. However, acquiring such data is often inefficient, costly, and sometimes infeasible, particularly in complex interactive scenarios,...