Generating physically feasible vehicle trajectories via knowledge-infused variational autoencoder
Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering
Published online on February 11, 2026
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,...
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,...