SSP-DMGTimeNet, a physics-constrained learning framework for spatiotemporal trajectory prediction of vehicle platoons, combines multi-scale temporal representations with cross-vehicle interaction features to capture complex and time-varying platoon dynamics.
A novel hybrid deep learning framework suitable for cloud-based control platforms, providing a foundational algorithmic solution for vehicle-infrastructure cooperative perception and decision-making and suggests potential for integration into intelligent transportation cloud control platforms.
A Neural Ordinary Differential Equation (Neural ODE)-based framework is proposed that formulates single-frame prediction as continuous-time motion-state forecasting and achieves competitive short-horizon prediction accuracy under the single-frame setting.
Trajectory prediction links environmental perception, behavior understanding, and decision-making and planning in autonomous driving. Its value depends not only on geometric error on standard test sets but also on model stability in open traffic environments, probabilistic reliability, and contributions to planning saf...
Training sequence models such as transformers is now standard for autonomous vehicle trajectory prediction, yet assembling high-quality centralized datasets remains challenging because real-world trajectories are fragmented across regions and vehicles. Federated Learning (FL) offers a natural alternative, but faces two...
Precise trajectory prediction in high-density terminal maneuvering areas is a fundamental prerequisite for the realization of next-generation trajectory-based operations. However, the practical deployment of deep learning models in this domain is often hindered by the technical challenges of effectively integrating het...
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