Jul 2026· Signal Processing and Communications Applications Conference· pp. 1-4· 0 citations· 21 references
Abstract
Safe decision making in autonomous driving relies on accurately predicting the future positions of traffic actors. Although existing trajectory prediction methods mainly focus on map information and social interactions, actors’ intrinsic motion dynamics are often overlooked. In this study, we propose a dynamic trajectory prediction architecture based on velocity profiles extracted from observed past trajectories. The proposed architecture decomposes driving scenes into low- and high-velocity models and routes each sample to the corresponding expert model. Experiments on a large-scale real-world autonomous driving dataset show that velocity-based decomposition reduces prediction error compared with conventional single-model baselines.
A Stochastic Gating Decoder for multimodal latent variable sampling, adaptively fusing kinematics and data-driven paths to capture driver intention uncertainty while maintaining kinematic consistency is introduced.
A comprehensive Explainable AI (XAI) evaluation framework is introduced, including temporal sensitivity analysis, interaction-aware perturbation studies, spatial influence analysis, and gradient-based feature attribution methods that provide insights into how the model captures temporal motion dependencies, neighboring...
R. Megalingam, Naveen Prasaad Selvarajan, Pritty Vijay· Italian National Conference...· 0 citations
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.
Yijun Tang, Wenhao Huang, Yang Pu et al.· Discover Computing· 0 citations
The HGCA-Net framework employs a dynamic spatio-temporal graph attention network for interaction modeling and an intention recognition branch for maneuver preferences to address extended-horizon error accumulation during nonlinear highway lane changes.
Z. Wang, Ge Tong, Tao Cheng et al.· International Conference on...· 0 citations
A MeanFlowbased trajectory planning framework that introduces trajectory-cluster-guided Gaussian noise modeling into the generation process and can generate feasible multimodal trajectory candidates and select stable planning outputs for CAV trajectory planning is proposed.
Dongting Ge· International Conference on...· 0 citations
These findings demonstrate that H3-indexed context, structured at multiple geographic resolutions and integrated through a selective mechanism, serves as transferable spatial context for vessel trajectory prediction.
