Accurate short-horizon vehicle trajectory prediction is critical for autonomous driving and vehicle-to-everything (V2X) communication. Most existing deep learning-based prediction methods rely on fixed-length historical trajectories and multi-agent context, which may be unavailable when a vehicle is newly observed. This paper studies single-frame short-horizon trajectory prediction at signalized intersections, where only one enriched observation frame is used for future motion-state forecasting. We propose a Neural Ordinary Differential Equation (Neural ODE)-based framework that formulates single-frame prediction as continuous-time motion-state forecasting. The proposed model encodes instantaneous kinematic variables, road-context information, and training-set-derived spatial priors into a latent representation, evolves the latent state in continuous time, and decodes future state variables. The predicted motion states are converted into future vehicle coordinates through kinematic integration. Experiments on the CitySIM-Intersection A dataset, evaluated by ADE and DE, show that the proposed method achieves competitive short-horizon prediction accuracy under the single-frame setting. The experiments include baseline comparisons with classical motion models and single-frame neural baselines, input ablation, coordinate and spatial-cell analyses, runtime evaluation, and supplementary studies on multi-frame sequence baselines, longer-horizon rollout, robustness, maneuver-specific performance, and statistical significance. The results clarify the applicability and limitations of single-frame short-horizon prediction on both straight and curved driving subsets.
Yijun Tang, Wenhao Huang, Yang Pu et al.· Discover Computing· 0 citations
This paper investigates the rate-splitting multiple access (RSMA)-enabled integrated sensing, communication, and power transfer (ISCPT) network assisted by a simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS). Particularly, considering the inherent heterogeneous quality-of-service (QoS) requirements, communication users (CUs) are granted priority in information reception, and the legitimate sensing targets (STs) are also regarded as potential eavesdroppers to intercept the information of CUs. In order to meet the service demands of heterogeneous users of such a system while ensuring the physical layer security of CUs against wiretapping, we formulate a secrecy energy efficiency (SEE) maximization problem via jointly optimizing the transmit beamforming matrix, sensing matrix, STAR-RIS reflection/transmission coefficient matrix, power splitting (PS) ratio vector, and common rate allocation vector. Due to the non-convexity of the formulated problem and the challenges caused by imperfect channel state information (CSI) and dynamic wireless environment, an agentic-AI enabled optimization approach (ISCPT-SEE-AA) is proposed, which works in a closed-loop perception-decision–reward adaptation manner. Specifically, a Transformer-based channel refinement module is developed to mitigate the uncertainty induced by imperfect CSI. Meanwhile, a mixture-of-experts group relative policy optimization (MoE-GRPO) scheme is adopted to achieve adaptive decision-making under time-varying network conditions. Furthermore, a large language model (LLM)-aided reward configuration module with retrieval-augmented generation (RAG) is integrated to automatically configure and update reward parameters, thereby reducing manual tuning overhead and enhancing training stability. Extensive simulation results verify that the proposed ISCPT-SEE-AA outperforms conventional learning-based schemes significantly in terms of SEE and exhibits stronger robustness against CSI imperfections. Notably, it achieves performance close to that of the convex optimization-based benchmark with substantially lower online computational complexity.
Wanle Zhang, Ke Xiong, Rui Dong et al.· IEEE Transactions on Cogniti...· 0 citations
