This paper investigates a movable antenna (MA)-enabled integrated sensing and communication (ISAC) system under the influence of antenna crosstalk. First, it generalizes the antenna crosstalk model from the conventional fixed-position antenna (FPA) system to MA. Then, a Cramer-Rao bound (CRB) minimization problem driven by joint beamforming and antenna position design is presented. To address the problem, a deep reinforcement learning (DRL) approach, specifically the twin delayed deep deterministic policy gradient (TD3) algorithm is employed to train a flexible beamforming agent. Numerical results show that the proposed crosstalk-resilient (CR) beamforming outperforms other benchmark schemes under antenna crosstalk.

Accurate bus Origin-Destination (OD) identification based on public transit data is essential for route planning and service optimization. However, current research lacks evaluation metrics specifically designed for the bus OD identification task, often relying on conventional indicators that fail to fully capture the plausibility of identification results. This study proposes an integrated framework encompassing label generation, modeling, and validation. Specifically, we developed a two-layer model that employs a Random Forest for feature selection and a CatBoost classifier for alighting stop identification. To enable validation without ground-truth labels, a novel Trend Consistency Index (TCI) was introduced. A case study using public transit data from Chengdu demonstrates that the proposed method outperforms mainstream algorithms such as XGBoost, achieving the highest scores across all key metrics. Multi-dimensional validation further confirmed its effectiveness, showing high fidelity in reproducing temporal travel patterns, accurately identifying major destination clusters through spatial heatmaps, and yielding an excellent fit for trip distribution curves (R² up to 0.8382). This research provides a robust and practical solution for the bus OD identification problem, offering a reliable quantitative foundation to advance smart public transit management.

This paper investigates a movable antenna (MA) enabled integrated sensing and communication (ISAC) system under the influence of antenna crosstalk. First, it generalizes the antenna crosstalk model from the conventional fixed-position antenna (FPA) system to the MA scenario. Then, a Cramer-Rao bound (CRB) minimization problem driven by joint beamforming and antenna position design is presented. Specifically, to address this highly non-convex flexible beamforming problem, we deploy a deep reinforcement learning (DRL) approach to train a flexible beamforming agent. To ensure stability during training, a Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm is adopted to balance exploration with reward maximization for efficient and reliable learning. Numerical results demonstrate that the proposed crosstalk-resilient (CR) algorithm enhances the overall ISAC performance compared to other benchmark schemes.

Proactive lane-change risk assessment requires estimating whether an intended maneuver will lead to unsafe interactions before the maneuver is completed. This is challenging in naturalistic driving data because actual crashes are extremely rare, and binary collision labels provide little discriminative information for learning risk. We propose IntentDiff, an intent-conditioned diffusion framework for proactive lane-change risk assessment. The framework uses predicted future trajectories as the basis for risk estimation. A vectorized scene context learning module combines a VectorNet backbone with a Vector Quantized Variational Autoencoder (VQ-VAE) to map agent–map interactions into discrete intent codes. These codes organize complex traffic situations into interpretable intent prototypes and provide semantic guidance for trajectory generation. Conditioned on the learned intent code, a diffusion model generates kinematically consistent multimodal trajectories of the target vehicle. On the forecast trajectories, Monte Carlo rear-end risk is evaluated against the four bounding vehicles and fused into a Lane-Change Risk Index (LCRI). On the highD dataset, the framework attains an average displacement error of 0.42 m over a 5-s horizon. The forecast-based LCRI agrees closely with the index computed from realized future trajectories, indicating that most high-risk lane changes can be identified before the maneuver is completed. Grouping LCRI by intent code further reveals systematic variation in risk across lane-change maneuvers, suggesting that the learned codebook captures risk-relevant interaction patterns in addition to maneuver semantics.