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论文提出一种面向路网轨迹生成的图约束生成范式 Anticipatory Generative Modeling，并提出模型 Foresight，通过"anticipate–select–update–re-anticipate"循环解决路口转移选择问题，超越扩散模型等基线方法。
The paper introduces the Industrial Kinematic Trajectory Model (IKTM), a coordinate-free autoregressive transformer that generates realistic vehicle trajectories for industrial environments without relying on absolute spatial coordinates.
The paper proposes MR-Traj, a multi-resolution diffusion framework for synthetic trajectory generation in urban systems, which captures complex spatial-temporal dependencies at multiple resolutions and improves performance in fine-grained mobility modeling for downstream tasks.
Push-Wiper is a robotics framework that reformulates viscous stain cleaning as an aggregation problem, using segmented pushing trajectories and a Diffusion Policy to generalize across stains, surfaces, and geometries, achieving up to 130% higher cleaning scores and zero-shot transfer.
SevDiff is a severity-conditioned diffusion model for generating vehicle conflict trajectories with controlled time-to-collision values, achieving high hit-rate on a real-world dataset for ADAS evaluation.
TerminalTraj is a scalable pipeline that generates high-quality terminal agent trajectories using Dockerized environments, achieving up to 20% improvement on terminal task benchmarks with models like TerminalTraj-32B.
RS-Diffuser 提出了一种风险敏感的离线扩散规划框架，结合扩散轨迹生成和分布值批评器，通过尾部感知目标在推理时灵活调整风险偏好，在安全关键任务中提升了回报和鲁棒性。
TrajGenAgent proposes a hierarchical LLM agent framework that decouples macro-level activity planning from micro-level spatiotemporal instantiation for realistic human mobility trajectory generation without fine-tuning. It also introduces an anomaly-detection-based evaluation for behavioral fidelity.
Introduces a generative framework that uses LLM agents to inject behavioral anomalies into simulated trajectories and applies kinematic and map constraints to produce realistic anomalous mobility data with ground truth.
This paper systematically investigates privacy risks in generative models for trajectory data, identifying a gap in empirical privacy evaluation and demonstrating Membership Inference Attacks against representative models.
RAD-2 presents a unified generator-discriminator framework for autonomous driving that combines diffusion-based trajectory generation with RL-optimized reranking, achieving 56% collision rate reduction compared to diffusion-based planners. The approach introduces techniques like Temporally Consistent Group Relative Policy Optimization and BEV-Warp simulation environment for efficient large-scale training.