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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.