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A novel PIDL model is proposed called DoubleGAN, which encodes the stochastic physics into the PIDL structure and captures the uncertain from vehicle trajectory data and introduces a stochastic CF model to inform the generator with prior physics information.
This paper can be a pointer to help researchers and practitioners identify challenges and opportunities for the development of DTs; a bridge to initiate conversations across disciplines; and a road map to exploiting potentials of DTs for diverse urban transportation applications.
This survey delivers a comprehensive and critical synthesis of the emerging role of GenAI across the autonomous driving stack, delving into the frontier applications of GenAI in image, LiDAR, trajectory, occupancy, and video generation, as well as LLM-guided reasoning and decision-making.
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