<ul><li>Simulation is crucial for safety evaluation in autonomous driving, but generating realistic and controllable traffic scenarios is challenging.</li><li>A new framework called Causal Compositional Diffusion Model (CCDiff) is introduced to address the challenges.</li><li>CCDiff maximizes controllability while adhering to realism by injecting causal structures into the diffusion process.</li><li>CCDiff outperforms state-of-the-art approaches in generating realistic and user-preferred trajectories.</li></ul>

Causal Composition Diffusion Model for Closed-loop Traffic Generation

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