<ul><li>Researchers propose the use of latent space generative world models to address the covariate shift problem in autonomous driving.</li><li>The driving policy can effectively mitigate covariate shift without requiring an excessive amount of training data by leveraging a world model during training.</li><li>The policy learns how to recover from errors by aligning with states observed in human demonstrations during end-to-end training.</li><li>Qualitative and quantitative results demonstrate significant improvements upon prior state of the art in closed-loop testing in the CARLA simulator.</li></ul>

Mitigating Covariate Shift in Imitation Learning for Autonomous Vehicles Using Latent Space Generative World Models

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