We propose the use of latent space generative world models to address the covariate shift problem in autonomous driving. A world model is a neural network capable of predicting an agent's next state given past states and actions. By leveraging a world model during training, the driving policy effectively mitigates covariate shift without requiring an excessive amount of training data. During end-to-end training, our policy learns how to recover from errors by aligning with states observed in human demonstrations, so that at runtime it can recover from perturbations outside the training distribution. Additionally, we introduce a novel transformer-based perception encoder that employs multi-view cross-attention and a learned scene query. We present qualitative and quantitative results, demonstrating significant improvements upon prior state of the art in closed-loop testing in the CARLA simulator, as well as showing the ability to handle perturbations in both CARLA and NVIDIA's DRIVE Sim.
View on arXiv@article{popov2025_2409.16663,
title={ Mitigating Covariate Shift in Imitation Learning for Autonomous Vehicles Using Latent Space Generative World Models },
author={ Alexander Popov and Alperen Degirmenci and David Wehr and Shashank Hegde and Ryan Oldja and Alexey Kamenev and Bertrand Douillard and David Nistér and Urs Muller and Ruchi Bhargava and Stan Birchfield and Nikolai Smolyanskiy },
journal={arXiv preprint arXiv:2409.16663},
year={ 2025 }
}