DINO Pre-training for Vision-based End-to-end Autonomous Driving
<|reference_start|>DINO Pre-training for Vision-based End-to-end Autonomous Driving: In this article, we focus on the pre-training of visual autonomous driving agents in the context of imitation learning. Current methods often rely on a classification-based pre-training, which we hypothesise to be holding back from extending capabilities of implicit image understanding. We propose pre-training the visual encoder of a driving agent using the self-distillation with no labels (DINO) method, which relies on a self-supervised learning paradigm.% and is trained on an unrelated task. Our experiments in CARLA environment in accordance with the Leaderboard benchmark reveal that the proposed pre-training is more efficient than classification-based pre-training, and is on par with the recently proposed pre-training based on visual place recognition (VPRPre).<|reference_end|>
@article{juneja2024dino,
    title={DINO Pre-training for Vision-based End-to-end Autonomous Driving},
    author={Shubham Juneja, Povilas Daniuv{s}is, Virginijus Marcinkeviv{c}ius},
    journal={arXiv preprint arXiv:2407.10803},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.10803},
    primaryClass={cs.CV cs.LG cs.RO}
}