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As autonomous driving technology matures, end-to-end methodologies haveemerged as a leading strategy, promising seamless integration from perceptionto control via deep learning. However, existing systems grapple with challengessuch as unexpected open set environments and the complexity of black-boxmodels. At the same time, the evolution of deep learning introduces larger,multimodal foundational models, offering multi-modal visual and textualunderstanding. In this paper, we harness these multimodal foundation models toenhance the robustness and adaptability of autonomous driving systems, enablingout-of-distribution, end-to-end, multimodal, and more explainable autonomy.Specifically, we present an approach to apply end-to-end open-set (anyenvironment/scene) autonomous driving that is capable of providing drivingdecisions from representations queryable by image and text. To do so, weintroduce a method to extract nuanced spatial (pixel/patch-aligned) featuresfrom transformers to enable the encapsulation of both spatial and semanticfeatures. Our approach (i) demonstrates unparalleled results in diverse testswhile achieving significantly greater robustness in out-of-distributionsituations, and (ii) allows the incorporation of latent space simulation (viatext) for improved training (data augmentation via text) and policy debugging.We encourage the reader to check our explainer video athttps://www.youtube.com/watch?v=4n-DJf8vXxo&feature=youtu.be and to view thecode and demos on our project webpage at https://drive-anywhere.github.io/.

Drive Anywhere: Generalizable End-to-end Autonomous Driving with Multi-modal Foundation Models