  Human drivers can seamlessly adapt their driving decisions across
geographical locations with diverse conditions and rules of the road, e.g.,
left vs. right-hand traffic. In contrast, existing models for autonomous
driving have been thus far only deployed within restricted operational domains,
i.e., without accounting for varying driving behaviors across locations or
model scalability. In this work, we propose AnyD, a single geographically-aware
conditional imitation learning (CIL) model that can efficiently learn from
heterogeneous and globally distributed data with dynamic environmental,
traffic, and social characteristics. Our key insight is to introduce a
high-capacity geo-location-based channel attention mechanism that effectively
adapts to local nuances while also flexibly modeling similarities among regions
in a data-driven manner. By optimizing a contrastive imitation objective, our
proposed approach can efficiently scale across inherently imbalanced data
distributions and location-dependent events. We demonstrate the benefits of our
AnyD agent across multiple datasets, cities, and scalable deployment paradigms,
i.e., centralized, semi-supervised, and distributed agent training.
Specifically, AnyD outperforms CIL baselines by over 14% in open-loop
evaluation and 30% in closed-loop testing on CARLA.
