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.

提出了一个地理感知的条件模仿学习模型 AnyD，能够有效地从多样性和全球分布的数据中学习，适应局部细微差异以及在数据驱动的方式下建模区域间的相似性，并在多个数据集、城市和可扩展的部署范式下取得显著性能改进。