Learning to Drive Anywhere

Ruizhao Zhu, Peng Huang, Eshed Ohn-Bar, Venkatesh Saligrama
Proceedings of The 7th Conference on Robot Learning, PMLR 229:3631-3653, 2023.

Abstract

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 GeCo, 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 the inherently imbalanced data distributions and location-dependent events. We demonstrate the benefits of our GeCo agent across multiple datasets, cities, and scalable deployment paradigms, i.e., centralized, semi-supervised, and distributed agent training. Specifically, GeCo outperforms CIL baselines by over $14%$ in open-loop evaluation and $30%$ in closed-loop testing on CARLA.

Cite this Paper

BibTeX
@InProceedings{pmlr-v229-zhu23c, title = {Learning to Drive Anywhere}, author = {Zhu, Ruizhao and Huang, Peng and Ohn-Bar, Eshed and Saligrama, Venkatesh}, booktitle = {Proceedings of The 7th Conference on Robot Learning}, pages = {3631--3653}, year = {2023}, editor = {Tan, Jie and Toussaint, Marc and Darvish, Kourosh}, volume = {229}, series = {Proceedings of Machine Learning Research}, month = {06--09 Nov}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v229/zhu23c/zhu23c.pdf}, url = {https://proceedings.mlr.press/v229/zhu23c.html}, abstract = {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 GeCo, 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 the inherently imbalanced data distributions and location-dependent events. We demonstrate the benefits of our GeCo agent across multiple datasets, cities, and scalable deployment paradigms, i.e., centralized, semi-supervised, and distributed agent training. Specifically, GeCo outperforms CIL baselines by over $14%$ in open-loop evaluation and $30%$ in closed-loop testing on CARLA.} }
Endnote
%0 Conference Paper %T Learning to Drive Anywhere %A Ruizhao Zhu %A Peng Huang %A Eshed Ohn-Bar %A Venkatesh Saligrama %B Proceedings of The 7th Conference on Robot Learning %C Proceedings of Machine Learning Research %D 2023 %E Jie Tan %E Marc Toussaint %E Kourosh Darvish %F pmlr-v229-zhu23c %I PMLR %P 3631--3653 %U https://proceedings.mlr.press/v229/zhu23c.html %V 229 %X 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 GeCo, 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 the inherently imbalanced data distributions and location-dependent events. We demonstrate the benefits of our GeCo agent across multiple datasets, cities, and scalable deployment paradigms, i.e., centralized, semi-supervised, and distributed agent training. Specifically, GeCo outperforms CIL baselines by over $14%$ in open-loop evaluation and $30%$ in closed-loop testing on CARLA.
APA
Zhu, R., Huang, P., Ohn-Bar, E. & Saligrama, V.. (2023). Learning to Drive Anywhere. Proceedings of The 7th Conference on Robot Learning, in Proceedings of Machine Learning Research 229:3631-3653 Available from https://proceedings.mlr.press/v229/zhu23c.html.

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