  Fleets of networked autonomous vehicles (AVs) collect terabytes of sensory
data, which is often transmitted to central servers (the ''cloud'') for
training machine learning (ML) models. Ideally, these fleets should upload all
their data, especially from rare operating contexts, in order to train robust
ML models. However, this is infeasible due to prohibitive network bandwidth and
data labeling costs. Instead, we propose a cooperative data sampling strategy
where geo-distributed AVs collaborate to collect a diverse ML training dataset
in the cloud. Since the AVs have a shared objective but minimal information
about each other's local data distribution and perception model, we can
naturally cast cooperative data collection as an $N$-player mathematical game.
We show that our cooperative sampling strategy uses minimal information to
converge to a centralized oracle policy with complete information about all
AVs. Moreover, we theoretically characterize the performance benefits of our
game-theoretic strategy compared to greedy sampling. Finally, we experimentally
demonstrate that our method outperforms standard benchmarks by up to $21.9\%$
on 4 perception datasets, including for autonomous driving in adverse weather
conditions. Crucially, our experimental results on real-world datasets closely
align with our theoretical guarantees.
