  Machine learning has emerged as a promising paradigm for enabling connected,
automated vehicles to autonomously cruise the streets and react to unexpected
situations. A key challenge, however, is to collect and select real-time and
reliable information for the correct classification of unexpected, and often
rare, situations that may happen on the road. Indeed, the data generated by
vehicles, or received from neighboring vehicles, may be affected by errors or
have different levels of resolution and freshness. To tackle this challenge, we
propose an active learning framework that, leveraging the information collected
through onboard sensors as well as received from other vehicles, effectively
deals with scarce and noisy data. In particular, given the available
information, our solution selects the data to add to the training set by
trading off between two essential features, namely, quality and diversity. The
results, obtained using real-world data sets, show that the proposed method
significantly outperforms state-of-the-art solutions, providing high
classification accuracy at the cost of a limited bandwidth requirement for the
data exchange between vehicles.
