  Understanding multi-vehicle interactive behaviors with temporal sequential
observations is crucial for autonomous vehicles to make appropriate decisions
in an uncertain traffic environment. On-demand similarity measures are
significant for autonomous vehicles to deal with massive interactive driving
behaviors by clustering and classifying diverse scenarios. This paper proposes
a general approach for measuring spatiotemporal similarity of interactive
behaviors using a multivariate matrix profile technique. The key attractive
features of the approach are its superior space and time complexity, real-time
online computing for streaming traffic data, and possible capability of
leveraging hardware for parallel computation. The proposed approach is
validated through automatically discovering similar interactive driving
behaviors at intersections from sequential data.
