Measuring Similarity of Interactive Driving Behaviors Using Matrix Profile
This work addresses the need for efficient similarity measures to cluster and classify diverse driving scenarios for autonomous vehicles, representing an incremental improvement in method application.
The paper tackles the problem of measuring similarity in interactive driving behaviors for autonomous vehicles by proposing a multivariate matrix profile technique, achieving real-time online computing and superior space and time complexity for streaming traffic data.
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.