Measuring Similarity of Interactive Driving Behaviors Using Matrix Profile

TL;DR

This paper introduces a novel matrix profile-based method for measuring the similarity of interactive driving behaviors, enabling real-time analysis and clustering of complex traffic scenarios for autonomous vehicles.

Contribution

It presents a new approach using multivariate matrix profiles for efficient, real-time similarity measurement of multi-vehicle behaviors in traffic environments.

Findings

Successfully discovers similar driving behaviors at intersections from sequential data.

Demonstrates superior space and time efficiency for real-time traffic analysis.

Capable of leveraging hardware for parallel computation to enhance performance.

Abstract

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.