Clustering of Driving Encounter Scenarios Using Connected Vehicle Trajectories

TL;DR

This paper introduces an unsupervised learning framework that clusters diverse driving encounter scenarios based on multi-vehicle GPS trajectories, aiding autonomous vehicle decision-making.

Contribution

It presents a novel two-layer framework combining deep autoencoders and clustering to analyze multi-vehicle interaction behaviors from GPS data.

Findings

Successfully clustered 2,568 naturalistic driving encounters.

Framework effectively captures spatiotemporal interaction characteristics.

Clusters can inform autonomous vehicle decision policies.

Abstract

Multi-vehicle interaction behavior classification and analysis offer in-depth knowledge to make an efficient decision for autonomous vehicles. This paper aims to cluster a wide range of driving encounter scenarios based only on multi-vehicle GPS trajectories. Towards this end, we propose a generic unsupervised learning framework comprising two layers: feature representation layer and clustering layer. In the layer of feature representation, we combine the deep autoencoders with a distance-based measure to map the sequential observations of driving encounters into a computationally tractable space that allows quantifying the spatiotemporal interaction characteristics of two vehicles. The clustering algorithm is then applied to the extracted representations to gather homogeneous driving encounters into groups. Our proposed generic framework is then evaluated using 2,568 naturalistic driving encounters. Experimental results demonstrate that our proposed generic framework incorporated with unsupervised learning can cluster multi-trajectory data into distinct groups. These clustering results could benefit decision-making policy analysis and design for autonomous vehicles.