Unsupervised and Supervised Learning with the Random Forest Algorithm for Traffic Scenario Clustering and Classification

TL;DR

This paper introduces a combined unsupervised and supervised learning framework using Random Forests to automatically categorize traffic scenarios, aiding autonomous vehicle testing.

Contribution

It presents a novel hybrid approach with a new similarity measure and clustering/classification pipeline for traffic scenario analysis.

Findings

Effective automatic categorization of highway traffic scenarios

High accuracy in scenario classification with confidence thresholding

Potential to improve autonomous vehicle testing processes

Abstract

The goal of this paper is to provide a method, which is able to find categories of traffic scenarios automatically. The architecture consists of three main components: A microscopic traffic simulation, a clustering technique and a classification technique for the operational phase. The developed simulation tool models each vehicle separately, while maintaining the dependencies between each other. The clustering approach consists of a modified unsupervised Random Forest algorithm to find a data adaptive similarity measure between all scenarios. As part of this, the path proximity, a novel technique to determine a similarity based on the Random Forest algorithm is presented. In the second part of the clustering, the similarities are used to define a set of clusters. In the third part, a Random Forest classifier is trained using the defined clusters for the operational phase. A thresholding technique is described to ensure a certain confidence level for the class assignment. The method is applied for highway scenarios. The results show that the proposed method is an excellent approach to automatically categorize traffic scenarios, which is particularly relevant for testing autonomous vehicle functionality.