Unsupervised and supervised learning with the random forest algorithm for traffic scenario clustering and classification

Friedrich Kruber, Jonas Wurst, Eduardo Sanchez Morales, Samarjit Chakraborty, Michael Botsch

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

19 Scopus citations


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.

Original languageEnglish
Title of host publication2019 IEEE Intelligent Vehicles Symposium, IV 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages8
ISBN (Electronic)9781728105604
StatePublished - Jun 2019
Event30th IEEE Intelligent Vehicles Symposium, IV 2019 - Paris, France
Duration: 9 Jun 201912 Jun 2019

Publication series

NameIEEE Intelligent Vehicles Symposium, Proceedings


Conference30th IEEE Intelligent Vehicles Symposium, IV 2019


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