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

5 Apr 2020Friedrich KruberJonas WurstEduardo Sánchez MoralesSamarjit ChakrabortyMichael Botsch

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... (read more)

PDF Abstract

Results from the Paper

  Submit results from this paper to get state-of-the-art GitHub badges and help the community compare results to other papers.

Methods used in the Paper

🤖 No Methods Found Help the community by adding them if they're not listed; e.g. Deep Residual Learning for Image Recognition uses ResNet