# An Unsupervised Random Forest Clustering Technique for Automatic Traffic Scenario Categorization

arXiv: https://arxiv.org/abs/2004.02121 · HTML: https://mlsanity.com/paper/2004.02121/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Apr 2020.

## Built on by (most cited first, 13 of 13)

- [(Re)2H2O: Autonomous Driving Scenario Generation via Reversely Regularized Hybrid Offline-and-Online Reinforcement Learning](https://mlsanity.com/paper/2302.13726/) (2023)
- [Large Scale Autonomous Driving Scenarios Clustering with Self-supervised Feature Extraction](https://mlsanity.com/paper/2103.16101/) (2021)
- [Novelty Detection and Analysis of Traffic Scenario Infrastructures in the Latent Space of a Vision Transformer-Based Triplet Autoencoder](https://mlsanity.com/paper/2105.01924/) (2021)
- [Open-set Recognition based on the Combination of Deep Learning and Ensemble Method for Detecting Unknown Traffic Scenarios](https://mlsanity.com/paper/2105.07635/) (2021)
- [Traffic Scenario Clustering by Iterative Optimisation of Self-Supervised Networks Using a Random Forest Activation Pattern Similarity](https://mlsanity.com/paper/2105.07639/) (2021)
- [Counterfactual Explanations for Deep Learning-Based Traffic Forecasting](https://mlsanity.com/paper/2405.00456/) (2024)
- [KnAC: an approach for enhancing cluster analysis with background knowledge and explanations](https://mlsanity.com/paper/2112.08759/) (2021)
- [Expert-LaSTS: Expert-Knowledge Guided Latent Space for Traffic Scenarios](https://mlsanity.com/paper/2207.09120/) (2022)
- [Few-Shot Scenario Testing for Autonomous Vehicles Based on Neighborhood Coverage and Similarity](https://mlsanity.com/paper/2402.01795/) (2024)
- [Extraction and Analysis of Highway On-Ramp Merging Scenarios from Naturalistic Trajectory Data](https://mlsanity.com/paper/2104.05661/) (2021)
- [Exploring Self-Attention for Visual Intersection Classification](https://mlsanity.com/paper/2203.13977/) (2022)
- [Acceleration method for generating perception failure scenarios based on editing Markov process](https://mlsanity.com/paper/2407.00980/) (2024)
- [Exploring Semantic Clustering and Similarity Search for Heterogeneous Traffic Scenario Graph](https://mlsanity.com/paper/2507.05086/) (2025)

## Abstract (arXiv)

A modification of the Random Forest algorithm for the categorization of traffic situations is introduced in this paper. The procedure yields an unsupervised machine learning method. The algorithm generates a proximity matrix which contains a similarity measure. This matrix is then reordered with hierarchical clustering to achieve a graphically interpretable representation. It is shown how the resulting proximity matrix can be visually interpreted and how the variation of the methods' metaparameter reveals different insights into the data. The proposed method is able to cluster data from any data source. To demonstrate the methods' potential, multiple features derived from a traffic simulation are used in this paper. The knowledge of traffic scenario clusters is crucial to accelerate the validation process. The clue of the method is that scenario templates can be generated automatically from actual traffic situations. These templates can be employed in all stages of the development process. The results prove that the procedure is well suited for an automatic categorization of traffic scenarios. Diverse other applications can benefit from this work.