# Traffic Scenario Clustering by Iterative Optimisation of Self-Supervised Networks Using a Random Forest Activation Pattern Similarity

arXiv: https://arxiv.org/abs/2105.07639 · HTML: https://mlsanity.com/paper/2105.07639/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 39 references that are in the map.
- First posted on arXiv 17 May 2021.

## Builds on (most cited first, 19 of 19)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction](https://mlsanity.com/paper/1802.03426/) (2018)
- [iCaRL: Incremental Classifier and Representation Learning](https://mlsanity.com/paper/1611.07725/) (2016)
- [Unsupervised Representation Learning by Predicting Image Rotations](https://mlsanity.com/paper/1803.07728/) (2018)
- [Virtual Adversarial Training: A Regularization Method for Supervised and Semi-Supervised Learning](https://mlsanity.com/paper/1704.03976/) (2017)
- [Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results](https://mlsanity.com/paper/1703.01780/) (2017)
- [A Survey on Deep Transfer Learning](https://mlsanity.com/paper/1808.01974/) (2018)
- [The highD Dataset: A Drone Dataset of Naturalistic Vehicle Trajectories on German Highways for Validation of Highly Automated Driving Systems](https://mlsanity.com/paper/1810.05642/) (2018)
- [Learning to Discover Novel Visual Categories via Deep Transfer Clustering](https://mlsanity.com/paper/1908.09884/) (2019)
- [Learning Spatio-Temporal Features with 3D Residual Networks for Action Recognition](https://mlsanity.com/paper/1708.07632/) (2017)
- [Automatically Discovering and Learning New Visual Categories with Ranking Statistics](https://mlsanity.com/paper/2002.05714/) (2020)
- [Scenarios for Development, Test and Validation of Automated Vehicles](https://mlsanity.com/paper/1801.08598/) (2018)
- [LSD-C: Linearly Separable Deep Clusters](https://mlsanity.com/paper/2006.10039/) (2020)
- [Shuffle and Learn: Unsupervised Learning using Temporal Order Verification](https://mlsanity.com/paper/1603.08561/) (2016)
- [An Unsupervised Random Forest Clustering Technique for Automatic Traffic Scenario Categorization](https://mlsanity.com/paper/2004.02121/) (2020)
- [A Deep Learning Framework for Generation and Analysis of Driving Scenario Trajectories](https://mlsanity.com/paper/2007.14524/) (2020)
- [Unsupervised and Supervised Learning with the Random Forest Algorithm for Traffic Scenario Clustering and Classification](https://mlsanity.com/paper/2004.02126/) (2020)
- [Deep Representation Learning and Clustering of Traffic Scenarios](https://mlsanity.com/paper/2007.07740/) (2020)

## Built on by (most cited first, 3 of 3)

- [Expert-LaSTS: Expert-Knowledge Guided Latent Space for Traffic Scenarios](https://mlsanity.com/paper/2207.09120/) (2022)
- [ExAgt: Expert-guided Augmentation for Representation Learning of Traffic Scenarios](https://mlsanity.com/paper/2207.08609/) (2022)
- [Behavior-Centric Extraction of Scenarios from Highway Traffic Data and their Domain-Knowledge-Guided Clustering using CVQ-VAE](https://mlsanity.com/paper/2603.16964/) (2026)

## Abstract (arXiv)

Traffic scenario categorisation is an essential component of automated driving, for e.,g., in motion planning algorithms and their validation. Finding new relevant scenarios without handcrafted steps reduce the required resources for the development of autonomous driving dramatically. In this work, a method is proposed to address this challenge by introducing a clustering technique based on a novel data-adaptive similarity measure, called Random Forest Activation Pattern (RFAP) similarity. The RFAP similarity is generated using a tree encoding scheme in a Random Forest algorithm. The clustering method proposed in this work takes into account that there are labelled scenarios available and the information from the labelled scenarios can help to guide the clustering of unlabelled scenarios. It consists of three steps. First, a self-supervised Convolutional Neural Network~(CNN) is trained on all available traffic scenarios using a defined self-supervised objective. Second, the CNN is fine-tuned for classification of the labelled scenarios. Third, using the labelled and unlabelled scenarios an iterative optimisation procedure is performed for clustering. In the third step at each epoch of the iterative optimisation, the CNN is used as a feature generator for an unsupervised Random Forest. The trained forest, in turn, provides the RFAP similarity to adapt iteratively the feature generation process implemented by the CNN. Extensive experiments and ablation studies have been done on the highD dataset. The proposed method shows superior performance compared to baseline clustering techniques.