activity
20192022
most citedAn Unsupervised Random Forest Clustering Technique for Automatic Traffic Scenario Categorization

53 citations · 137 across the 8 of their papers we have counts for

collaborators

8 papers

cs.CV2022★ 12 cited

Expert-LaSTS: Expert-Knowledge Guided Latent Space for Traffic Scenarios

Jonas Wurst, Lakshman Balasubramanian, Michael Botsch +1

Clustering traffic scenarios and detecting novel scenario types are required for scenario-based testing of autonomous vehicles. These tasks benefit from either good similarity meas…

cs.CV2022

ExAgt: Expert-guided Augmentation for Representation Learning of Traffic Scenarios

Lakshman Balasubramanian, Jonas Wurst, Robin Egolf +3

Representation learning in recent years has been addressed with self-supervised learning methods. The input data is augmented into two distorted views and an encoder learns the rep…

cs.CV2021

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

Lakshman Balasubramanian, Jonas Wurst, Michael Botsch +1

Traffic scenario categorisation is an essential component of automated driving, for e.\,g., in motion planning algorithms and their validation. Finding new relevant scenarios witho…

cs.CV2021★ 13 cited

Novelty Detection and Analysis of Traffic Scenario Infrastructures in the Latent Space of a Vision Transformer-Based Triplet Autoencoder

Jonas Wurst, Lakshman Balasubramanian, Michael Botsch +1

Detecting unknown and untested scenarios is crucial for scenario-based testing. Scenario-based testing is considered to be a possible approach to validate autonomous vehicles. A tr…

cs.LG2020★ 5 cited

An Entropy Based Outlier Score and its Application to Novelty Detection for Road Infrastructure Images

Jonas Wurst, Alberto Flores Fernández, Michael Botsch +1

A novel unsupervised outlier score, which can be embedded into graph based dimensionality reduction techniques, is presented in this work. The score uses the directed nearest neigh…

eess.SP2020★ 41 cited

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

Friedrich Kruber, Jonas Wurst, Eduardo Sánchez Morales +2

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 microscopi…