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

arXiv: https://arxiv.org/abs/2207.09120 · HTML: https://mlsanity.com/paper/2207.09120/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 31 references that are in the map.
- First posted on arXiv 19 Jul 2022.

## Builds on (most cited first, 15 of 15)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction](https://mlsanity.com/paper/1802.03426/) (2018)
- [FaceNet: A Unified Embedding for Face Recognition and Clustering](https://mlsanity.com/paper/1503.03832/) (2015)
- [Embedding Label Structures for Fine-Grained Feature Representation](https://mlsanity.com/paper/1512.02895/) (2015)
- [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)
- [Clustering of Driving Encounter Scenarios Using Connected Vehicle Trajectories](https://mlsanity.com/paper/1807.08415/) (2018)
- [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)
- [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)
- [An Entropy Based Outlier Score and its Application to Novelty Detection for Road Infrastructure Images](https://mlsanity.com/paper/2005.13288/) (2020)

## Built on by (most cited first, 2 of 2)

- [Traffic Scene Similarity: a Graph-based Contrastive Learning Approach](https://mlsanity.com/paper/2309.09720/) (2023)
- [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)

Clustering traffic scenarios and detecting novel scenario types are required for scenario-based testing of autonomous vehicles. These tasks benefit from either good similarity measures or good representations for the traffic scenarios. In this work, an expert-knowledge aided representation learning for traffic scenarios is presented. The latent space so formed is used for successful clustering and novel scenario type detection. Expert-knowledge is used to define objectives that the latent representations of traffic scenarios shall fulfill. It is presented, how the network architecture and loss is designed from these objectives, thereby incorporating expert-knowledge. An automatic mining strategy for traffic scenarios is presented, such that no manual labeling is required. Results show the performance advantage compared to baseline methods. Additionally, extensive analysis of the latent space is performed.