# Clustering of Driving Encounter Scenarios Using Connected Vehicle Trajectories

arXiv: https://arxiv.org/abs/1807.08415 · HTML: https://mlsanity.com/paper/1807.08415/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Jul 2018.

## Built on by (most cited first, 9 of 9)

- [Spatial Retrieval Augmented Autonomous Driving](https://mlsanity.com/paper/2512.06865/) (2025)
- [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)
- [UniTE: A Survey and Unified Pipeline for Pre-training Spatiotemporal Trajectory Embeddings](https://mlsanity.com/paper/2407.12550/) (2024)
- [A Survey of Distance-Based Vessel Trajectory Clustering: Data Pre-processing, Methodologies, Applications, and Experimental Evaluation](https://mlsanity.com/paper/2407.11084/) (2024)
- [Driving-RAG: Driving Scenarios Embedding, Search, and RAG Applications](https://mlsanity.com/paper/2504.04419/) (2025)
- [Expert-LaSTS: Expert-Knowledge Guided Latent Space for Traffic Scenarios](https://mlsanity.com/paper/2207.09120/) (2022)
- [Toward Unsupervised Test Scenario Extraction for Automated Driving Systems from Urban Naturalistic Road Traffic Data](https://mlsanity.com/paper/2202.06608/) (2022)
- [Evolving Testing Scenario Generation Method and Intelligence Evaluation Framework for Automated Vehicles](https://mlsanity.com/paper/2306.07142/) (2023)
- [Urban Deceleration Behavior Modes Under Scene Context: An Early-Kinematic Classifier from Argoverse 2 Multi-Agent Trajectories](https://mlsanity.com/paper/2607.00027/) (2026)

## Abstract (arXiv)

Multi-vehicle interaction behavior classification and analysis offer in-depth knowledge to make an efficient decision for autonomous vehicles. This paper aims to cluster a wide range of driving encounter scenarios based only on multi-vehicle GPS trajectories. Towards this end, we propose a generic unsupervised learning framework comprising two layers: feature representation layer and clustering layer. In the layer of feature representation, we combine the deep autoencoders with a distance-based measure to map the sequential observations of driving encounters into a computationally tractable space that allows quantifying the spatiotemporal interaction characteristics of two vehicles. The clustering algorithm is then applied to the extracted representations to gather homogeneous driving encounters into groups. Our proposed generic framework is then evaluated using 2,568 naturalistic driving encounters. Experimental results demonstrate that our proposed generic framework incorporated with unsupervised learning can cluster multi-trajectory data into distinct groups. These clustering results could benefit decision-making policy analysis and design for autonomous vehicles.