# An Automated Analysis Framework for Trajectory Datasets

arXiv: https://arxiv.org/abs/2202.07438 · HTML: https://mlsanity.com/paper/2202.07438/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 34 references that are in the map.
- First posted on arXiv 12 Feb 2022.

## Builds on (most cited first, 8 of 8)

- [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)
- [INTERACTION Dataset: An INTERnational, Adversarial and Cooperative moTION Dataset in Interactive Driving Scenarios with Semantic Maps](https://mlsanity.com/paper/1910.03088/) (2019)
- [Convolutional Social Pooling for Vehicle Trajectory Prediction](https://mlsanity.com/paper/1805.06771/) (2018)
- [The inD Dataset: A Drone Dataset of Naturalistic Road User Trajectories at German Intersections](https://mlsanity.com/paper/1911.07602/) (2019)
- [Top-view Trajectories: A Pedestrian Dataset of Vehicle-Crowd Interaction from Controlled Experiments and Crowded Campus](https://mlsanity.com/paper/1902.00487/) (2019)
- [Is it Safe to Drive? An Overview of Factors, Challenges, and Datasets for Driveability Assessment in Autonomous Driving](https://mlsanity.com/paper/1811.11277/) (2018)
- [Anomaly Detection in Trajectory Data with Normalizing Flows](https://mlsanity.com/paper/2004.05958/) (2020)
- [Review and Perspective for Distance Based Trajectory Clustering](https://mlsanity.com/paper/1508.04904/) (2015)

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

- [MEAT: Maneuver Extraction from Agent Trajectories](https://mlsanity.com/paper/2206.05158/) (2022)
- [Amelia: A Large Dataset and Benchmark for Airport Surface Movement Forecasting](https://mlsanity.com/paper/2407.21185/) (2024)

## Abstract (arXiv)

Trajectory datasets of road users have become more important in the last years for safety validation of highly automated vehicles. Several naturalistic trajectory datasets with each more than 10.000 tracks were released and others will follow. Considering this amount of data, it is necessary to be able to compare these datasets in-depth with ease to get an overview. By now, the datasets' own provided information is mainly limited to meta-data and qualitative descriptions which are mostly not consistent with other datasets. This is insufficient for users to differentiate the emerging datasets for application-specific selection. Therefore, an automated analysis framework is proposed in this work. Starting with analyzing individual tracks, fourteen elementary characteristics, so-called detection types, are derived and used as the base of this framework. To describe each traffic scenario precisely, the detections are subdivided into common metrics, clustering methods and anomaly detection. Those are combined using a modular approach. The detections are composed into new scores to describe three defined attributes of each track data quantitatively: interaction, anomaly and relevance. These three scores are calculated hierarchically for different abstract layers to provide an overview not just between datasets but also for tracks, spatial regions and individual situations. So, an objective comparison between datasets can be realized. Furthermore, it can help to get a deeper understanding of the recorded infrastructure and its effect on road user behavior. To test the validity of the framework, a study is conducted to compare the scores with human perception. Additionally, several datasets are compared.