# Active Learning of Driving Scenario Trajectories

arXiv: https://arxiv.org/abs/2108.03217 · HTML: https://mlsanity.com/paper/2108.03217/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 34 references that are in the map.
- First posted on arXiv 6 Aug 2021.

## Builds on (most cited first, 11 of 11)

- [Deep Bayesian Active Learning with Image Data](https://mlsanity.com/paper/1703.02910/) (2017)
- [BatchBALD: Efficient and Diverse Batch Acquisition for Deep Bayesian Active Learning](https://mlsanity.com/paper/1906.08158/) (2019)
- [Clustering with Same-Cluster Queries](https://mlsanity.com/paper/1606.02404/) (2016)
- [Local algorithms for interactive clustering](https://mlsanity.com/paper/1312.6724/) (2013)
- [A Deep Learning Framework for Generation and Analysis of Driving Scenario Trajectories](https://mlsanity.com/paper/2007.14524/) (2020)
- [Deep Active Learning for Anomaly Detection](https://mlsanity.com/paper/1805.09411/) (2018)
- [Efficient Online Learning for Optimizing Value of Information: Theory and Application to Interactive Troubleshooting](https://mlsanity.com/paper/1703.05452/) (2017)
- [Active Learning with Logged Data](https://mlsanity.com/paper/1802.09069/) (2018)
- [Model-Centric and Data-Centric Aspects of Active Learning for Deep Neural Networks](https://mlsanity.com/paper/2009.10835/) (2020)
- [m-TSNE: A Framework for Visualizing High-Dimensional Multivariate Time Series](https://mlsanity.com/paper/1708.07942/) (2017)
- [A Generic Framework for Clustering Vehicle Motion Trajectories](https://mlsanity.com/paper/2009.12443/) (2020)

## Built on by (most cited first, 7 of 7)

- [A Survey on Autonomous Driving Datasets: Statistics, Annotation Quality, and a Future Outlook](https://mlsanity.com/paper/2401.01454/) (2024)
- [Correlation Clustering with Active Learning of Pairwise Similarities](https://mlsanity.com/paper/2302.10295/) (2023)
- [A Unified Active Learning Framework for Annotating Graph Data with Application to Software Source Code Performance Prediction](https://mlsanity.com/paper/2304.13032/) (2023)
- [Passive and Active Learning of Driver Behavior from Electric Vehicles](https://mlsanity.com/paper/2203.02179/) (2022)
- [A Markovian Formalism for Active Querying](https://mlsanity.com/paper/2306.08001/) (2023)
- [Information-Theoretic Active Correlation Clustering](https://mlsanity.com/paper/2402.03587/) (2024)
- [Cold-Start Active Correlation Clustering](https://mlsanity.com/paper/2509.25376/) (2025)

## Abstract (arXiv)

Annotated driving scenario trajectories are crucial for verification and validation of autonomous vehicles. However, annotation of such trajectories based only on explicit rules (i.e. knowledge-based methods) may be prone to errors, such as false positive/negative classification of scenarios that lie on the border of two scenario classes, missing unknown scenario classes, or even failing to detect anomalies. On the other hand, verification of labels by annotators is not cost-efficient. For this purpose, active learning (AL) could potentially improve the annotation procedure by including an annotator/expert in an efficient way. In this study, we develop a generic active learning framework to annotate driving trajectory time series data. We first compute an embedding of the trajectories into a latent space in order to extract the temporal nature of the data. Given such an embedding, the framework becomes task agnostic since active learning can be performed using any classification method and any query strategy, regardless of the structure of the original time series data. Furthermore, we utilize our active learning framework to discover unknown driving scenario trajectories. This will ensure that previously unknown trajectory types can be effectively detected and included in the labeled dataset. We evaluate our proposed framework in different settings on novel real-world datasets consisting of driving trajectories collected by Volvo Cars Corporation. We observe that active learning constitutes an effective tool for labelling driving trajectories as well as for detecting unknown classes. Expectedly, the quality of the embedding plays an important role in the success of the proposed framework.