# Space, Time, and Interaction: A Taxonomy of Corner Cases in Trajectory Datasets for Automated Driving

arXiv: https://arxiv.org/abs/2210.08885 · HTML: https://mlsanity.com/paper/2210.08885/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 58 references that are in the map.
- First posted on arXiv 17 Oct 2022.

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

- [CARLA: An Open Urban Driving Simulator](https://mlsanity.com/paper/1711.03938/) (2017)
- [A Survey of Autonomous Driving: Common Practices and Emerging Technologies](https://mlsanity.com/paper/1906.05113/) (2019)
- [DeepXplore: Automated Whitebox Testing of Deep Learning Systems](https://mlsanity.com/paper/1705.06640/) (2017)
- [Machine Learning Testing: Survey, Landscapes and Horizons](https://mlsanity.com/paper/1906.10742/) (2019)
- [Inspect, Understand, Overcome: A Survey of Practical Methods for AI Safety](https://mlsanity.com/paper/2104.14235/) (2021)
- [An Application-Driven Conceptualization of Corner Cases for Perception in Highly Automated Driving](https://mlsanity.com/paper/2103.03678/) (2021)
- [Risk-Aware High-level Decisions for Automated Driving at Occluded Intersections with Reinforcement Learning](https://mlsanity.com/paper/2004.04450/) (2020)
- [Finding Critical Scenarios for Automated Driving Systems: A Systematic Literature Review](https://mlsanity.com/paper/2110.08664/) (2021)
- [Toward Optimal Probabilistic Active Learning Using a Bayesian Approach](https://mlsanity.com/paper/2006.01732/) (2020)
- [A-Eye: Driving with the Eyes of AI for Corner Case Generation](https://mlsanity.com/paper/2202.10803/) (2022)
- [Engineering Autonomous Driving Software](https://mlsanity.com/paper/1409.6579/) (2014)

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

- [Perception Datasets for Anomaly Detection in Autonomous Driving: A Survey](https://mlsanity.com/paper/2302.02790/) (2023)
- [AnoVox: A Benchmark for Multimodal Anomaly Detection in Autonomous Driving](https://mlsanity.com/paper/2405.07865/) (2024)
- [Perception Without Vision for Trajectory Prediction: Ego Vehicle Dynamics as Scene Representation for Efficient Active Learning in Autonomous Driving](https://mlsanity.com/paper/2405.09049/) (2024)
- [Edge Case Detection in Automated Driving: Methods, Challenges, and Future Directions](https://mlsanity.com/paper/2410.08491/) (2024)
- [A Semi-Automated Corner Case Detection and Evaluation Pipeline](https://mlsanity.com/paper/2305.16369/) (2023)
- [Criteria for Uncertainty-based Corner Cases Detection in Instance Segmentation](https://mlsanity.com/paper/2404.11266/) (2024)
- [ExceptionDrive: A Planning-Oriented Counterfactual Corner-Case Benchmark for Autonomous Driving](https://mlsanity.com/paper/2609.37871/) (2026)

## Abstract (arXiv)

Trajectory data analysis is an essential component for highly automated driving. Complex models developed with these data predict other road users' movement and behavior patterns. Based on these predictions - and additional contextual information such as the course of the road, (traffic) rules, and interaction with other road users - the highly automated vehicle (HAV) must be able to reliably and safely perform the task assigned to it, e.g., moving from point A to B. Ideally, the HAV moves safely through its environment, just as we would expect a human driver to do. However, if unusual trajectories occur, so-called trajectory corner cases, a human driver can usually cope well, but an HAV can quickly get into trouble. In the definition of trajectory corner cases, which we provide in this work, we will consider the relevance of unusual trajectories with respect to the task at hand. Based on this, we will also present a taxonomy of different trajectory corner cases. The categorization of corner cases into the taxonomy will be shown with examples and is done by cause and required data sources. To illustrate the complexity between the machine learning (ML) model and the corner case cause, we present a general processing chain underlying the taxonomy.