# Towards Corner Case Detection for Autonomous Driving

arXiv: https://arxiv.org/abs/1902.09184 · HTML: https://mlsanity.com/paper/1902.09184/

## Key facts

- Cited by 39 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Feb 2019.

## Built on by (most cited first, 30 of 39)

- [DiLu: A Knowledge-Driven Approach to Autonomous Driving with Large Language Models](https://mlsanity.com/paper/2309.16292/) (2023)
- [Software Engineering for AI-Based Systems: A Survey](https://mlsanity.com/paper/2105.01984/) (2021)
- [Inspect, Understand, Overcome: A Survey of Practical Methods for AI Safety](https://mlsanity.com/paper/2104.14235/) (2021)
- [Description of Corner Cases in Automated Driving: Goals and Challenges](https://mlsanity.com/paper/2109.09607/) (2021)
- [An Application-Driven Conceptualization of Corner Cases for Perception in Highly Automated Driving](https://mlsanity.com/paper/2103.03678/) (2021)
- [Towards Knowledge-driven Autonomous Driving](https://mlsanity.com/paper/2312.04316/) (2023)
- [Corner Cases for Visual Perception in Automated Driving: Some Guidance on Detection Approaches](https://mlsanity.com/paper/2102.05897/) (2021)
- [Enhancing Autonomous Vehicle Training with Language Model Integration and Critical Scenario Generation](https://mlsanity.com/paper/2404.08570/) (2024)
- [Reinforced Refinement with Self-Aware Expansion for End-to-End Autonomous Driving](https://mlsanity.com/paper/2506.09800/) (2025)
- [DeepGuard: A Framework for Safeguarding Autonomous Driving Systems from Inconsistent Behavior](https://mlsanity.com/paper/2111.09533/) (2021)
- [VPTR: Efficient Transformers for Video Prediction](https://mlsanity.com/paper/2203.15836/) (2022)
- [Finding Critical Scenarios for Automated Driving Systems: A Systematic Literature Review](https://mlsanity.com/paper/2110.08664/) (2021)
- [Amodal Cityscapes: A New Dataset, its Generation, and an Amodal Semantic Segmentation Challenge Baseline](https://mlsanity.com/paper/2206.00527/) (2022)
- [Video Prediction by Efficient Transformers](https://mlsanity.com/paper/2212.06026/) (2022)
- [Enhancing Autonomous Driving Systems with On-Board Deployed Large Language Models](https://mlsanity.com/paper/2504.11514/) (2025)
- [Generating Out-Of-Distribution Scenarios Using Language Models](https://mlsanity.com/paper/2411.16554/) (2024)
- [Exploring the Potential of World Models for Anomaly Detection in Autonomous Driving](https://mlsanity.com/paper/2308.05701/) (2023)
- [A-Eye: Driving with the Eyes of AI for Corner Case Generation](https://mlsanity.com/paper/2202.10803/) (2022)
- [Hierarchical End-to-End Autonomous Driving: Integrating BEV Perception with Deep Reinforcement Learning](https://mlsanity.com/paper/2409.17659/) (2024)
- [Corner case data description and detection](https://mlsanity.com/paper/2101.02494/) (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)
- [Does Redundancy in AI Perception Systems Help to Test for Super-Human Automated Driving Performance?](https://mlsanity.com/paper/2112.04758/) (2021)
- [VLM-C4L: Continual Core Dataset Learning with Corner Case Optimization via Vision-Language Models for Autonomous Driving](https://mlsanity.com/paper/2503.23046/) (2025)
- [Exploring ML testing in practice -- Lessons learned from an interactive rapid review with Axis Communications](https://mlsanity.com/paper/2203.16225/) (2022)
- [Large Language Model-assisted Autonomous Vehicle Recovery from Immobilization](https://mlsanity.com/paper/2510.26023/) (2025)
- [Improving Performance of Semantic Segmentation CycleGANs by Noise Injection into the Latent Segmentation Space](https://mlsanity.com/paper/2201.06415/) (2022)
- [Integrating End-to-End and Modular Driving Approaches for Online Corner Case Detection in Autonomous Driving](https://mlsanity.com/paper/2409.01178/) (2024)
- [Edge Case Detection in Automated Driving: Methods, Challenges, and Future Directions](https://mlsanity.com/paper/2410.08491/) (2024)
- [RALAD: Bridging the Real-to-Sim Domain Gap in Autonomous Driving with Retrieval-Augmented Learning](https://mlsanity.com/paper/2501.12296/) (2025)
- [Conditioning Latent-Space Clusters for Real-World Anomaly Classification](https://mlsanity.com/paper/2309.09676/) (2023)

## Abstract (arXiv)

The progress in autonomous driving is also due to the increased availability of vast amounts of training data for the underlying machine learning approaches. Machine learning systems are generally known to lack robustness, e.g., if the training data did rarely or not at all cover critical situations. The challenging task of corner case detection in video, which is also somehow related to unusual event or anomaly detection, aims at detecting these unusual situations, which could become critical, and to communicate this to the autonomous driving system (online use case). Such a system, however, could be also used in offline mode to screen vast amounts of data and select only the relevant situations for storing and (re)training machine learning algorithms. So far, the approaches for corner case detection have been limited to videos recorded from a fixed camera, mostly for security surveillance. In this paper, we provide a formal definition of a corner case and propose a system framework for both the online and the offline use case that can handle video signals from front cameras of a naturally moving vehicle and can output a corner case score.