mlsanityData to 7 Oct 2026

Corner Cases for Visual Perception in Automated Driving: Some Guidance on Detection Approaches

arXiv 2102.05897 ↗First posted 11 Feb 2021cs.CVcited by 27

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Abstract (arXiv)

Automated driving has become a major topic of interest not only in the active research community but also in mainstream media reports. Visual perception of such intelligent vehicles has experienced large progress in the last decade thanks to advances in deep learning techniques but some challenges still remain. One such challenge is the detection of corner cases. They are unexpected and unknown situations that occur while driving. Conventional visual perception methods are often not able to detect them because corner cases have not been witnessed during training. Hence, their detection is highly safety-critical, and detection methods can be applied to vast amounts of collected data to select suitable training data. A reliable detection of corner cases will not only further automate the data selection procedure and increase safety in autonomous driving but can thereby also affect the public acceptance of the new technology in a positive manner. In this work, we continue a previous systematization of corner cases on different levels by an extended set of examples for each level. Moreover, we group detection approaches into different categories and link them with the corner case levels. Hence, we give directions to showcase specific corner cases and basic guidelines on how to technically detect them.

Key facts. Cited by 27 papers in the map (data through 7 Oct 2026). Builds on 33 of its 44 references that are in the map. First posted on arXiv 11 Feb 2021.

Builds on

33 of 44 references.

References · 33

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 2; 2022: 2; 2023: 10; 2024: 4; 2025: 6; 2026: 3.

27 papers.

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  1. 2023Street-View Image Generation from a Bird's-Eye View Layoutcited by 102
  2. 2022CODA: A Real-World Road Corner Case Dataset for Object Detection in Autonomous Drivingcited by 100
  3. 2024Automated Evaluation of Large Vision-Language Models on Self-driving Corner Casescited by 75
  4. 2021An Application-Driven Conceptualization of Corner Cases for Perception in Highly Automated Drivingcited by 33
  5. 2023Milestones in Autonomous Driving and Intelligent Vehicles Part I: Control, Computing System Design, Communication, HD Map, Testing, and Human Behaviorscited by 25
  6. 2024AnoVox: A Benchmark for Multimodal Anomaly Detection in Autonomous Drivingcited by 14
  7. 2025SafeDriveRAG: Towards Safe Autonomous Driving with Knowledge Graph-based Retrieval-Augmented Generationcited by 14
  8. 20233D Data Augmentation for Driving Scenes on Cameracited by 12
  9. 2025HiBug2: Efficient and Interpretable Error Slice Discovery for Comprehensive Model Debuggingcited by 11
  10. 2023Survey on video anomaly detection in dynamic scenes with moving camerascited by 10
  11. 2023Deep Learning Safety Concerns in Automated Driving Perceptioncited by 10
  12. 2023Exploring the Potential of World Models for Anomaly Detection in Autonomous Drivingcited by 7
15 more
  1. 2022A-Eye: Driving with the Eyes of AI for Corner Case Generationcited by 5
  2. 2025ECCV 2024 W-CODA: 1st Workshop on Multimodal Perception and Comprehension of Corner Cases in Autonomous Drivingcited by 5
  3. 2021Does Redundancy in AI Perception Systems Help to Test for Super-Human Automated Driving Performance?cited by 4
  4. 2023CC-SGG: Corner Case Scenario Generation using Learned Scene Graphscited by 4
  5. 2025RALAD: Bridging the Real-to-Sim Domain Gap in Autonomous Driving with Retrieval-Augmented Learningcited by 2
  6. 2025Stereo-based 3D Anomaly Object Detection for Autonomous Driving: A New Dataset and Baselinecited by 2
  7. 2023Conditioning Latent-Space Clusters for Real-World Anomaly Classificationcited by 1
  8. 2024Outline of an Independent Systematic Blackbox Test for ML-based Systemscited by 1
  9. 2024Label-Free Model Failure Detection for Lidar-based Point Cloud Segmentationcited by 1
  10. 2023A Semi-Automated Corner Case Detection and Evaluation Pipelinecited by 0
  11. 2023survAIval: Survival Analysis with the Eyes of AIcited by 0
  12. 2025A Machine Learning Perspective on Automated Driving Corner Casescited by 0
  13. 2026NSF-HRPT: Neural Semantic Field meets Hierarchical Risk Perception Tree for Safety-Critical Scenario Assessmentcited by 0
  14. 2026Leveraging Visual and Geometric Priors for Metric-scale and Complete Vehicle Gaussian Reconstruction from Limited Viewscited by 0
  15. 2026Probabilistic Modelling of Operational Design Domains, A New Approach for Testing AI Systemscited by 0