mlsanityData to 7 Oct 2026

Description of Corner Cases in Automated Driving: Goals and Challenges

arXiv 2109.09607 ↗First posted 20 Sep 2021cs.LG cs.ROcited by 35

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Abstract (arXiv)

Scaling the distribution of automated vehicles requires handling various unexpected and possibly dangerous situations, termed corner cases (CC). Since many modules of automated driving systems are based on machine learning (ML), CC are an essential part of the data for their development. However, there is only a limited amount of CC data in large-scale data collections, which makes them challenging in the context of ML. With a better understanding of CC, offline applications, e.g., dataset analysis, and online methods, e.g., improved performance of automated driving systems, can be improved. While there are knowledge-based descriptions and taxonomies for CC, there is little research on machine-interpretable descriptions. In this extended abstract, we will give a brief overview of the challenges and goals of such a description.

Key facts. Cited by 35 papers in the map (data through 7 Oct 2026). Builds on 14 of its 31 references that are in the map. First posted on arXiv 20 Sep 2021.

Builds on

14 of 31 references.

References · 14

  1. 2016The Cityscapes Dataset for Semantic Urban Scene Understandingcited by 4,427
  2. 2017CARLA: An Open Urban Driving Simulatorcited by 2,431
  3. 2017DeepXplore: Automated Whitebox Testing of Deep Learning Systemscited by 307
  4. 2021SegmentMeIfYouCan: A Benchmark for Anomaly Segmentationcited by 150
  5. 2020Synthesize then Compare: Detecting Failures and Anomalies for Semantic Segmentationcited by 96
  6. 2018Scenarios for Development, Test and Validation of Automated Vehiclescited by 45
  7. 2019Towards Corner Case Detection for Autonomous Drivingcited by 39
  8. 2021An Application-Driven Conceptualization of Corner Cases for Perception in Highly Automated Drivingcited by 33
  9. 2021Weather and Light Level Classification for Autonomous Driving: Dataset, Baseline and Active Learningcited by 20
  10. 2019Requirements-driven Test Generation for Autonomous Vehicles with Machine Learning Componentscited by 17
  11. 2021Out-of-distribution Detection and Generation using Soft Brownian Offset Sampling and Autoencoderscited by 8
  12. 2018Using control synthesis to generate corner cases: A case study on autonomous drivingcited by 6
2 more
  1. 2021Diverse Complexity Measures for Dataset Curation in Self-drivingcited by 6
  2. 2021Reliving the Dataset: Combining the Visualization of Road Users' Interactions with Scenario Reconstruction in Virtual Realitycited by 3

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 3; 2022: 4; 2023: 8; 2024: 13; 2025: 6; 2026: 1.

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  1. 2023DiLu: A Knowledge-Driven Approach to Autonomous Driving with Large Language Modelscited by 204
  2. 20213D-VField: Adversarial Augmentation of Point Clouds for Domain Generalization in 3D Object Detectioncited by 43
  3. 2023Towards Knowledge-driven Autonomous Drivingcited by 29
  4. 2025NuPlanQA: A Large-Scale Dataset and Benchmark for Multi-View Driving Scene Understanding in Multi-Modal Large Language Modelscited by 29
  5. 2026DriveVA: Video Action Models are Zero-Shot Driverscited by 19
  6. 2023Perception Datasets for Anomaly Detection in Autonomous Driving: A Surveycited by 16
  7. 2024KoMA: Knowledge-driven Multi-agent Framework for Autonomous Driving with Large Language Modelscited by 16
  8. 2024AnoVox: A Benchmark for Multimodal Anomaly Detection in Autonomous Drivingcited by 14
  9. 2021Towards Traffic Scene Description: The Semantic Scene Graphcited by 12
  10. 2022Amodal Cityscapes: A New Dataset, its Generation, and an Amodal Semantic Segmentation Challenge Baselinecited by 12
  11. 2024Generating Out-Of-Distribution Scenarios Using Language Modelscited by 10
  12. 2023Exploring the Potential of World Models for Anomaly Detection in Autonomous Drivingcited by 7
23 more
  1. 2024Generating Traffic Scenarios via In-Context Learning to Learn Better Motion Plannercited by 6
  2. 2021Compressing Sensor Data for Remote Assistance of Autonomous Vehicles using Deep Generative Modelscited by 5
  3. 2022Multimodal Detection of Unknown Objects on Roads for Autonomous Drivingcited by 4
  4. 2024Hierarchical Visual Categories Modeling: A Joint Representation Learning and Density Estimation Framework for Out-of-Distribution Detectioncited by 4
  5. 2025VLM-C4L: Continual Core Dataset Learning with Corner Case Optimization via Vision-Language Models for Autonomous Drivingcited by 4
  6. 2025Large Language Model-assisted Autonomous Vehicle Recovery from Immobilizationcited by 3
  7. 2022Quantification of Actual Road User Behavior on the Basis of Given Traffic Rulescited by 2
  8. 2024Data selection method for assessment of autonomous vehiclescited by 2
  9. 2024TagOOD: A Novel Approach to Out-of-Distribution Detection via Vision-Language Representations and Class Center Learningcited by 2
  10. 2024Edge Case Detection in Automated Driving: Methods, Challenges, and Future Directionscited by 2
  11. 2022Experiments on Anomaly Detection in Autonomous Driving by Forward-Backward Style Transferscited by 1
  12. 2023Conditioning Latent-Space Clusters for Real-World Anomaly Classificationcited by 1
  13. 2024Hybrid Video Anomaly Detection for Anomalous Scenarios in Autonomous Drivingcited by 1
  14. 2024Label-Free Model Failure Detection for Lidar-based Point Cloud Segmentationcited by 1
  15. 2025Learning Isometric Embeddings of Road Networks using Multidimensional Scalingcited by 1
  16. 2023A Feature-based Approach for the Recognition of Image Quality Degradation in Automotive Applicationscited by 0
  17. 2023A Semi-Automated Corner Case Detection and Evaluation Pipelinecited by 0
  18. 2023Generating and Explaining Corner Cases Using Learnt Probabilistic Lane Graphscited by 0
  19. 2024Criteria for Uncertainty-based Corner Cases Detection in Instance Segmentationcited by 0
  20. 20243CSim: CARLA Corner Case Simulation for Control Assessment in Autonomous Drivingcited by 0
  21. 2024Physics-model-guided Worst-case Sampling for Safe Reinforcement Learningcited by 0
  22. 2025A Machine Learning Perspective on Automated Driving Corner Casescited by 0
  23. 2025Real-DRL: Teach and Learn in Realitycited by 0