mlsanityData to 14 Oct 2026

CODA: A Real-World Road Corner Case Dataset for Object Detection in Autonomous Driving

arXiv 2203.07724 ↗First posted 15 Mar 2022cs.CV cs.LG cs.ROcited by 100

Not on the map. Why?

Cited by 23 in End-to-end autonomous driving, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

Contemporary deep-learning object detection methods for autonomous driving usually assume prefixed categories of common traffic participants, such as pedestrians and cars. Most existing detectors are unable to detect uncommon objects and corner cases (e.g., a dog crossing a street), which may lead to severe accidents in some situations, making the timeline for the real-world application of reliable autonomous driving uncertain. One main reason that impedes the development of truly reliably self-driving systems is the lack of public datasets for evaluating the performance of object detectors on corner cases. Hence, we introduce a challenging dataset named CODA that exposes this critical problem of vision-based detectors. The dataset consists of 1500 carefully selected real-world driving scenes, each containing four object-level corner cases (on average), spanning more than 30 object categories. On CODA, the performance of standard object detectors trained on large-scale autonomous driving datasets significantly drops to no more than 12.8% in mAR. Moreover, we experiment with the state-of-the-art open-world object detector and find that it also fails to reliably identify the novel objects in CODA, suggesting that a robust perception system for autonomous driving is probably still far from reach. We expect our CODA dataset to facilitate further research in reliable detection for real-world autonomous driving. Our dataset will be released at https://coda-dataset.github.io.

Key facts. Cited by 100 papers in the map (data through 14 Oct 2026). Builds on 34 of its 50 references that are in the map. First posted on arXiv 15 Mar 2022.

Builds on

34 of 50 references.

References · 34

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  2. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,744
  3. 2014Microsoft COCO: Common Objects in Contextcited by 17,608
  4. 2021Swin Transformer: Hierarchical Vision Transformer using Shifted Windowscited by 12,139
  5. 2015Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networkscited by 9,639
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  7. 2016Feature Pyramid Networks for Object Detectioncited by 5,036
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  10. 2016The Cityscapes Dataset for Semantic Urban Scene Understandingcited by 4,444
  11. 2020Deformable DETR: Deformable Transformers for End-to-End Object Detectioncited by 3,422
  12. 2018Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentationcited by 3,223
22 more
  1. 2015SSD: Single Shot MultiBox Detectorcited by 3,130
  2. 2019Scalability in Perception for Autonomous Driving: Waymo Open Datasetcited by 2,451
  3. 2019MMDetection: Open MMLab Detection Toolbox and Benchmarkcited by 1,385
  4. 2018BDD100K: A Diverse Driving Dataset for Heterogeneous Multitask Learningcited by 1,288
  5. 2017Cascade R-CNN: Delving into High Quality Object Detectioncited by 1,276
  6. 2019Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detectioncited by 564
  7. 2020Sparse R-CNN: End-to-End Object Detection with Learnable Proposalscited by 495
  8. 2021Unbiased Teacher for Semi-Supervised Object Detectioncited by 306
  9. 2021Towards Open World Object Detectioncited by 297
  10. 2020Frustratingly Simple Few-Shot Object Detectioncited by 282
  11. 2020A Simple Semi-Supervised Learning Framework for Object Detectioncited by 282
  12. 2021One Million Scenes for Autonomous Driving: ONCE Datasetcited by 218
  13. 2019Detecting the Unexpected via Image Resynthesiscited by 137
  14. 2019The Fishyscapes Benchmark: Measuring Blind Spots in Semantic Segmentationcited by 122
  15. 2016Lost and Found: Detecting Small Road Hazards for Self-Driving Vehiclescited by 117
  16. 2021DeFRCN: Decoupled Faster R-CNN for Few-Shot Object Detectioncited by 106
  17. 2020Synthesize then Compare: Detecting Failures and Anomalies for Semantic Segmentationcited by 96
  18. 2021OoD-Bench: Quantifying and Understanding Two Dimensions of Out-of-Distribution Generalizationcited by 86
  19. 2021Effective Sparsification of Neural Networks with Global Sparsity Constraintcited by 45
  20. 2021SODA10M: A Large-Scale 2D Self/Semi-Supervised Object Detection Dataset for Autonomous Drivingcited by 43
  21. 2021MultiSiam: Self-supervised Multi-instance Siamese Representation Learning for Autonomous Drivingcited by 41
  22. 2021Corner Cases for Visual Perception in Automated Driving: Some Guidance on Detection Approachescited by 27

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2022: 2; 2023: 24; 2024: 33; 2025: 25; 2026: 16.

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  1. 2024Vista: A Generalizable Driving World Model with High Fidelity and Versatile Controllabilitycited by 347
  2. 2024MME-RealWorld: Could Your Multimodal LLM Challenge High-Resolution Real-World Scenarios that are Difficult for Humans?cited by 292
  3. 2023MagicDrive: Street View Generation with Diverse 3D Geometry Controlcited by 252
  4. 2024Eyes Closed, Safety On: Protecting Multimodal LLMs via Image-to-Text Transformationcited by 124
  5. 2023Benchmarking Robustness of 3D Object Detection to Common Corruptions in Autonomous Drivingcited by 114
  6. 2023Mixture of Cluster-conditional LoRA Experts for Vision-language Instruction Tuningcited by 106
  7. 2023DetCLIPv2: Scalable Open-Vocabulary Object Detection Pre-training via Word-Region Alignmentcited by 90
  8. 2025Cosmos-Drive-Dreams: Scalable Synthetic Driving Data Generation with World Foundation Modelscited by 84
  9. 2023On the Road with GPT-4V(ision): Early Explorations of Visual-Language Model on Autonomous Drivingcited by 80
  10. 2024Automated Evaluation of Large Vision-Language Models on Self-driving Corner Casescited by 77
  11. 2024A Survey on Autonomous Driving Datasets: Statistics, Annotation Quality, and a Future Outlookcited by 66
  12. 2023GeoDiffusion: Text-Prompted Geometric Control for Object Detection Data Generationcited by 56
38 more
  1. 2024EMOVA: Empowering Language Models to See, Hear and Speak with Vivid Emotionscited by 45
  2. 20243DRealCar: An In-the-wild RGB-D Car Dataset with 360-degree Viewscited by 39
  3. 2023Mixed Autoencoder for Self-supervised Visual Representation Learningcited by 38
  4. 2025Foundation Models in Autonomous Driving: A Survey on Scenario Generation and Scenario Analysiscited by 38
  5. 2023Gaining Wisdom from Setbacks: Aligning Large Language Models via Mistake Analysiscited by 35
  6. 2024DetDiffusion: Synergizing Generative and Perceptive Models for Enhanced Data Generation and Perceptioncited by 32
  7. 2023Implicit Concept Removal of Diffusion Modelscited by 23
  8. 2024Delving into Multi-modal Multi-task Foundation Models for Road Scene Understanding: From Learning Paradigm Perspectivescited by 23
  9. 2024ROADWork: A Dataset and Benchmark for Learning to Recognize, Observe, Analyze and Drive Through Work Zonescited by 22
  10. 2024Can LVLMs Obtain a Driver's License? A Benchmark Towards Reliable AGI for Autonomous Drivingcited by 22
  11. 2025Fine-Grained Evaluation of Large Vision-Language Models in Autonomous Drivingcited by 19
  12. 2023Perception Datasets for Anomaly Detection in Autonomous Driving: A Surveycited by 16
  13. 2024Task-customized Masked AutoEncoder via Mixture of Cluster-conditional Expertscited by 16
  14. 2024Training-Free Open-Ended Object Detection and Segmentation via Attention as Promptscited by 16
  15. 2023UGainS: Uncertainty Guided Anomaly Instance Segmentationcited by 15
  16. 2023Make Me a BNN: A Simple Strategy for Estimating Bayesian Uncertainty from Pre-trained Modelscited by 15
  17. 2024doScenes: An Autonomous Driving Dataset with Natural Language Instruction for Human Interaction and Vision-Language Navigationcited by 15
  18. 2023A Survey on Approximate Edge AI for Energy Efficient Autonomous Driving Servicescited by 14
  19. 2024AnoVox: A Benchmark for Multimodal Anomaly Detection in Autonomous Drivingcited by 14
  20. 2024SyntheOcc: Synthesize Geometric-Controlled Street View Images through 3D Semantic MPIscited by 14
  21. 2025Rethinking Driving World Model as Synthetic Data Generator for Perception Taskscited by 14
  22. 2023Learning to Name Classes for Vision and Language Modelscited by 12
  23. 2023Towards Distribution-Agnostic Generalized Category Discoverycited by 12
  24. 2025CoGen: 3D Consistent Video Generation via Adaptive Conditioning for Autonomous Drivingcited by 12
  25. 2024Mixture of insighTful Experts (MoTE): The Synergy of Thought Chains and Expert Mixtures in Self-Alignmentcited by 10
  26. 2024Generating Out-Of-Distribution Scenarios Using Language Modelscited by 10
  27. 2025Spotting the Unexpected (STU): A 3D LiDAR Dataset for Anomaly Segmentation in Autonomous Drivingcited by 10
  28. 2024Anything in Any Scene: Photorealistic Video Object Insertioncited by 9
  29. 2024OpenAD: Open-World Autonomous Driving Benchmark for 3D Object Detectioncited by 9
  30. 2024LOGen: Toward Lidar Object Generation by Point Diffusioncited by 9
  31. 2024OoDIS: Anomaly Instance Segmentation and Detection Benchmarkcited by 8
  32. 2024RAC3: Retrieval-Augmented Corner Case Comprehension for Autonomous Driving with Vision-Language Modelscited by 8
  33. 2023Adjusting Logit in Gaussian Form for Long-Tailed Visual Recognitioncited by 7
  34. 2023Prospective Role of Foundation Models in Advancing Autonomous Vehiclescited by 7
  35. 2024Unified Triplet-Level Hallucination Evaluation for Large Vision-Language Modelscited by 7
  36. 2025VL-SAM-V2: Open-World Object Detection with General and Specific Query Fusioncited by 6
  37. 2026SVCBench: A Streaming Video Counting Benchmark for Spatial-Temporal State Maintenancecited by 6
  38. 2026OVO-S-Bench: A Hierarchical Benchmark for Streaming Spatial Intelligence in Multimodal LLMscited by 6

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