Abstract
Contemporary deep-learning object detection methods for autonomous driving usually presume fixed 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 is available at https://coda-dataset.github.io.
K. Li, K. Chen and H. Wang—Equal contribution.
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Notes
- 1.
We adopt the definition of object-level corner case proposed in [3].
- 2.
KITTI are captured in a mid-size city of Germany, nuScenes are captured in Singapore, and ONCE are captured in various cities of China.
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Acknowledgement
We gratefully acknowledge the support of MindSpore, CANN (Compute Architecture for Neural Networks) and Ascend AI Processor used for this research.
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Li, K. et al. (2022). CODA: A Real-World Road Corner Case Dataset for Object Detection in Autonomous Driving. In: Avidan, S., Brostow, G., Cissé, M., Farinella, G.M., Hassner, T. (eds) Computer Vision – ECCV 2022. ECCV 2022. Lecture Notes in Computer Science, vol 13698. Springer, Cham. https://doi.org/10.1007/978-3-031-19839-7_24
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