CODA: A Real-World Road Corner Case Dataset for Object Detection in Autonomous Driving. (arXiv:2203.07724v3 [cs.CV] UPDATED)

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 …

arxiv autonomous autonomous driving case dataset detection driving