Given a short query with keyphrases about a research idea in 2017 in the domain of artificial intelligence, recommend the relevant datasets to validate the research idea from options provided. A short description of the datasets is also provided to help you.

Query: I want to develop a novel system to automatically find the mistakes made by an object detector without ground truth labels.

Keyphrases: object detection images autonomous driving

Dataset description:
DOTA: DOTA is a large-scale dataset for object detection in aerial images. It can be used to develop and evaluate object detectors in aerial images.
AtariARI: The AtariARI (Atari Annotated RAM Interface) is an environment for representation learning. The Atari Arcade Learning Environment (ALE) does not explicitly expose any ground truth state information.
SNIPS: The SNIPS Natural Language Understanding benchmark is a dataset of over 16,000 crowdsourced queries distributed among 7 user intents of various complexity:   SearchCreativeWork (e.g. Find me the I, Robot television show), GetWeather (e.g.
KITTI: KITTI (Karlsruhe Institute of Technology and Toyota Technological Institute) is one of the most popular datasets for use in mobile robotics and autonomous driving. It consists of hours of traffic scenarios recorded with a variety of sensor modalities, including high-resolution RGB, grayscale stereo cameras, and a 3D laser scanner.

Options:- A: DOTA  B: AtariARI  C: SNIPS  D: KITTI
KITTI