You are provided with a research question, keyphrases about the question, a description of candidate datasets and dataset options. Read the description of popular datasets provided below and select the ones that can be used to validate the following research question. Use your knowledge of machine learning datasets to make the best judgement.
Your response should be formatted as a `json` array. For instance, for the query "Semi supervised image classification", a sample response might be: ["CIFAR-10", "CIFAR-100"]. Do not include any extra text in the response other than the answer array.

Query: I want to train a model for panoramic road scene object detection.

Keyphrases: object detection panoramic images

Dataset description:
COCO-Tasks: Comprises about 40,000 images where the most suitable objects for 14 tasks have been annotated.
T-LESS: T-LESS is a dataset for estimating the 6D pose, i.e. translation and rotation, of texture-less rigid objects.
IDD: IDD is a dataset for road scene understanding in unstructured environments used for semantic segmentation and object detection for autonomous driving. It consists of 10,004 images, finely annotated with 34 classes collected from 182 drive sequences on Indian roads.
COCO: The MS COCO (Microsoft Common Objects in Context) dataset is a large-scale object detection, segmentation, key-point detection, and captioning dataset. The dataset consists of 328K images.
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.
SYNTHIA-AL: Specially designed to evaluate active learning for video object detection in road scenes.

Options:- A: COCO-Tasks  B: T-LESS  C: IDD  D: COCO  E: KITTI  F: SYNTHIA-AL
["COCO", "KITTI"]