Given a short query with keyphrases about a research idea in 2020 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: This paper proposes a strategy for visual prediction in the context of autonomous driving. The idea is to use a hierarchical

Keyphrases: visual prediction images autonomous driving

Dataset description:
SYNTHIA: The SYNTHIA dataset is a synthetic dataset that consists of 9400 multi-viewpoint photo-realistic frames rendered from a virtual city and comes with pixel-level semantic annotations for 13 classes. Each frame has resolution of 1280 × 960.
A2D2: Audi Autonomous Driving Dataset (A2D2) consists of simultaneously recorded images and 3D point clouds, together with 3D bounding boxes, semantic segmentation, instance segmentation, and data extracted from the automotive bus.
BLVD: BLVD is a large scale 5D semantics dataset collected by the Visual Cognitive Computing and Intelligent Vehicles Lab. This dataset contains 654 high-resolution video clips owing 120k frames extracted from Changshu, Jiangsu Province, China, where the Intelligent Vehicle Proving Center of China (IVPCC) is located.
Lyft Level 5 Prediction: A self-driving dataset for motion prediction, containing over 1,000 hours of data. This was collected by a fleet of 20 autonomous vehicles along a fixed route in Palo Alto, California, over a four-month period.

Options:- A: SYNTHIA  B: A2D2  C: BLVD  D: Lyft Level 5 Prediction
SYNTHIA