Exploring the MOTOR Two-Wheeler Rider Dataset in FiftyOne

MOTOR, a dataset created by researchers at IIIT Hyderabad's CVIT lab, addresses the gap in driving datasets by focusing on motorcycles and scooters, which dominate transportation in many regions like India and the Global South. Unlike traditional datasets centered on four-wheeled vehicles, MOTOR encompasses 1,629 annotated maneuver sequences from 16 riders, captured over four weeks of real Indian traffic. This dataset includes synchronized views from front, helmet, rear, and eye-tracker cameras, along with GPS routes, gyroscope data, and rider gaze heatmaps. A subset of this dataset, comprising 324 clips, is available in a grouped-dataset format on Hugging Face, specifically designed for easy exploration using FiftyOne. The dataset supports multimodal analysis by integrating video, gaze, and telemetry data, which is crucial for developing advanced driver-assistance systems for two-wheelers and enhancing safety research. The FiftyOne platform facilitates a comprehensive view of the dataset, although it has some constraints, such as handling GeoLocation fields. The MOTOR dataset is significant for understanding rider behavior, legality, and attention modeling, providing a valuable resource for research and development in autonomous systems and safety applications.

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