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Emirates Multi-Task (EMT) is a comprehensive dataset for autonomous driving research, containing 57 minutes of diverse urban traffic footage from the Gulf Region. The dataset provides rich semantic annotations across two agent categories: people (pedestrians and cyclists), vehicles (seven classes). Each video segment spans 2.5-3 minutes, capturing challenging real-world scenarios:
- Dense Urban Traffic: Complex multi-agent interactions in congested scenarios
- Weather Variations: Clear and rainy conditions
- Visual Challenges: High reflections from road surfaces and adverse weather combinations (rainy nights)
More details can be found in the download page and paper.
O2DTD is the first dataset on desert freespace detection, collected with six different light conditions (dawn, morning, afternoon, sunset, twilight, and night), containing a total of 5,045 RGB images.
The dataset includes around twenty minutes of unlabeled data (A set of 8 LiDARs, Monochrome Cameras, IMU, GPS) captured from an Autonomous Shuttle that is deployed and operated in Khalifa University, SAN Campus, UAE.
