SiaSearch says it will provide enhanced access to its ‘nuScenes’ dataset for the self-driving research community. The partnership will enable nuScenes users to access, explore, and understand the data ‘in an unprecedented way’.
SiaSearch has developed a platform that automatically extracts granular metadata from raw multimodal automated driving sensor data. It then makes the data fully searchable and accessible for evaluation through its GUI and API.
With a catalog of over 50 different driving events and attributes, users can ask any questions they have about the raw data and get answers ‘within seconds’ using the Sia Interval Query Engine.
nuScenes is a dataset that teaches autonomous vehicles how to safely engage with ever-changing road environments. It’s described as a highly sophisticated and diverse dataset, comprising a full sensor suite with data from two distinct cities: Boston and Singapore, and manual annotations for 23 object classes.
SiaSearch points out that finding edge cases and interesting interactions within a large scale dataset, such as nuScenes, is ‘like finding a needle in a haystack’. By making nuScenes available through SiaSearch however, it is claimed the dataset is even more powerful, as it ‘becomes fully accessible and the process of selecting the relevant driving sequences is streamlined’.
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SiaSearch automatically tags nuScenes with driving maneuvers, interactions with other traffic agents, and infrastructural and environmental attributes. With the tagging, researchers can use nuScenes to find any driving scenario they need within seconds, the company says. This allows researchers to focus on their job’s essence, training models, instead of spending precious time on manually reviewing the dataset.