(From https://www.nuscenes.org)

A guide to better access, explore, and understand unstructured sensor data for autonomous driving development

To accelerate the speed of autonomous vehicle adoption, an increasing number of organizations and individuals are making their projects available to the public. Open data is fueling commercial and technological advancement in autonomous driving — one of most well known resources being the nuScenes dataset.

Developed by the team at Motional (formerly nuTonomy), nuScenes is one of the most popular open-source datasets for autonomous driving. The nuScenes dataset enables researchers to study a wide range of urban driving situations using data captured by the full sensor suite of a self-driving car. …


Enabling flexible data selection with the SiaSearch API

When building machine learning (ML) models for highly complex automation tasks it is not only essential to identify the right models for the right job but also to select the suitable datasets for training, testing and validation. While dataset selection is typically easier for ML problems based on structured data, there are massive challenges to use data-driven technologies with unstructured data. Typical examples of such applications are robotics, automated driving and computer vision. While the content of structured data can be easily accessed and queried, dealing with unstructured data typically requires immense…


Applications in the field of computer vision are heavily dependent on high quality data. Data-driven development offers a huge potential, however the performance of the systems relies mostly on the data which is provided during training time. In order to ensure reliable system performance the right data needs to be selected for model training and testing.

The majority of machine learning applications rely on supervised approaches for model training. For some industries (e.g. e-commerce, online ads) the generated data is automatically labeled by the customer during usage. However, computer vision applications typically require a significant amount of manual work in…


The data challenge of intelligent vehicles

Transferring self-driving vehicles from a well-controlled research setting into the real world is a crucial step which in recent years has proven to be harder than originally expected (see, for example, the valuation cut of Waymo by Morgan Stanley due to developmental delays, or Cruise’s announcement about delays in bringing L4 to market).

Whether we are talking about automated highway driving or level 4–5 autonomy, an autonomous vehicle has to safely tackle a large variety of traffic situations, ranging from everyday situations to rare corner cases. In order to develop and test the perception and navigation algorithms of an autonomous…

Mark Pfeiffer

CTO at SiaSearch

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