Autonomous vehicle (AV) initiatives have been grabbing headlines for decades. 

Due to the complex and frequently changing nature of real-world environments, AV computer vision models must be trained with vast datasets to reach and maintain safe levels of efficacy. 

In addition to driverless cars, unmanned aerial vehicles (UAV), commonly known as drones, are being employed in a huge range of novel scenarios, including in agriculture and geo-analysis. 

New Generations of AVs

Engineering and training AVs has been a momentous task, especially when it comes to placing the public's safety into the trust of computers. 

New generations of AVs are finally proving their safety credentials, providing a roadmap toward AV implementation. In addition, AVs are vital to attaining climate change goals, seeing as they’re almost solely electric, and there are hopes that they’ll also be much lighter and more maneuverable than combustion vehicles in metropolitan environments.

Today, it’s crucial that AI teams continue to tackle the ongoing challenges of training demonstrably safe AVs. 

This includes covering edge cases, such as the recent rise of eScooters on the roads. Aya Data has proven experience in working within AV training pipelines - we helped an international client cover an unaddressed eScooter edge case in their AV training workflow. View the case study here.

Data Labeling for AVs and UAVs 

Training AVs requires a blend of supervised, unsupervised, and reinforcement learning, with many interleaved and overlapping processes working together in ensemble. 

The supervised component of AV training demands highly complex datasets that cover numerous iterations of real-world environments. While synthetic data is promising here, high-quality labeled datasets are still instrumental in training and optimizing AVs of all kinds. 

Aya Data labels data sets for AV companies to enable autonomous vehicles to better understand their environment, whether interpreting spatial data like LIDAR or covering off edge cases like e-scooters.

Typical Services: Polygon, Bounding Box, Semantic Segmentation, Key Points

Domain Expertise: Autonomous Vehicles