Tom tasky fev8/26/2023 ![]() “Once you start developing and looking at the data, you see it might be poor quality and how much additional software effort is required,” Tasky said. The amount of time and effort involved in this part of the process can be underestimated, he said. The supplier has a patented preprocessing and analytics solution that handles up to 40 TB of data per vehicle per day in an 元 Pilot project taking place in Europe. The level of preprocessing required to make sure the raw data is useful for machine learning is a key challenge, said Tom Tasky, director of Intelligent Mobility at FEV North America. “We can actually sample our data using this technology to build high-quality datasets that are lower in volume but higher in quality to get more performance out of our systems.” “You can think of active learning as a way for us to understand the ways in which our machine-learning models are failing,” Moak explained. ![]() The startup uses “active learning” techniques to identify the most relevant detections and provide the most useful insights into critical edge cases. “When our trucks drive on the road, they’re collecting terabytes upon terabytes of data, and we need to get that up into the cloud and into the hands of our engineers, ultimately,” said Brandon Moak, co-founder and CTO of autonomous technology developer Embark, whose recently-launched prototype SAE Level 4 tractor is shown above. Companies are working to overcome data challenges to ensure their ML algorithms can produce the AI required to achieve widespread SAE Level 4 and 5 operations. Transforming raw data into high-quality structured data is a critical path to properly fueling machine-learning (ML) models and deploying artificial-intelligence (AI) applications across autonomous fleets. ![]() Once you start developing and looking at the data, you see it might be poor quality and how much additional software effort is required.”
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