Smart diagnostics, advanced validation help support the reliability metrics required to gain confidence that autonomous trucks are ready for the road.
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. 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.
“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. The startup uses “active learning” techniques to identify the most relevant detections and provide the most useful insights into critical edge cases.