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Autonomous Driving: Synthetic Data versus Real Data

Autonomous Driving: Synthetic Data versus Real Data

Advanced Driver Assistance Systems (ADAS) and Autonomous Driving (AD) have become a hot topic in the automotive industry, with many companies using their highly automated driving functions as a key differentiator to their competition. Most of these functions rely on machine …

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Workshop for the AVEAS Project. Latest developments and key results

Participants of the AVEAS Project workshop discussing the latest developments and key results in autonomous driving technologies.

We are thrilled to share the latest developments of the AVEAS Project with you. This joint project is funded by the Federal Ministry of Economics and Climate Protection (BMWK) with understand.ai as the consortium leader. The AVEAS Project aims to revolutionize the way we collect …

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Enabling ADAS Validation Scale with Automated Annotation. A case study

Graphical representation of the automated annotation system used for scaling ADAS validation, as featured in understand.ai's case study.

Bringing a new ADAS/ AD system on the road is an expensive and often complex undertaking. Validating an autonomous driving function is a particularly large project. It requires huge amounts of perception data to be labeled at high quality. One of the biggest budget and time …

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Significance of Ground Truth Labels in Machine Learning

Significance of Ground Truth Labels in Machine Learning

· 3 minutes read