Autonomous vehicle (AV) stacks are evolving from a hierarchy of discrete building blocks to end-to-end architectures built on foundation models. This transition demands an AV data flywheel to generate synthetic data and augment sensor datasets, address coverage gaps and, and ultimately, build a validation toolchain to safely develop and deploy autonomous vehicles. In this blog post…
]]>Autonomous vehicles (AV) come in all shapes and sizes, ranging from small passenger cars to multi-axle semi-trucks. However, a perception algorithm deployed on these vehicles must be trained to handle similar situations, like avoiding an obstacle or a pedestrian. The datasets used to develop and validate these algorithms are typically collected by one type of vehicle— for example sedans…
]]>Detecting far-field objects, such as vehicles that are more than 100 m away, is fundamental for automated driving systems to maneuver safely while operating on highways. In such high-speed environments, every second counts. Thus, if the perception range of an autonomous vehicle (AV) can be increased from 100 m to 200 m while traveling at 70 mph, the vehicle has significantly more time to…
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