Protocol asked ten autonomous vehicle executives, “What do people most often get wrong in discussions about autonomous vehicles?” Cruise’s SVP of Engineering, Mo Elshenaway, answered:
“Self-driving is an all-encompassing AI and engineering challenge. It’s easy to see an AV on the streets and think only about the AI models that power them or the compute and sensor suites built as part of it, but there is a virtual software assembly line built alongside the car itself that enables us to meet the unique scale and safety imperative at play here.
To enable AVs to drive superiorly in any given scenario, and continuously evolve and adapt new paradigms, it requires an ecosystem capable of ingesting petabytes of data and hundreds of years worth of compute every day, training and testing models on a continuous loop for multiple times a week software updates that improve performance and ensure safety. The complex network of new tools, testing infrastructure and development platforms that are behind every seamless handling of a construction zone or double-parked car are themselves significant engineering achievements that stand to have an outsized impact beyond AV as they push the boundaries of ML, robotics and more.”
This was probably the biggest surprise upon joining Cruise, which is embarrassing to admit. Cruise has invested tremendously in developing an entire AV software infrastructure that supports the core AV stack. There are front-end engineers working on visualization tools for machine learning scientists, and site reliability engineers ensuring the performance of cloud services. It’s a little bit like an iceberg, 90% of the activity is below the surface of what we might think of as “core AV engineering.”
The rest of the answers in the article are great, too, including Sterling Anderson (Aurora), Jesse Levinson (Zoox), and Raquel Urtasun (Waabi).
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