Tesla: Artificial Intelligence in Autonomous Driving
Former President, Level 5 at Lyft and Woven Planet (Toyota)
inpractise.com/articles/tesla-artificial-intelligence-in-autonomous-driving
Former President, Level 5 at Lyft and Woven Planet (Toyota)
The expert has more than 15 years of experience in the field of autonomous driving at leading ridesharing and other large tech companies. He founded and led several self-driving divisions within larger groups, all the way through their successful exits. He spent over a decade at Google, building its geo imagery department and has a strong background in machine learning, AI, autonomous driving, computer vision, mapping, and geospatial imagery.
Interview Transcript
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One question was about the impact of recent advances in AI on the development of autonomous vehicles. Could we dive into that and perhaps explain, as if to a 10-year-old, how AI works in autonomous vehicles?
The challenge for AI is to take all that sensor information and decide what to do next, such as accelerating or slowing down and turning the steering wheel left or right. Essentially, the AI for a self-driving car is a contextual engine that takes context, in the form of sensor data, and outputs velocity and steering angle.
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You mentioned something about things being at the edge. Could you explain how that is related to latency?
One of the biggest challenges for AI in AVs is their data hunger. Recent advances in AI, such as large language models like ChatGPT, are trained on gargantuan amounts of data. This raises the question of whether it's possible to ever gather enough data for AV systems.
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Do you think recent advances could help in finding and recognizing these edge cases?
Data collection is key in the space of AV, but you can't just browse the web for this data; you have to create it yourself.
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