Challenges in Real-Time Models for Autonomous Vehicles

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Exploring the difficulties of deploying real-time models in self-driving cars compared to cloud-based models, focusing on training, hardware constraints, latency optimization, and efficient resource utilization. Discussion includes adapting cutting-edge techniques to fit constrained environments and the complexity of decision-making in autonomous vehicles based on anticipatory analysis.

Autonomous vehicle engineering is a huge challenge and requires the integration of many different technologies. A self-driving car needs data from multiple sensors, ML models to process that data, engineering to couple software and mechanical systems, and much more.

Ian Williams is a Senior Staff Software Engineer at Cruise, and before that worked at Google, Lyft, and eBay. He joins the show to talk about the diverse engineering challenges and strategies associated with building self-driving cars.

This episode is hosted by Tyson Kunovsky. Tyson is the co-founder and CEO of AutoCloud, an infrastructure as code platform. He is originally from South Africa, and has a background in software engineering and cloud development. When he’s not busy designing new GitOps workflows, he enjoys skiing, riding motorcycles, and reading sci-fi books. Check the show notes for more information on Tyson’s work, and where to find him.

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