Machine Learning for Autonomous Vehicles

Overview

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Explore the world of machine learning for autonomous vehicles in this insightful GOTO 2022 conference talk featuring Oscar Beijbom and Prayson Daniel. Delve into the data requirements for self-driving cars, security concerns, language choices, and model deployment strategies. Learn about real-world data collection methods, the daily challenges faced by software developers in the field, and the potential timeline for widespread adoption of autonomous vehicles. Gain valuable insights into the current state of the technology, ethical considerations, and the future of transportation as experts discuss the readiness of autonomous cars for public use and the possibility of their realization within the next five years.

Syllabus

Intro
What kickstarted the autonomous vehicles trend?
ML for self-driving vehicles
Gathering data for training ML for autonomous cars
Real-world data collection for ML
Are we ready for autonomous cars?
A day in the life of a SW dev. building autonomous cars
Language choices
When is a model good enough for production?
Security & autonomous cars: Can hackers take control?
Nyckel: A ML platform
Will self-driving cars become a reality in the next 5 years?
Outro