Accelerating Uber's Self-Driving Vehicle Development with Data

eng.uber.com

A key challenge faced by self-driving vehicles comes during interactions with pedestrians. (...) Through data, we can learn the movement of cars and pedestrians in a city, and train our self-driving vehicles how to drive. We map pedestrian movement in cities with LiDAR-equipped cars(...)

This is super-cool. It's been clear since the very earliest days of self-driving that cars would be massive IOT endpoints, but it's fascinating to watch that play out. Uber's self-driving division isn't just analyzing roads via its LiDAR-equipped cars, it's analyzing the people walking on those roads.

To anyone even slightly familiar with deep learning it's easy to see how doable this would be, but it's also fascinating to take a step back and think about how mobile, internet-connected, autonomous endpoints are actually mapping us. Cue Keanu: whoah.

The article presents a fascinating (if high-level) overview of what it looks like to use this type of data in practice. If this isn't what your day job looks like today, it might be in the future.

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