Inside Waymo's Secret World for Training Self-Driving Cars

An exclusive look at how Alphabet understands its most ambitious artificial intelligence project

In a corner of Alphabet’s campus, there is a team working on a piece of software that may be the key to self-driving cars. No journalist has ever seen it in action until now. They call it Carcraft, after the popular game World of Warcraft.

The software’s creator, a shaggy-haired, baby-faced young engineer named James Stout, is sitting next to me in the headphones-on quiet of the open-plan office. On the screen is a virtual representation of a roundabout. To human eyes, it is not much to look at: a simple line drawing rendered onto a road-textured background. We see a self-driving Chrysler Pacifica at medium resolution and a simple wireframe box indicating the presence of another vehicle.

Months ago, a self-driving car team encountered a roundabout like this in Texas. The speed and complexity of the situation flummoxed the car, so they decided to build a look-alike strip of physical pavement at a test facility. And what I’m looking at is the third step in the learning process: the digitization of the real-world driving. Here, a single real-world driving maneuver—like one car cutting off the other on a roundabout—can be amplified into thousands of simulated scenarios that probe the edges of the car’s capabilities.

Veröffentlichung:
01. September 2017

Auto-mat ist eine Initiative von

TCS

Das Portal wird realisiert von

Mobilitätsakademie
 

in kooperation mit

Swiss eMobility

veranstaltungspartner

Schweizer Mobilitätsarena
 
 
 
Datenschutzhinweis
Diese Webseite nutzt externe Komponenten, welche dazu genutzt werden können, Daten über Ihr Verhalten zu sammeln. Lesen Sie dazu mehr in unseren Datenschutzinformationen.
Notwendige Cookies werden immer geladen