WEpod Becomes First Driverless Car to Play in Traffic

A system of automated electric vehicles, known as WEpods, just made history by becoming the first self-driving shuttles to take to public roads. They’re the first vehicles in the world without a steering wheel to be given license plates.

Unlike other forms of automated transport, these cheery little six-passenger vehicles don’t travel in special lanes, and they’re not guided by rails, magnets or wires.

Instead, they’re steered through traffic by a complex set of systems, including several NVIDIA-powered brains, between the towns of Wageningen and Ede in the central Dutch province of Gelderland.

To summon a WEpod, passengers just tap on an app on their smartphone.

Hitting the Road with Deep Learning

The story behind this first: a new kind of technology — called deep learning — that lets computers teach themselves about the world through a training process that is widely adopted for vision-based systems.

text
The WEpods are steered with the help of several NVIDIA-powered brains through the Dutch province of Gelderland.

Deep learning has already given computers the ability to surpass human capabilities on a number of tasks. And it’s critical for autonomous vehicles, where it’s just not possible to hand-code for every possible situation a self-driving car might encounter. Especially with regards to interpreting the objects surrounding the vehicle.

No wonder, then, that the WEpod team at the Delft University of Technology — along with auto manufacturers such Audi, BMW, Ford and Mercedes — have turned to deep learning on NVIDIA GPUs.

Data Driven

The result is a vehicle that’s able to build a complete picture of the environment around it as it travels through traffic.

Each WEpod continuously assesses its environment and options at high rates, resulting in a dynamic system able to deal with real-world situations of mixed traffic quickly, reliably and safely.

“This is a massive computing challenge,” said Dimitrios Kotiadis, senior researcher from TU Delft.

A GPU-Powered Supercomputer on Wheels

GPUs have been key in meeting this challenge. Unlike CPUs, which sprint through a handful of computing tasks at a time, GPU are built to work on thousands of computing tasks at once.

text
Their parallel architecture makes our GPUs ideal for many kinds of deep learning tasks.

This parallel architecture — coupled with our software tools — make GPUs ideal for many kinds of deep learning tasks (see “Accelerating AI with GPUs: A New Computing Model”). And it was key to accelerating the training and deployment of WEPod’s autonomous vehicles.

“NVIDIA technology plays a crucial role in enabling us to meet our computational requirements,” Kotiadis said. “Each WEpod is in many ways a supercomputer on wheels.”

Summoned by a Smartphone

The result is a new kind of public transport concept that offers the convenience of a personal vehicle, without the hassles of car ownership.

Although the vehicles are running on a fixed route for now, the WEpod team hopes other cities will adopt WEpod technology once the trials are complete. The system will start operations  in May.

“Autonomous, on-demand transit systems like WEpod have the potential to revolutionize our cities,” said WEpod Project Manager Jan Willem van der Wiel.

We’re glad to be along for the ride.

Similar Stories