Why GPUs May Be the Engine That Could Lead Us to Autonomous Trains

Apr 10, 2018 at 09:00 am by Press Release

Trains could be first out of the station to earn bragging rights as a fully autonomous form of transportation.

That’s because — unlike passenger vehicles or long-haul trucks — trains move on tracks, making them easier to control.

Autonomous trains were a hot topic at last month’s GPU Technology Conference last week, with speakers at three sessions spotlighting the impact of NVIDIA GPUs.

Train systems are built on decades-old technologies — from hardware-based signal systems to radio frequency-driven dispatchers.

Now the professionals who make the trains run on time are shifting to new tools, including the internet, sophisticated sensors and, increasingly, deep learning.

That’s a big change for an industry that was a hallmark of the original industrial revolution.

Nvidia Store

‘Future of Smart Trains’

“We’re going into the future of smart trains and dumb tracks. Technology is changing from track technology to in-train technology,” said Derel Wust, managing director at 4TEL Pty Ltd, an Australian private company working on a deep learning pilot program in New South Wales.

An example of the high costs for older train technology is playing out in New York’s multibillion-dollar MTA communications-based train control subway project, Wust said. Governor Andrew Cuomo said in a speech that at this rate it will take 40 years to upgrade the entire system.

Another report estimated costs of about $20 billion over a 35-year period.  Wust said that a train-based AI solution would minimize a big portion of that expense and lost time.

4TEL is focused on improved safety, reduced infrastructure and continuous machine learning for train systems. Many see the role played by train operators improved by automated acceleration and braking systems. “All the accidents we see in our daily lives can usually be attributed to human error,” Wust said.

Working with John Holland, an engineering company, 4TEL has a contract that provides the opportunity to bring its new machine learning-based approach to trains on the Country Regional Network of New South Wales. At this stage Wust wants to collect data and see how it works with behavioral models.

The company has a pilot program  in New South Wales for its Horus box, which uses infrared and optical cameras and machine learning algorithms to provide a software service for train operation. The box runs on the NVIDIA DRIVE platform.

Tracking Simulations

Wust’s company is among a handful of railway firms pioneering autonomous trains using so-called digital twin models, which allow simulations on tracks that mirror their networks.

Swiss Federal Railway Company, which manages nearly 5,000 miles of railways in a country that’s 200 miles wide, was at GTC to talk about its efforts to update train systems to keep them running on time.

It maintains a complex system that comes from a combination of high-speed trains and slow-moving freight trains, requiring some 13,000 switches to help control its mixed train traffic.

Known as SBB, the company shuttles more than 1.2 million passengers a day. It operates its own power plants that supplies its trains almost exclusively with renewable energies and is one of Switzerland’s largest real estate companies. “What might surprise you is that we are also a big software company,” said Erik Nygren, a business analysis and AI researcher at the company.

The company’s Research and Innovation Platform, led by Dirk Abels, uses NVIDIA DGX-1 for simulations as well as for deep reinforcement learning to optimize train schedules and dispatching.

SBB has integrated all of its geographic information into its simulation environment, allowing for interactions with real-time train data by train dispatchers in the virtual setting.

Also, it has developed safety features that can give locomotives automatic braking systems. “We have collision detection, and it does it within a half second,” said Adrian Egli, a business analyst and HPC expert for SBB.

Maintenance Tracking

Like 4TEL, General Electric is working on in-train box systems that pack cameras, software and GPU technology for guiding trains. GE has a contract with Indian Railways for a locomotive in Bangalore, India, to pilot its technology.

The company sells the equipment as well its subscription-based camera system that includes analytics. GE uses the NVIDIA DGX-1 as well as the NVIDIA TITAN X GPU for training.

Inspections can be an ordeal that shuts down segments of tracks while people walk them looking for faulty railroad ties and other maintenance issues.

GE is working on using its system of front-facing cameras for train safety as well as track inspection.  “Can we make the locomotive smart enough and self-aware to start inspecting its own track?” said Dattaraj Rao, a principal architect at General Electric, who also spoke at GTC.

4TEL’s Wust said it’s pretty easy to imagine that trains gathering data via onboard cameras and sensors could collect data from opposing tracks, such as a rail obstruction. This could provide Waze-like traffic information onto the railway system for other trains.

“We started investigating these images to try to extract value from them,” Rao said.

The post AI Think I Can: Why GPUs May Be the Engine That Could Lead Us to Autonomous Trains appeared first on The Official NVIDIA Blog.


Sign up for our newsletter

This website uses cookies to ensure you get the best experience possible More Info
Got it!