Ford has announced a $1 billion investment over the next five years in Argo AI, an artificial intelligence company, to develop a virtual driver system for the automaker’s autonomous vehicle coming in 2021.
Ford Motor Company has announced it is investing $1 billion during the next five years in Argo AI, an artificial intelligence company, to develop a virtual driver system for the automaker’s autonomous vehicle coming in 2021 – and for potential license to other companies.
Founded by former Google and Uber leaders, Argo AI is bringing together some of the most experienced roboticists and engineers working in autonomy from inside and outside of Ford. The team of experts in robotics and artificial intelligence is led by Argo AI founders Bryan Salesky, company CEO, and Peter Rander, company COO. Both are alumni of Carnegie Mellon National Robotics Engineering Center and former leaders on the self-driving car teams of Google and Uber, respectively.
“The next decade will be defined by the automation of the automobile, and autonomous vehicles will have as significant an impact on society as Ford’s moving assembly line did 100 years ago,” said Ford President and CEO Mark Fields. “As Ford expands to be an auto and a mobility company, we believe that investing in Argo AI will create significant value for our shareholders by strengthening Ford’s leadership in bringing self-driving vehicles to market in the near term and by creating technology that could be licensed to others in the future.”
The current team developing Ford’s virtual driver system – the machine-learning software that acts as the brain of autonomous vehicles – will be combined with the robotics talent and expertise of Argo AI. This innovative partnership will work to deliver the virtual driver system for Ford’s SAE level 4 self-driving vehicles.
Ford will continue to lead on development of its purpose-built autonomous vehicle hardware platform, as well as on systems integration, manufacturing, exterior and interior design, and regulatory policy management.
Argo AI will join forces with Ford’s autonomous vehicle software development effort to strengthen the commercialization of self-driving vehicles. Argo AI’s agility and Ford’s scale uniquely combine the benefits of a technology startup with the experience and discipline of the automaker’s industry-leading autonomous vehicle development program.
“We are at an inflection point in using artificial intelligence in a wide range of applications, and the successful deployment of self-driving cars will fundamentally change how people and goods move,” said Salesky. “We are energized by Ford’s commitment and vision for the future of mobility, and we believe this partnership will enable self-driving cars to be commercialized and deployed at scale to extend affordable mobility to all.”
The collaboration supports Ford’s intent to have a fully autonomous, SAE level 4-capable vehicle for commercial application in mobility services in 2021.
“Working together with Argo AI gives Ford a distinct competitive advantage at the intersection of the automotive and technology industries,” said Raj Nair, Ford executive vice president, Global Product Development, and chief technical officer. “This open collaboration is unlike any other partnership – allowing us to benefit from combining the speed of a startup with Ford’s strengths in scaling technology, systems integration and vehicle design.”
Also complementing the relationship will be Ford Smart Mobility LLC, which will lead on the commercialization strategy for Ford’s self-driving vehicles. This includes choices for using autonomous vehicles to move goods and people, such as ride sharing, ride hailing or package delivery fleets.
Ford will be the majority stakeholder in Argo AI. Importantly, Argo AI has been structured to operate with substantial independence. Its employees will have significant equity participation in the company, enabling them to share in its success. Argo AI’s board will have five members: Nair; John Casesa, Ford group vice president, Global Strategy; Salesky; Rander; and an independent director.
The $1 billion investment in Argo AI will be made over five years and is consistent with the autonomous vehicle capital allocation plan shared last September as part of Ford Investor Day.
By the end of this year, Argo AI expects to have more than 200 team members, based in the company’s Pittsburgh headquarters and at major sites in Southeastern Michigan and the Bay Area of California.
Argo AI’s initial focus will be to support Ford’s autonomous vehicle development and production. In the future, Argo AI could license its technology to other companies and sectors looking for autonomous capability.
Project Bloodhound saved
The British project to break the world landspeed record at a site in the Northern Cape has been saved by a new backer, after it went into bankruptcy proceedings in October.
Two weeks ago, and two months after entering voluntary administration, the Bloodhound Programme Limited announced it was shutting down. This week it announced that its assets, including the Bloodhound Supersonic Car (SSC), had been acquired by an enthusiastic – and wealthy – supporter.
“We are absolutely delighted that on Monday 17th December, the business and assets were bought, allowing the Project to continue,” the team said in a statement.
“The acquisition was made by Yorkshire-based entrepreneur Ian Warhurst. Ian is a mechanical engineer by training, with a strong background in managing a highly successful business in the automotive engineering sector, so he will bring a lot of expertise to the Project.”
Warhurst and his family, says the team, have been enthusiastic Bloodhound supporters for many years, and this inspired his new involvement with the Project.
“I am delighted to have been able to safeguard the business and assets preventing the project breakup,” he said. “I know how important it is to inspire young people about science, technology, engineering and maths, and I want to ensure Bloodhound can continue doing that into the future.
“It’s clear how much this unique British project means to people and I have been overwhelmed by the messages of thanks I have received in the last few days.”
The record attempt was due to be made late next year at Hakskeen Pan in the Kalahari Desert, where retired pilot Andy Green planned to beat the 1228km/h land-speed record he set in the United States in 1997. The target is for Bloodhound to become the first car to reach 1000mph (1610km/h). A track 19km long and 500 metres wide has been prepared, with members of the local community hired to clear 16 000 tons of rock and stone to smooth the surface.
The team said in its announcement this week: “Although it has been a frustrating few months for Bloodhound, we are thrilled that Ian has saved Bloodhound SSC from closure for the country and the many supporters around the world who have been inspired by the Project. We now have a lot of planning to do for 2019 and beyond.”
Motor Racing meets Machine Learning
The futuristic car technology of tomorrow is being built today in both racing cars and
toys, writes ARTHUR GOLDSTUCK
The car of tomorrow, most of us imagine, is being built by the great automobile manufacturers of the world. More and more, however, we are seeing information technology companies joining the race to power the autonomous vehicle future.
Last year, chip-maker Intel paid $15.3-billion to acquire Israeli company Mobileye, a leader in computer vision for autonomous driving technology. Google’s autonomous taxi division, Waymo, has been valued at $45-billion.
Now there’s a new name to add to the roster of technology giants driving the future.
Amazon Web Services, the world’s biggest cloud computing service and a subsidiary of Amazon.com, last month unveiled a scale model autonomous racing car for developers to build new artificial intelligence applications. Almost in the same breath, at its annual re:Invent conference in Las Vegas, it showcased the work being done with machine learning in Formula 1 racing.
AWS DeepRacer is a 1/18th scale fully autonomous race car, designed to incorporate the features and behaviour of a full-sized vehicle. It boasts all-wheel drive, monster truck tires, an HD video camera, and on-board computing power. In short, everything a kid would want of a self-driving toy car.
But then, it also adds everything a developer would need to make the car autonomous in ways that, for now, can only be imagined. It uses a new form of machine learning (ML), the technology that allows computer systems to improve their functions progressively as they receive feedback from their activities. ML is at the heart of artificial intelligence (AI), and will be core to autonomous, self-driving vehicles.
AWS has taken ML a step further, with an approach called reinforcement learning. This allows for quicker development of ML models and applications, and DeepRacer is designed to allow developers to experiment with and hone their skill in this area. It is built on top of another AWS platform, called Amazon SageMaker, which enables developers and data scientists to build, train, and deploy machine learning quickly and easily.
Along with DeepRacer, AWS also announced the DeepRacer League, the world’s first global autonomous racing league, open to anyone who orders the scale model from AWS.
As if to prove that DeepRacer is not just a quirky entry into the world of motor racing, AWS also showcased the work it is doing with the Formula One Group. Ross Brawn, Formula 1’s managing director of Motor Sports, joined AWS CEO Andy Jassy during the keynote address at the re:Invent conference, to demonstrate how motor racing meets machine learning.
“More than a million data points a second are transmitted between car and team during a Formula 1 race,” he said. “From this data, we can make predictions about what we expect to happen in a wheel-to-wheel situation, overtaking advantage, and pit stop advantage. ML can help us apply a proper analysis of a situation, and also bring it to fans.
“Formula 1 is a complete team contest. If you look at a video of tyre-changing in a pit stop – it takes 1.6 seconds to change four wheels and tyres – blink and you will miss it. Imagine the training that goes into it? It’s also a contest of innovative minds.”
Formula 1 racing has more than 500 million global fans and generated $1.8 billion in revenue in 2017. As a result, there are massive demands on performance, analysis and information.
During a race, up to 120 sensors on each car generate up to 3GB of data and 1 500 data points – every second. It is impossible to analyse this data on the fly without an ML platform like Amazon SageMaker. It has a further advantage: the data scientists are able to incorporate 65 years of historical race data to compare performance, make predictions, and provide insights into the teams’ and drivers’ split-second decisions and strategies.
This means Formula 1 can pinpoint how a driver is performing and whether or not drivers have pushed themselves over the limit.
“By leveraging Amazon SageMaker and AWS’s machine-learning services, we are able to deliver these powerful insights and predictions to fans in real time,” said Pete Samara, director of innovation and digital technology at Formula 1.