Ford has acquired Chariot, a crowd-sourced shuttle service, and is collaborating with bike-sharing provider Motivate to expand its transportation solutions in city centers.
The company is also establishing a new City Solutions team to work with cities around the world on their transportation needs.
“We’re expanding our business to be both an auto and a mobility company, and partnering with cities on current and future transportation needs is the next major step,” said Mark Fields, Ford president and CEO. “For more than 100 years, Ford has been part of the community and the trusted source for automotive transportation. Now, we want to work with communities to offer even more transportation choices and solutions for people – for decades to come.”
Today, half the world’s population lives in cities. By 2030, that number is expected to grow to 60 percent. As city populations grow, the challenges tied to moving people and goods around become tougher. Ford is committed to being part of the solution.
“Cities globally are dealing with increased congestion, a growing middle class and environmental issues – all of which can be alleviated by developing mobility solutions fine-tuned to the unique challenges of each location,” said Jim Hackett, chairman, Ford Smart Mobility LLC, the company’s subsidiary created to design, build, grow and invest in emerging mobility services. “At the same time, by expanding our business model to include new forms of transportation – from bikes to dynamic shuttles and more – we are introducing new customers to Ford and creating new revenue and profit opportunities for the future.”
Ford’s acquisition of Chariot, subject to normal customary closing conditions, will serve as the cornerstone for its new global shuttle services business. The shuttle service is expected to be expanded beyond San Francisco to at least five additional markets in the next 18 months.
Started in 2014, Chariot operates nearly 100 Ford Transit shuttles along 28 routes throughout San Francisco Bay Area. Today, Chariot’s routes are crowd-sourced based on rider demand. In the future, they will operate dynamically – using data algorithms to map efficient routes to best serve the real-time mobility needs of communities.
The Chariot shuttles complement mass transit by filling the gap between taxi and bus services – providing an on-demand, point-to-point transportation option that is convenient, efficient and cost-effective. For every one dynamic shuttle that is placed into service during peak travel times, urban congestion could be reduced by up to 25 fewer vehicles, according to a private study for Ford conducted by KPMG.
“Chariot’s mission from day one has been to solve the commute by providing a mass transit solution that is fast, reliable and affordable for people living in today’s cities,” said Ali Vahabzadeh, Chariot cofounder and CEO. “We started our Chariot service with Ford’s 15-passenger vehicles and continue to use Ford Transit shuttles to this day. We couldn’t be more thrilled to be Ford Smart Mobility’s first acquisition and leverage its leadership in transportation to fulfill Chariot’s goals worldwide.”
Bikes are another important mode of transportation for commuters in the Bay Area. Ford and Motivate, the global leader in bike share, are working with city officials to add new stations and increase the number of bikes to 7,000 in the Bay Area by the end of 2018. When it launches next year, Ford GoBike will be accessed by users through the FordPass platform.
“A transportation revolution is coming to the Bay Area,” said Jay Walder, CEO of Motivate. “This unique partnership with Ford shows that bike share is no longer alternative transportation; it is central to creating smart, on-demand mobility that represents our values for equity and sustainability. Thanks to the partnership of Metropolitan Transportation Commission, San Francisco, San Jose, Oakland, Berkeley and Emeryville, bike share will soon be available for all in the Bay Area.”
Ford plans to develop technologies to use data collected from the bikes to build an interconnected mobility network. This could include real-time data, such as weather conditions, usage patterns and bike availability, to optimize commutes.
Ford also is establishing its new City Solutions team to work with cities on expanding mobility services worldwide as part of Ford Smart Mobility LLC. John Kwant – who has worked with several global cities during his Ford career as part of the company’s government affairs and global strategy teams – has been tapped to lead the effort as vice president, Ford City Solutions.
The team will address the reality that each city’s transportation ecosystem has evolved over time and poses a unique set of transportation challenges. Through a joint discovery process, Ford City Solutions will work with municipalities to propose, pilot and develop mobility solutions tailored to the community. Discussions are already under way with several global cities.
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.