Ford has become the first automaker to test autonomous vehicles at Mcity, a full-scale simulated real-world urban environment at the University of Michigan.
The 32-acre facility is part of the university’s Mobility Transformation Center.
“Testing Ford’s autonomous vehicle fleet at Mcity provides another challenging, yet safe, urban environment to repeatedly check and hone these new technologies,” said Raj Nair, Ford group vice president, Global Product Development. “This is an important step in making millions of people’s lives better and improving their mobility.”
Ford has been testing autonomous vehicles for more than 10 years and is now expanding testing on the diversity of roads and realistic neighborhoods of Mcity near the North Campus Research Complex to accelerate research of advanced sensing technologies.
Ford Fusion Hybrid Autonomous Research Vehicle merges today’s driver-assist technologies, such as front-facing cameras, radar and ultrasonic sensors, and adds four LiDAR sensors to generate a real-time 3D map of the vehicle’s surrounding environment – essential for dynamic performance.
Real-world testing in a whole new way
Mcity opened in July. The full-scale urban environment provides real-world road scenarios – such as running a red light – that can’t be replicated on public roads. Click here to see the Fusion Hybrid Autonomous Research Vehicle testing at Mcity.
There are street lights, crosswalks, lane delineators, curb cuts, bike lanes, trees, hydrants, sidewalks, signs, traffic control devices – even construction barriers. Here, Ford Fusion Hybrid Autonomous Research Vehicle is tested over a range of surfaces – concrete, asphalt, simulated brick and dirt – and maneuvers two-, three- and four-lane roads, as well as ramps, roundabouts and tunnels.
“The goal of Mcity is that we get a scaling factor. Every mile driven there can represent 10, 100 or 1,000 miles of on-road driving in terms of our ability to pack in the occurrences of difficult events,” said Ryan Eustice, University of Michigan associate professor and principal investigator in Ford’s research collaboration with the university.
Ford’s track record of technology leadership
Ford revealed its Fusion Hybrid Autonomous Research Vehicle with University of Michigan and State Farm Insurance in 2013 in an effort to advance sensing systems so these technologies could be integrated into Ford’s next-generation vehicles. Earlier this year, Ford announced it moved its research efforts in autonomous vehicle technology to the next step in development, to the advanced engineering phase. The team is working to make sensing and computing technologies feasible for production while continuing to test and refine algorithms.
Ford offers a full portfolio of semi-autonomous technology and the most available driver-assist features in four vehicle segments in the United States – large light-duty pickups with F-150, midsize SUVs with Edge and Explorer, midsize cars with Fusion and large cars with Taurus.
Along with testing at Mcity and on public roads, Ford’s autonomous fleet has been put through the paces at the company’s vehicle development facilities in Dearborn and Romeo, Michigan.
“We are pleased to welcome Ford as the first automaker to use Mcity to test autonomous vehicles,” said Peter Sweatman, director, Mobility Transformation Center. “Mcity offers a unique, real-world test environment that will help Ford accelerate development of its autonomous technology while building on its existing research collaboration with University of Michigan.”
Changing the way the world moves: Ford Smart Mobility
Autonomous vehicles are one element of Ford Smart Mobility, Ford’s plan to deliver the next level in connectivity, mobility, autonomous vehicles, the customer experience and big data.
With Ford Smart Mobility, the company is once again changing the way the world moves to make people’s lives better – using innovation and advanced technology across its business to address the world’s biggest transportation challenges. This is what Henry Ford did 112 years ago.
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.