Vans have been a key component in delivery for years now and drones are a relatively new phenomenon. However, in an effort to improve mobility in rural areas, Ford employees have come up with ideas to make the two work together.
For more than half a century, vans have played a key role in deliveries. Drones are a modern phenomenon. But the two could work hand in hand to improve mobility in urban areas in one example of Ford’s vision for the “City of Tomorrow”.
Self-driving vans could quickly and efficiently transport everything from groceries to urgently needed medical supplies on the ground, with drones potentially able to take to the air for the final leg of the journey to reach destinations inaccessible by car, such as high up in a tower block – or where parking would be difficult, impractical, or unsafe.
The innovative “Autolivery” concept, developed by a team of Ford employees for the company’s Last Mile Mobility Challenge, imagines electric self-driving vans used together with drones to pick up and drop off goods and packages in urban areas. The concept can be experienced through virtual reality headsets at Mobile World Congress, the world’s largest gathering for the mobile industry, in Barcelona, as part of Ford’s vision of the “City of Tomorrow”.
The experience showed dinner party preparations, with a missing ingredient quickly ordered and delivered in time to add to the recipe. As new data reveals that motorists in Europe’s cities spent up to 91 hours sitting in congested traffic during 2016, the “Autolivery” service illustrates how new technologies could improve the lives of consumers with smart connected homes, and help to pave the way to a more sustainable future. *
“Ford has at its heart a culture of disruption and innovation designed to come up with solutions that put people first, to save them time, money and aggravation, and also to make our cities easier to navigate and better to live in,” said Ken Washington, vice president, Research and Advanced Engineering, Ford Motor Company.
The Autolivery idea, one of many submitted by Ford employees to tackle the last mile challenge, paid particular attention to the challenge of the “last 15 metres” in goods delivery. Widely considered the most challenging part of the goods delivery process to automate, many companies are working on how to solve the complexity of delivering packages the last 15 metres, or from kerb to door. The pressure to solve this challenge is expected to increase globally in coming years with GDP growth and a rise in local deliveries due to online sales.
“While the scene shown today is not yet possible, ‘Autolivery’ suggests how our ongoing mobility research could enrich our lives in a more sustainable ‘City of Tomorrow’,” said Washington.
“The City of Tomorrow” envisages overcoming mobility challenges in urban environments, including gridlock and air pollution to help people move more easily today and in the future. Roads could be converted into green space and parks, allowing for higher quality of life and healthier communities. The company regularly invites employees, entrepreneurs and start‑ups to develop innovations through hackathons and challenges. “Autolivery” was developed by Shanghai-based Ford designers Euishik Bang, James Kuo and Chelsia Lau who responded to Ford’s Last Mile Mobility Challenge – to come up with mobility solutions for urban areas.
“It’s all about making life in the city easier. The possibility of harnessing autonomous and electric vehicle technology with drones to quickly and easily send and deliver parcels could help to make life better for everyone,” said Bang. Also developed for Last Mile Mobility Challenge, and shown at Mobile World Congress, were the electric rideable platform Carr‑E and the folding electric tricycle TriCiti.
Ford intends to have a fully autonomous, SAE level 4-capable vehicle for commercial application in mobility services such as ride sharing, ride hailing or package delivery fleets in 2021. It also expects continued growth in electrified vehicles offerings, to the point where they outnumber their petrol‑powered counterparts in the next 15 years. Shared modes of transportation will continue to gain popularity and connected communications between vehicles and infrastructure will grow.
“We are challenging ourselves to understand how people live, work and move in urban areas, to inform our research in mobility technologies and solutions,” Washington said.
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.