Rewarding good drivers with reduced travel costs is the idea behind a new Ford Smart Mobility experiment that takes a similar approach to achieving goals as that employed by fitness and exercise apps.
The Ford-led Driver Behaviour Project explores providing drivers with a personal score, based on various driver inputs, and accessed via a prototype smartphone app. This type of research could lead to cheaper car-hire and car-sharing, and provide insurers with information required to support discounts.
“Like an activity-tracking app that shows the distance we cover and calories we burn, a personal driver score encourages people to drive smarter,” said Jonathan Scott, project lead, Ford Smart Mobility. “We wanted to better understand how people use our products so we could help them to improve that behaviour – and a score, combined with guidance, makes it easier to improve.”
Over a four-month period, plug-in devices gathered data from more than 40 Ford Fiestas, driven by volunteers in London, to record actions that each driver took over 160,000 kilometres and more than 4,000 hours. This included detailing the slightest turn of the steering wheel and harsh braking, as well as time of day, weather, and road history.
Ford’s perspective is that customers own their data and the company is exploring ways that customers can use this data to their advantage. For this project, the vehicle data empowers drivers with their personal driving score, based on activities such as steady acceleration and steering smoothly.
The app enables drivers to see how different driving behaviours affected that score, and offers insights to help improve – such as driving in the correct gear. It also calculates a score for each journey based on the driver’s interaction with the vehicle, as judged via the data received on accelerating, braking, and steering. The score changes according to the results of each journey, with a graph showing the trend over time, enabling drivers to see on which days their scores were higher or lower.
Both Ford’s data scientists and transport data experts, Transport API, will now analyse the data to gain further insights. In addition to the vehicle-specific data, global design company IDEO was engaged to research what people say, think, feel and do when behind the wheel. This showed a significant difference between how people think they drive, and how they actually do drive.
“From the vehicle data and research gathered, we were able to test an internally developed, highly advanced driving score algorithm. The score could be used to develop a mobility profile, enabling drivers to save money on services tailored to their needs,” Scott added.
Ford is currently expanding into both an auto and a mobility company; as such the company is aggressively pursuing emerging opportunities through Ford Smart Mobility – its plan to be a leader in connectivity, mobility, autonomous vehicles, the customer experience, and data and analytics. The Driver Behaviour Project could help to enhance current mobility solutions, including the on-demand GoDrive car sharing service and the on demand ride sharing service GoRide.
Identifying driver stress
The project team also is studying how driving affects the physical and emotional states of another group of volunteers in the U.K., at the University of Nottingham. This research is exploring ways to help people become better drivers.
Volunteers are subjected to a series of driving situations, both using a driving simulator and real world driving, during which their heart rates, eye movements, and brain patterns are monitored. The research highlights when drivers are nervous or stressed, such as in heavy traffic, or when larger vehicles reduce visibility.
Changing the way the world moves
Ford is demonstrating the Driver Behaviour Project, along with Ford Smart Mobility services and the company’s new mobility experience platform FordPass at London Technology Week, with events taking place across the city from June 20-26.
Also on show is smart parking system GoPark, soon to be available with a new space-finding function. Portable hardware devices plugged into participating vehicles will help to identify vehicle locations and enable a powerful, probability-based algorithm to let members know the likelihood there will be a space free at their chosen destination.
As headline sponsor of London Technology Week, the company is hosting the panel discussion and networking session “Changing the way the world moves” at 16:30 CET, on June 21, at the Vinyl Factory, in Soho. Mobility and technology experts from Transport for London, the geocoding system What3Words, technology focused merchant bank Lepe Partners and IDEO will be among the panellists. The session will be moderated by startup expert, connector and advisor Bindi Karia. Guests also will be able to talk to Mike Nakrani, head of Ford Smart Mobility Europe, and other Ford Smart Mobility project leads.
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.