Over the past decade or so, the technology industry has transformed many things we used to buy as physical products into digital services we subscribe to or access on a pay-per-use basis. Think about how we have moved from buying CDs towards paying a monthly subscription to use Spotify, or the shift from DVDs to Netflix.
Could cars be the next part of everyday life to be transformed from a physical product into an on-demand service? Some technologists and economists predict that the privately-owned car will go the way of the horse and buggy within in a decade. In their view, only motor enthusiasts and the rich will one day own personal cars, which they will use for leisure rather than transportation.
One study in the US, for example, forecasts that private car ownership will fall by as much 80% by 2030 and that using electric ride-shares will be four to 10 times cheaper than buying a new car by 2021. The researchers foresee a world where communal, autonomous (self-driving) electric cars owned by cities or ride-sharing companies offer a safe, efficient and flexible personal transportation system.
We’re already at the beginning of this revolution, with ride-hailing services like Uber and Taxify already potentially cheaper for some people than owning a car. Car sharing services such as Zipcar – which enables you to subscribe by the month and then hire a car by the hour – and Turo – an Airbnb-like service that matches car owners with car renters – have also started to pop up around the world.
MyTreasury.co.za crunched the basic numbers and found using Uber could be more cost-effective on a per-kilometre basis for people in Johannesburg, Cape Town and Durban who travel less than 50 kilometres a day. The reason for this is that you pay only for the distance you travel, without the costs of car ownership such as maintenance, insurance, financing, licences and depreciation.
One also needs to add in the lifestyle costs. How much of your time do you spend stuck in traffic or looking for parking? What if you could be making calls and working on your computer during your commute instead of sitting at the wheel?
The end of the parking lot?
The authors of the US study I cited earlier believe that the effect of shared ride-hailing will completely change how cities work in the years to come. Not only will it be more efficient to increase the utilisation of vehicles by sharing them, it will also reduce the amount of space we currently use for parking in cities where real-estate is expensive and in short supply.
What’s more, autonomous vehicles should be safer since their software will not make mistakes, drive recklessly or get behind the wheel after a beer too many after a long lunch on Friday. In theory, a shift to shared, driverless cars should also improve traffic flow by reducing the stop-start rhythm of human driving.
As great as that all sounds, shared, driverless vehicles are most likely further in the future than the more optimistic forecasts suggest. While the technology is advancing fast, it may take longer to change human behaviour. For many of us in the middle classes, a car is more than a way to get from point A to point B. It is also a status symbol, a fashion statement and an emblem of personal freedom.
This is why car ownership remains stubbornly high even in European and Asian cities with cheap, reliable public transport and bans on, or congestion charges for, private cars in their centres. The transition will be even slower in a country like South Africa. The taxi industry, unions and government will resist the job losses; autonomous vehicles are probably also not ready to navigate the unpredictable drivers of Jozi’s mean streets.
Transforming car ownership
Still, the rise of on-demand technology is already affecting many aspects of the car ownership experience. Our data at Naked indicates that a surprisingly small percentage – just over 21% – of our customers opt for car hire as part of their insurance cover. We suspect the reason for this is that many of our customers choose to save on their premiums knowing that they can Uber for a while if something happens to their car.
Car insurance itself is also turning into an on-demand service, powered by artificial intelligence and algorithms, just like ride-hailing services. For example, Naked’s CoverPause allows customers to switch their accident cover off when they are not using their vehicle for a while.
You can save around half of your insurance premium on the days that you are not driving. Simply press one button on the app to downgrade your cover. If you want to drive again, you can switch back to full cover with one click. In future, we can also expect to see car insurance pricing models, such as paying for each kilometre you drive, to become more common.
So, while car ownership and car insurance are likely to be a part of your life for some years to come, connected technology will change your experience in remarkable ways. Today, buying and switching insurance from your phone is as quick and easy as registering for Uber and hailing your first ride.
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.