Continental has revealed two new tyre technology concepts that will make for even greater road safety and comfort in the future.
The two systems enable continuous monitoring of the tyre’s condition, as well as situation-matched adaptation of tyre performance characteristics to prevailing road conditions. The technologies, called ContiSense and ContiAdapt, made their debut at the recent 2017 Frankfurt Motor Show (IAA).
ContiSense is based on the development of electrically conductive rubber compounds that enable electric signals to be sent from a sensor in the tyre to a receiver in the car. Rubber-based sensors continuously monitor both tread depth and temperature. If the measured values are above or below predefined limits, the system at once alerts the driver.
If anything penetrates the tread, a circuit in the tyre is closed, also triggering an immediate warning for the driver – faster than the systems used to date, which only warn the driver when the tyre pressure has already begun to fall.
In the future, the ContiSense system will feature additional sensors that can also be utilised individually. Thus, information about the road surface, such as its temperature or the presence of snow, can be “felt” by the tyre and passed on to the driver. The data can be transmitted to the vehicle electronics, or via Bluetooth to a smartphone.
ContiAdapt combines micro-compressors integrated into the wheel to adjust the tyre pressure with a variable-width rim. The system can thus modify the size of the contact patch, which under different road conditions is a decisive factor for both safety and comfort.
Four different combinations allow perfect adaptation to wet, uneven, slippery and normal conditions. For example, a smaller contact patch combined with high tyre pressure make for low rolling resistance and energy-efficient driving on smooth, dry roads. By contrast, the combination of a larger contact patch with lower tyre pressure delivers ideal grip on slippery roads.
The system also permits very low tyre pressures of below 1 bar to be set, to help ease the vehicle out of a parking space in deep snow, for example, or to traverse a dangerous stretch of black ice.
ContiSense and ContiAdapt are joined by a concept tyre that enables the benefits of both systems to be fully leveraged. The tyre design features three different tread zones for driving on wet, slippery or dry surfaces.
Depending on the tyre pressure and rim width, different tread zones are activated and the concept tyre adopts the required “footprint” in each case. In this way, the tyre characteristics adapt to the prevailing road conditions or driver preferences.
Continental considers both these tyre technology concepts promising solutions for the mobility of the future as tyres are adapted to meet the needs of automated driving and electrification.
Low rolling resistance, for example, makes it possible for electric cars to cover greater distances on a single charge. At the same time, the tyres can be adapted to suit the driver’s personal preferences, or in response to sudden changes in the weather.
These concepts are the logical next step in the future-oriented development of the REDI sensor, brought to market by Continental in 2014, which was instrumental in establishing smart communication between vehicle and tyre.
The new tyre technology concepts follow on from two established mobility technologies: ContiSeal, for the automatic sealing of punctures, and ContiSilent, for a tangible reduction in tyre/road noise.
Able to draw on more than a century of experience in tyre technology and with in-house expertise in the fields of vehicle electronics and automotive IT, Continental is systematically aligning its products with the future requirements of autonomous driving and electric mobility while bolstering its drive towards Vision Zero – an initiative that aimed at achieving zero fatalities, zero injuries and zero accidents.
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.