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Jaguar XF vs Tornado

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Storm chaser ‘Tornado Tim’ Baker drove the Jaguar XF through America’s Midwest in search of a tornado as this year’s storm season came to a dramatic close.

A storm chaser’s role is vital to help predict tornadoes and save lives, so the XF was the perfect mobile lab for the chase team to catch their tornado and collect vital data.

The chase, which covered 2,000 miles (3, 218kms) of highway and farm tracks through seven US states, saw the XF evade baseball-sized hailstones and drive through floods and high winds before intercepting a twister on the Iowa-Illinois border.

Tornado Tim drives a country road in search of an oncoming Tornado outside of Princeton Illinois on Wednesday June 22, 2016  (photo by Sandy Huffaker/Jaguar)

Tornado Tim drives a country road in search of an oncoming Tornado outside of Princeton Illinois on Wednesday June 22, 2016 (photo by Sandy Huffaker/Jaguar)

After the encounter, Baker said: “Storm chasing is all about getting to the right place at the right time – and also staying out of trouble. It has been an interesting year for storms and it was great to try this car out as the season came to a close.”

“It took us a while to track one down, but when the weather map delivered, the car did too. The navigation and in-car Wi-Fi, which allowed us to connect our multiple devices, worked brilliantly in the chase. The all-wheel-drive capability was also excellent as we travelled through rain and floods on loose gravel roads.”

The chase began with the biggest storm of the 2016 season looming over the American Midwest, with a potential 95 million people in its path. It took Tim from Denver, Colorado, right up into Minnesota and down to Illinois.

During the chase, Baker met Brian Smith, Warning Coordination Meteorologist at the Omaha office of the National Weather Service (NWS), which uses radar data to help scientists issue prompt, life-saving warnings to agencies and the general public.

Tornado Tim ldrives towards an incoming super cell outside of Montevideo, MN on Saturday, June 19, 2016  (photo by Sandy Huffaker/Jaguar)

Tornado Tim ldrives towards an incoming super cell outside of Montevideo, MN on Saturday, June 19, 2016 (photo by Sandy Huffaker/Jaguar)

Tornado chasers like Tim, and the vehicles they use, are a vital part of the modern network of weather warning. Having people on the ground to confirm the storms and analyse their path can help experts study them and predict future disasters.

The AWD XF found its twister when Baker intercepted a Category EF 0 (60-70 mph winds) tornado, two hours west of Chicago. The storm drenched the region, flooding roads and scattering debris, but the car coped brilliantly with the dirt roads and slippery highways.

With roads blocked, Tim used the super quick, pinch and zoom in-car navigation system, with 3D and satellite mapping, to find a safe way around the twister. Overnight, the storm delivered several more tornadoes, damaging buildings.

Kevin Stride, Vehicle Line Director for Jaguar XF said: “This was a real showcase for the XF’s capabilities. Tim was able to view storm data on the car’s 10 inch touchscreen and use the world-class In-Control Touch Pro navigation system to find them while travelling in comfort.”

“When the weather deteriorated, the car’s all surface capability with Adaptive Surface Response and torque on demand all-wheel-drive came into its own. The XF’s AdSR was able to fully exploit all available traction by altering mapping of the throttle, automatic transmission and DSC system to give confidence on the gravel tracks and cope with extreme flooding and high winds.”

“As expected, the tornado chase provided some extremely diverse challenges and we knew this would be a real genuine test for the XF, so we were delighted to see it handle all the conditions with ease…and come back in one piece.”

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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.

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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.”

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Motor Racing meets Machine Learning

The futuristic car technology of tomorrow is being built today in both racing cars and
toys, writes ARTHUR GOLDSTUCK

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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.

DeepRacer on the inside

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.

DeepRacer on the outside

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.”

AWS CEO Andy Jassy unveils DeepRacer

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.

  • Arthur Goldstuck is founder of World Wide Worx and editor-in-chief of Gadget.co.za. Follow him on Twitter on @art2gee and on YouTube

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