To celebrate the release of Gran Turismo Sport, Nissan has created the ultimate remote-control car for gamers – the Nissan GT-R /C.
Celebrating the release of Gran Turismo Sport, out in Europe on October 18th, and marking 20 years of Nissan involvement in the Gran Turismo gaming series, the one-off project car was extensively modified to be driven entirely by a DualShock 4 controller.
A few millimetres of button movement or joystick travel are all it takes to unleash the GT-R’s full power. The remote-control vehicle is capable of a top speed of 196mph – not restricted for the purpose of the project car – with no one sitting behind the wheel.
The GT-R /C was put through its paces by NISMO racing driver Jann Mardenborough, around Silverstone’s famous National Circuit. Jann controlled the GT-R /C from the cockpit of a Robinson R44 Raven II helicopter, which had been given special permission to operate at a low altitude.
Mardenborough is one of the most successful winners of GT Academy, Nissan’s revolutionary driver discovery and development programme. Jann was approached to be the first driver of the GT-R /C because of his unrivalled talent in both Gran Turismo gaming and real-life motorsport.
Nissan has brought ingenuity and innovation to motorsport for more than 80 years, fusing technology with performance to maintain a competitive edge. Since 2008, Nissan has also made motorsport more accessible to everyone with GT Academy turning amateur gamers into professional racing drivers.
The GT-R /C was engineered in the UK by JLB Design Ltd., using a standard-spec 542bhp V6-powered 2011 R35 – the same year Jann Mardenborough won GT Academy.
On Mardenborough’s fastest lap (1:17:47), the GT-R /C averaged 76mph/122kph and reached a top speed of 131mph/211kph – the ‘driven’ average for the 1.6mile/2.6km loop circuit is around 83mph/134kph.
The GT-R /C is fitted with four robots that operate the steering, transmission, brakes and throttle. Six computers mounted in the boot update the controls at up to 100 times a second. The steering position is measured to one part in 65,000.
The unmodified DualShock 4 connects to a micro-computer which interprets the joystick and button signals and transmits them to the GT-R /C’s on-board systems. The wireless operation has a primary control range of one kilometre.
To help Mardenborough judge the vehicle’s speed through the corners, a Racelogic VBOX Motorsport sensor was installed to relay speed data to a LCD display in the helicopter cockpit.
The GT-R /C is also fitted with two independent safety systems, operating on different radio frequencies, which allow two additional operators to apply full ABS braking and cut the engine in the event of the main operator losing control of the vehicle.
James Brighton, JLB Design Ltd commented; “The GT-R /C presented some unique challenges and a number of engineering firsts for us. We had to ensure the robotics would operate effectively during fast acceleration/deceleration as well as high cornering g-forces; deliver realistic and reassuring control of the car at all speeds; and maintain a robust connection between the car and the DualShock®4 over variable distances and with minimal latency in robot response times.
“I’m delighted to say all these challenges were overcome but it’s testament to Jann’s unique skillset that he was able to master the vehicle’s operation in a very short period of time whilst delivering some truly impressive lap times.”
Jann Mardenborough added; “This was once-in-a-lifetime, truly epic stuff. The GT-R /C has brought my two worlds together – the virtual of gaming and the reality of motorsport – in a way I never thought possible. The response from the car when using the controller was far more engaging than I thought it would be. JLB Design has done an incredible job at making everything respond really well.
“Steering, acceleration and braking were all intelligently configured, allowing for controlled application so I could really get a feel through the corners and hold it steady down the fast straights. Driving a full-size, remote-control GT-R to 131mph at Silverstone whilst chasing it down in a helicopter was an unforgettable experience. Now that’s innovation that excites!”
In 2018, the Nissan GT-R /C will be used in a tour of primary and secondary schools in the UK to promote future careers in STEM (Science, Technology, Engineering and Maths) subjects.
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.