Nissan Motor has announced that the new Serena will come equipped with the company’s ProPILOT autonomous drive technology, offering convenience and peace of mind during highway mobility.
ProPILOT is a revolutionary autonomous drive technology designed for highway use in single-lane traffic. Nissan is the first Japanese automaker to introduce a combination of steering, accelerator and braking that can be operated in full automatic mode, easing driver workload in heavy highway traffic and long commutes.
Employing advanced image-processing technology, the car’s ProPILOT system understands road and traffic situations and executes precise steering enabling the vehicle to perform naturally. ProPILOT technology is extremely user-friendly, thanks to a switch on the steering wheel that allows the driver to easily activate and deactivate the system. ProPILOT’s easy-to-understand and fit-to-drive interface includes a personal display showing the operating status.
The accelerator, brakes and steering are controlled based on information obtained through a mono camera equipped with advanced-image processing software. The ProPILOT camera can quickly recognize in three-dimensional depth both preceding vehicles and lane markers.
Once activated, ProPILOT automatically controls the distance between the vehicle and the preceding vehicle, using a speed preset by the driver (between approximately 30 km/h and 100 km/h). The system also keeps the car in the middle of the highway lane by reading lane markers and controlling steering, even through curves.
If a car in front stops:
The ProPILOT system automatically applies the brakes to bring the vehicle to a full stop. After coming to a full stop, the vehicle will remain in place even if the driver’s foot is off the brake pedal. When ready to resume driving, ProPILOT is activated when the driver touches the switch again or lightly presses the accelerator.
Nissan is carrying out intensive studies of driving conditions in various regions so that ProPILOT will be well suited to the conditions in the markets in which it will be launched. The ProPILOT system equipped on the Serena in Japan was developed in pursuit of an easy-to-use technology for highway driving conditions in Japan.
Making Zero Fatalities a Reality
Nissan is proactively working on vehicle intelligence and vehicle electrification to make its corporate visions of “Zero Emissions” and “Zero Fatalities” a reality. Under “Nissan Intelligent Mobility”, ProPILOT promotes safety and instills confidence in drivers, and it is part of “Nissan Intelligent Driving”.
ProPILOT will be introduced into other vehicles, including the Qashqai in Europe in 2017. There are also plans for the technology to be introduced in the U.S. and China markets. A multi-lane autonomous driving technology will enable automatic lane changes on highways and is planned for introduction in 2018 while autonomous driving on urban roads and in intersections is planned for launch in 2020.
Nissan will advance its leadership in autonomous drive technology by introduction in each market’s core models, further improving safety and pioneering a new era for the automobile.
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.