Jaguar Land Rover has demonstrated a range of research technologies that would allow a future autonomous car to drive itself over any surface or terrain.
Jaguar Land Rover’s multi-million pound Autonomous all-terrain driving research project aims to make the self-driving car viable in the widest range of real life, on- and off-road driving environments and weather conditions.
Tony Harper, Head of Research, Jaguar Land Rover, said: “Our all-terrain autonomy research isn’t just about the car driving itself on a motorway or in extreme off-road situations. It’s about helping both the driven and autonomous car make their way safely through any terrain or driving situation.
“We don’t want to limit future highly automated and fully autonomous technologies to tarmac. When the driver turns off the road, we want this support and assistance to continue. In the future, if you enjoy the benefits of autonomous lane keeping on a motorway at the start of your journey, we want to ensure you can use this all the way to your destination, even if this is via a rough track or gravel road.
“So whether it’s a road under construction with cones and a contraflow, a snow-covered road in the mountains or a muddy forest track, this advanced capability would be available to both the driver AND the autonomous car, with the driver able to let the car take control if they were unsure how best to tackle an obstacle or hazard ahead. We are already world-leaders in all-terrain technologies: these research projects will extend that lead still further.”
To enable this level of autonomous all-terrain capability, Jaguar Land Rover’s researchers are developing next-generation sensing technologies that will be the eyes of the future autonomous car. Because the sensors are always active and can see better than the driver, this advanced sensing will ultimately give a vehicle the high levels of artificial intelligence required for the car to think for itself and plan the route it should take, on any surface.
SURFACE IDENTIFICATION AND 3D PATH SENSING research combines camera, ultrasonic, radar and LIDAR sensors to give the car a 360 degree view of the world around it, with sensors so advanced that the car could determine surface characteristics, down to the width of a tyre, even in rain and falling snow, to plan its route.
Tony Harper said: “The key enabler for autonomous driving on any terrain is to give the car the ability to sense and predict the 3D path it is going to drive through. This means being able to scan and analyse both the surface to be driven on, as well as any hazards above and to the sides of the path ahead. This might include car park barriers, tree roots and boulders or overhanging branches, as well as the materials and topography to be driven on.”
Ultrasonic sensors can identify surface conditions by scanning up to five metres ahead of the car, so Terrain Response settings could be automatically changed before the car drives from tarmac to snow, or from grass to sand. This will optimise all-terrain performance, without loss of momentum or control.
To complete the 3D path, branches overhanging a track, or a car park overhead barrier would also need to be identified to determine if the route ahead is clear. Overhead Clearance Assist uses stereo camera technology to scan ahead for overhead obstructions. The driver programmes the system with the vehicle’s height, which can include roof boxes or bicycles, and the car will warn the driver with a simple message in the infotainment touchscreen if there is insufficient clearance.
Sensors could also be used to scan the roughness of the road or track ahead and adjust vehicle speed. TERRAIN-BASED SPEED ADAPTION (TBSA) uses cameras to sense bumpy terrain including uneven and undulating surfaces and washboard roads, potholes and even standing water. It is then intelligent enough to predict the potential impact of these surfaces on the car’s ride and automatically adjust speed to keep passengers comfortable.
Another key element of successful all-terrain autonomous driving is the ability for vehicles to communicate with each other, especially if they are out of sight around a bend or on the other side of an off-road obstacle.
In a world-first off-road demonstration, Jaguar Land Rover has connected two Range Rover Sports together using innovative DSRC (Dedicated Short Range Communications) technology to create an Off-Road Connected Convoy. This wireless vehicle-to-vehicle (V2V) communications system shares information including vehicle location, wheel-slip, changes to suspension height and wheel articulation, as well as All-Terrain Progress Control (ATPC) and Terrain Response settings instantly between the two vehicles.
Tony Harper said: “This V2V communications system can seamlessly link a convoy of vehicles in any off-road environment. If a vehicle has stopped, other vehicles in the convoy will be alerted – if the wheels of drop into a hole, or perhaps slip on a difficult boulder, this information is transmitted to all of the other vehicles. In the future, a convoy of autonomous vehicles would use this information to automatically adjust their settings or even change their route to help them tackle the obstacle.
“Or for the ultimate safari experience, cars following in convoy would be told by the lead car where to slow down and stop for their passengers to take the best photographs.”
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.