Our world’s growing metropolises require a new kind of transport network. Autonomous trains and completely new train concepts will revolutionise passenger and freight traffic between cities.

It’s 6 pm in Berlin. It would be nice to go and try out that new restaurant in Hamburg. So you order an autonomous taxi from Uber via your smartphone. It arrives punctually at your office, picks up your wife from her workplace and then heads over to the central station Hyperloop portal. The autonomous taxi has already contacted the vacuum conveyor, so it can drive directly into the waiting Hyperloop pod. The pod accelerates gradually until it reaches airline speed, and you get to Hamburg in just 20 minutes. The Uber taxi drives out of the Hyperloop portal and straight to the restaurant … This is the vision of Doug Chey, Senior Vice President Systems Development at Hyperloop One. “It’s direct, autonomous, ultrafast intercity travel and the doors only open twice, once to let you in and once to let you out,” he writes in his Hyperloop One blog. He does admit, however, that this is the long-term vision. “We obviously have a ton of engineering work to do.” Elon Musk, who is already successfully building electric cars with his company Tesla and launching spacecraft with SpaceX, is the original visionary behind these trains in vacuum tubes. Hyperloop One is one of the companies working on implementing the idea and has already carried out some initial driving tests with the vehicles – but currently only at a speed of just over 100 kilometres per hour.

Driverless doesn’t equal autonomous

For now, “traditional” trains will continue to connect metropolises. But here, too, there is a trend towards more and more automation. In the future, as well as the doors closing automatically, the entire train will run automatically.

Driverless trains were introduced a number of years ago and can be found in cities including Nuremberg, Singapore and Paris. However, they are only in use in local transport systems. What’s more, they are not truly autonomous, because the intelligence that runs them is in the infrastructure; only a little of it – if any – is in the railway vehicle itself. None of the driverless trains we have seen thus far are fitted with any sensors to monitor the route in front of them. In addition, the trains are controlled remotely via a computer system that is installed in a control room and do not make any decisions for themselves.

However, this will change in the not too distant future, and it is a change that will bring significant benefits. Autonomous trains can run more frequently and achieve higher speeds, enabling managers to increase the number of trains in operation on a route instead of having to go to the significant expense of building new tracks.

The French rail operator SNCF, for example, intends to run its TGV high-speed train autonomously from 2023 onwards. Equipped with its own sensors, the train should be able to detect obstructions in its path and brake automatically. Testing is due to start in 2019. There will still be a train driver on board of autonomous trains, although he or she would only intervene in the event of an emergency. SNCF expects to be able to increase the speed and frequency of rail connections with an autonomous TGV and operate up to 25 per cent more high-speed trains on the route between Paris and Lyon.

Deutsche Bahn has also announced that it will be introducing autonomous trains on a small number of selected routes by 2023 at the latest. The company started a pilot project for its autonomous trains in the Ore Mountains in 2017, running a converted diesel train in an “automated” manner on a 25-kilometres test route. The vehicle has to learn to detect obstacles and optical signals in its path. “The train will have sufficient intelligence to work with the existing infrastructure,” according to project coordinator Michael T. Hoffmann. Sensor systems like those fitted to driverless cars (cameras, radar and lidar, for example) are used for the optical measurement of distance and speed. However, the vehicle does not make its own decisions, as “a control room remains in control”, to quote Hoffmann.

Intelligence for single wagons in freight transport

The gift of autonomy will be bestowed not only to complete trains. The German Aerospace Centre, or DLR for short, recently presented NGT Cargo, a concept for the freight trains of the future which gives every individual wagon the capability to be autonomous. “Freight transport is currently dominated by block trains that are not shunted and that use a large number of wagons to carry large, standard volumes of freight from point A to point B,” explains DLR researcher Dr Joachim Winter, who is leading the Next Generation Train (NGT) project. Coupling these trains, however, takes up a great deal of time and significant resources. The automatically driven NGT Cargo trains will be made up of single wagons and powerful end cars, automatically coupled together as required. The intelligent freight wagons in the NGT Cargo concept have a separate drive based on electric motors and a battery that stores energy recovered during braking. This makes it possible for the single wagons to shunt autonomously and, thanks to their sensors, they can even travel the final kilometres to the respective customer autonomously.

A lack of qualified staff, higher efficiency and better security are grounds for extending automation to seafaring applications. The first ships with autonomous functions are set to be launched in 2018.

It is not a matter of whether autonomous ships will exist, but when. Oskar Levander, the Vice President of Innovation – Marine at Rolls-Royce, is convinced: “The technologies needed to make remote and autonomous ships a reality exist. We will see a remote-controlled ship in commercial use by the end of the decade.” The British company has been active in this area, including starting the Advanced Autonomous Waterborne Applications Initiative (AAWA). Several Finnish universities, companies from the shipping industry and classification society DNV GL participated in this project, which ended in July 2017. The aim of the initiative was to develop the technologies for remote-controlled and autonomous ships.

Autonomy Mixed with Remote Control

There are similar projects around the world, which generally do not envisage completely autonomous operation. Human beings are intended to resume control by no later than the time of docking and undocking at the port. However, they will no longer be sitting on the ship’s bridge, but inside a control centre on land. In the future, they are also expected to monitor the ships in autonomous operation from this vantage point, for instance as the vessels navigate the open sea.

“The advantages of unmanned ships are manifold, but primarily centre on the safeguarding of life and reduction in the cost of production and operations,” explains Brett A. Phaneuf, Managing Director of the British firm Automated Ships. According to a study by Munich-based Allianz Insurance, between 75 and 96 per cent of accidents at sea can be attributed to errors made by the crew, often as a result of fatigue. Remote-controlled or autonomous vessels would significantly reduce this risk. A further benefit is that the ships can be designed with higher loading capacity and lower wind resistance. After all, in the absence of a crew, there is no need for bridges, quarters or systems such as heating, ventilation and wastewater management. As a result, the ships become lighter and more streamlined, fuel consumption is reduced, construction and operating costs decrease, and there is more space available for cargo. Last but not least, autonomous ships will make sailors’ work more attractive as a career. Seamen will no longer be at sea for weeks on end and instead will work on land in the control station, able to return home every night.

The Technology is Already Here

Most of the technology necessary for an autonomous vessel exists today; many features are automated on a modern ship’s bridge. The autopilot steers a set course with the help of GPS, while a cruise control system manages the speed. Radars and ship detection systems scan the environment and automatically sound the alarm in the event of danger. In addition, autonomous ships are to be equipped with more sensors. Types of sensors previously unused in a maritime context are integrated along with the normal radar in the Autosea project, for example, to provide the means to also detect small objects such as driftwood or small boats. These sensor types include lidar, infrared or 3D cameras. The Norwegian University of Science and Technology is directing the project, whose industry partners include the two Norwegian companies Kongsberg Maritime and Maritime Robotics.

Globally Connected via Satellite

A software program evaluates the data from all the sensors and determines, for example, whether and how the ship should change course to avoid collisions. People will be able to monitor what is happening via satellite and intervene if necessary. This requires a constant real-time connection with high data transfer rates. To this end, British company Inmarsat – also a partner of the AAWA project – has now deployed four Global Xpress satellites. By means of the Ka band, these can create a high-speed broadband connection that is available worldwide. This method is not only used to transfer the operating data to the control station; autonomous ships can also use it to access weather-forecast information or intelligence from other vessels for decision-making purposes, making them part of the Internet of Things.

Autonomous ships: Stages of development 

2020

Reduced crew, certain functions will be executed with remote control

2025

Remote-controlled, unmanned coastal freighters

2035

Autonomous, ocean-going ships

First Ships from 2018

The plans for unmanned, ocean-going vessels have become increasingly specific. For one, the Hrönn is under construction, a joint project by Automated Ships and Kongsberg Maritime, which is intended to launch in 2018. The Hrönn is designed as a light-duty supply ship for offshore wind turbines or fish farms. The vessel will initially be remotely controlled. However, its control algorithms are due to be developed in parallel during use, later enabling it to be operated fully automatically and even autonomously. Kongsberg is also involved in another project, the Yara Birkeland, which will be the world’s first electrically powered and autonomous container ship. The ship is to first set sail – initially still with crew – in 2018, then with remote control as of 2019, and is expected to be autonomous from 2020 onwards.

“Autonomous shipping is the future of the maritime industry. As disruptive as the smartphone, the smart ship will revolutionise the landscape of ship design and operations,” Mikael Makinen, Rolls-Royce, President – Marine, states with confidence.

(Picture credit: Rolls Royce Plc)

Autonomous farm vehicles will change the landscape of farming, allowing arable land to be used more efficiently while protecting the environment.

Agriculture is facing major challenges. The world’s available agricultural land will need to be used more efficiently to continue to feed its rapidly growing population in the future. Yet farms in many parts of the world today are lacking labour.

Autonomous farm vehicles: In Use 24/7

The major tractor manufacturers have therefore been working for several years on autonomous farm vehicles. For example, Case IH presented its Autonomous Concept Vehicle (ACV) to the public for the first time in 2016. “The ACV retains much of the conventional technology of a modern tractor, and uses ultra-accurate RTK GPS to provide parallel steering capability with a variation of less than 2.5 cm, which many farmers are already using to ensure missed or overlapped land between passes spans no more than this width,” Case IH’s Dan Stuart states. In addition, the ACV is equipped with radar, lidar, proximity sensors, as well as safety and wireless systems, meaning that it can be monitored and controlled from a PC or tablet. Consequently, having entered the field, the tractor can work completely independently without a driver. The ACV is still a concept. However, a test programme working with farmers in real-life conditions has already been launched.

To the Heart of Agriculture

However, in recent times, there has been a growing number of voices responding negatively to the use of large, heavy machinery on farms. Dr Jens-Karl Wegener, Head of the Institute for Application Techniques at Germany’s Federal Research Centre for Cultivated Plants, the Julius Kühn Institute, points out that “agriculture, the way it currently operates, is coming under social criticism. In the face of nitrate pollution, loss of species and soil compaction, we have to critically question how much longer it will be good for.” The use of ever larger machines on ever larger areas of land seems to be part of the problem rather than a solution. Wegener takes a different approach based on the needs of the individual plant.

“This sort of precision farming, geared to the needs of the individual plant, would naturally also affect how the land will look in the future,” project team member Lisa-Marie Urso says. Spot farming is the term she and her colleagues use for the new cultivation system, which takes account of small-scale differences in the landscape. “The benefit of spot farming would be the ability to achieve multiple crop rotations at a time, not only one, as has been the case to date,” Urso continues. Depending on the ground conditions, various crops (rape, wheat and beets) could be sown, taking into account properties of the land such as dips containing pooled water, dry hillocks or other small structures. “That would certainly increase the diversity of species in the fields, as well as saving on fertiliser and pesticides based on single-crop treatment,” Wegener comments.

Increasing intelligence thanks to machine learning

The fact that this is not merely a vision was demonstrated by start-up company Deepfield Robotics back in 2015 with its “Bonirob”, a robot the size of a small car which uses video and lidar positioning as well as satellites to navigate around the field with centimetre-precise accuracy. It is able to distinguish crops from weeds based on their leaf shapes, and removes weeds mechanically using a ramrod rather than with toxic herbicides. Any unwanted weeds are simply rammed into the ground at high speed.

In view of the diverse flora, the Bonirob’s automatic image recognition is key. Professor Dr Amos Albert, Head of Deepfield Robotics, describes the challenge: “The leaves of carrots and camellias are very similar in their early stages, for example.” So the Bonirob has to be taught how to learn and identify leaf shapes. Albert and his team apply machine learning for the purpose. The system captures large numbers of images, in which the Bosch researchers mark the weeds. “In this way, the Bonirob gradually learns to distinguish productive crops from weeds ever more efficiently based on parameters such as leaf colour, shape and size,” Albert explains. But the Bonirob is not yet available on the market as a production vehicle.

Tackling individual weeds in a targeted way

Swiss company Ecorobotix has gone one step further: it has already built an initial production run of its “Jät” robot. The test-phase machines are currently proving their worth on fields in Switzerland, France and Belgium. They are scheduled for official market launch in 2018. The solar-powered robot weighs just 130 kilograms. It works for as long as 12 hours a day with no human operator, and is controlled and configured entirely using a smartphone app. The robot orientates and positions itself using its RTK GPS, its camera and sensors. Its imaging system enables it to align itself to the rows of crops and to detect whether and where there are weeds within or between the rows. It adapts its speed to the density of the weeds as it does so. Two robotic arms target a micro-dose spray of herbicide specifically onto the detected weed.

(Picture credit: Case IH; iStockphoto: Barcin, gusach, juliedeshaies)

Autonomous construction machines like driverless dumper trucks have already proven their readiness for practical application at ore mines around the world. Now work is underway to produce autonomous excavators, though the technology is still in its infancy.

In the late summer of 2016, a project in the Swedish town of Eskilstuna provided a glimpse of the construction site of the future: at the site, Volvo Construction Equipment (CE) demonstrated how an autonomous wheel loader and a driverless dumper truck worked together. The wheel loader handled around 70 per cent of the volume normally loaded by a human-controlled machine. That’s a lot less – though the autonomous construction machines are able to operate round the clock. “The machines are able to perform the same task on a predetermined route time and time again, over protracted periods of time. But the technology is still in its infancy. We are working to devise solutions capable of delivering the safety and performance the market demands,” explains Jenny Elfsberg, Volvo CE’s Director of Emerging Technologies. “We still have a long way to go. So we don’t yet have any plans for implementation on an industrial scale,” she adds. The machine prototypes do not yet communicate with each other, for example. Yet that is vital in terms of avoiding collisions and simplifying efficient material flow. Nevertheless, Elfsberg is certain: “Autonomous machines will improve safety in a hazardous working environment, and eliminate the risk of accidents caused by human error. They will also perform repetitive tasks more efficiently and precisely than a human operator.”

24/7 operation: Drones supply topographical information, autonomous machines then move the required volume of earth precisely and highly efficiently on this basis.

Autonomous construction machines working together

Japanese manufacturer Komatsu is also working on autonomous construction machines. Its “Smart Construction” concept is already well advanced. The excavators and wheel loaders are only partially autonomous so far, however: the excavator operator now merely controls the boom, for example, while the bucket operates automatically. Its height and position are adjusted with the aid of cameras on the excavator and GPS sensors. The system “knows” how much earth has to be moved where. The necessary data is provided by thousands of aerial photographs captured by drones from US manufacturer Skycatch. The Skycatch software uses the images to compute a three-dimensional topographical model, accurate to within three centimetres. Based on several million measuring points, the volume of earth to be moved can then be calculated very much more precisely than using conventional manual methods.

Fully automated iron ore transportation

It will be a while before fully autonomous excavators are available. But fully autonomous trucks are already operating today: Australian mining company Rio Tinto, for example, introduced so-called Autonomous Haulage Systems (AHS) at its iron ore mines in the Pilbara region as far back as 2008. Today the company is the world’s largest owner-operator of autonomous trucks, running 71 autonomous dumper trucks. Actually, the word “truck” does not quite cover it: the Komatsu driverless dumper trucks transporting iron ore at the Yandicoogina, Hope Downs 4 and Nammuldi mines are the height of a three-storey building.

Higher productivity, less risk for staff

The dumper trucks are equipped with powerful computers which control the standard driving functions: starting the engine, accelerating and braking. The navigation system is GPS-based, and the trucks are fitted with distance sensors and collision-avoidance systems in order to identify and avoid any hazards. The vehicles are additionally monitored remotely from an operations centre in Perth, 1,500 kilometres away. Rio Tinto’s Mining Operations Manager at Yandicoogina, Josh Bennett, explains: “What we have done is map out our entire mine and put that into a system, and the system then works out how to manoeuvre the trucks through the mine.” The trucks are programmed to transport their loads as efficiently as possible. And they are a success: since 2008, the autonomous fleet has cut loading and transportation costs at the mines by 13 per cent. “Autonomous trucks reduce employee exposure to hazards and risks associated with operating heavy equipment, such as fatigue-related incidents, sprains and other soft-tissue injuries, and exposure to noise and dust,” says Yandicoogina Mining Operations Manager Josh Bennett.

The future of autonomous driving will not only offer a completely new driving experience, it will also change the entire automotive industry.

Over 1.2 billion people spend more than 50 minutes a day in their cars – a large portion of that time, however, is spent in traffic jams. Wouldn’t it be nice if you could take your hands off the steering wheel during this time and get on with other things? This dream became reality in 2017: this year has seen the presentation of the world’s first production cars to be developed for highly automated driving. Vehicles can now take over driving functions such as parking or autonomously accelerating and braking in traffic jams.

Sharp eye for the surroundings

A basic requirement for automated driving is the ability to reliably perceive vehicle surroundings and evaluate them accurately on the fly. “In order for the system to acquire this information step-by-step, a range of sensors such as radars, cameras, and surround view systems are needed. The aim is to achieve an understanding of the vehicle’s surroundings which is as good as or better than a person’s own understanding. More range, more sensors, and the combination of acquired data with powerful computer systems will help to sharpen the view and is the key to achieving a consistent view of our surroundings,” says Karl Haupt, Head of Continental’s Advanced Driver Assistance Systems business unit.

British car-maker Jaguar Land Rover has taken this a step further. “We don’t want to limit future highly automated and fully autonomous technologies to tarmac,” says Tony Harper, Head of Research, Jaguar Land Rover. “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.” For this purpose, Jaguar Land Rover has combined cameras, ultrasound, radar and lidar sensors in a concept vehicle. These systems not only enable a 360-degree view of the car’s surroundings, but are so highly developed that they can determine surface properties down to the dimension of a tyre width – even in rain or snow. Ultrasonic sensors can also detect the surface conditions within a range of up to five metres so that the vehicle can automatically adjust its traction and driving behaviour when switching from tarmac to snow or from grass to sand.

The car comes to the driver

The sensor system is only one part of the solution; the other is having the intelligence to generate commands from the data collected. This requires cars to be equipped with high-performance control units. The Audi A8 revealed in July 2017, for example, features a central driver assistance controller with deep-learning-based software which constantly froms an image of the surroundings from the sensor data during piloted driving. Daimler is also pushing to develop the software behind fully automated and driverless driving. In April 2017, the manufacturer entered into a development agreement with Bosch to bring fully automated and driverless driving to urban roads by the beginning of the next decade. The objective is to work together to develop software and algorithms for an autonomous driving system. The idea behind this thinking is that the car will come to the driver and not the other way around. Users will be able to conveniently order an automated shared car or robot taxi via their smartphone. The vehicle will then make its way autonomously to the user. “The car as we know it will soon be history,” says Dr Volkmar Denner, Chairman of the Board of Management of Robert Bosch. “Today you use the Internet to book a hotel room; in the future, you’ll arrange your mobility online as well.”

An autonomous fleet can effectively replace a much larger number of private vehicles.

Tapping into new business areas

Car-sharing is one of the big advantages of autonomous cars. David Alexander, Senior Research Analyst with Navigant Research: “Studies have shown that an autonomous fleet can effectively replace a much larger number of private vehicles in a city centre, which represents both an opportunity and a challenge for original equipment manufacturers (OEMs).” On the one hand, according to Navigant, 120 million autonomous cars will be sold between 2020 and 2035. On the other hand, automobile manufacturing is expected to reach its zenith and then decline because of shared cars. Therefore, automotive manufacturers should adapt their business models accordingly and sell additional value-added services, for example. “The more popular autonomous driving becomes, the greater the demand by users for services to meaningfully utilise the time freed up in the car,” concludes Ralf Gaydoul, Partner and Head of the Automotive Center at Horváth & Partners Management Consultants. “If the values were to be added up across all categories of need, this would give rise to a monthly amount of well in excess of 100 euros per driver.” Together with the Fraunhofer Institute for Industrial Engineering (IAO), Gaydoul has studied the willingness of motorists to pay for such services: according to the study, three-quarters of those surveyed would pay for value-added services. The willingness to pay for services is at its highest in relation to communication and productivity. “These services are the most heavily in demand in all three countries examined, though with different variations,” says Dr Jennifer Dungs, Head of the Mobility and Urban System Design Division at the Fraunhofer IAO. “For example, interest in in-car social media services is much higher in Japan than here in Germany (64 per cent compared with 23 per cent).”

(Picture Credit: Continental)

Lorries which automatically drive in convoy – known as platooning – are nearly ready for the market. The first fully autonomous test vehicles are already on the roads. Forwarders and manufacturers are hoping for a significant reduction in operating costs.

Strict legal regulations regarding driving times and safety regulations, personnel shortages, rising operating costs – the transport sector has been struggling with the same problems for a long time. Most of these could be solved through the use of autonomous lorries.

Platooning: The final development stage

“We are getting closer to a point where lorries will be increasingly controlled by technological intelligence, starting on motorways to begin with,” says Norbert Dressler, Senior Partner at Roland Berger and commercial vehicles expert. “This will represent the final stage in an incremental, 15+ year developmental process with a gradual reduction of driver engagement. While driver assistance systems such as ACC or Lane Keep Assist are already implemented in many trucks, automated vehicle operation will be possible in the final stage (level 5 or full automation) under all traffic conditions and potentially no longer needing a driver.” Each stage of higher automation brings with it higher system complexity and increasing costs, ranging from 1,800 dollars per truck to implement level 1 to 23,400 dollars per truck all the way to the final level 5. A key driver behind the high cost is software, which accounts for approximately 85 per cent of total cost. “All manufacturers are already working on new solutions to counteract the pressure of digitalisation and new competitors,” says Romed Kelp, expert for the commercial vehicle industry at global management consulting firm Oliver Wyman. “They all have prototypes on the road and are investing hundreds of millions in digital technologies.” Vehicles from major European manufacturers are already travelling in electronic convoys in initial inter-brand platooning demonstrations. Platooning is an on-board system for road transport where two or more truck-trailer combinations are driven one behind the other a short distance apart, using current technical driving assistance and control systems as well as car-to-car communication. Far from compromising road safety, this actually increases it. The distance between the vehicles is about ten metres or around half a second’s driving time. All the vehicles travelling in the platoon – the whole group of articulated lorries – are connected by an “electronic towing bar”. During the drive, the first vehicle sets the speed and direction of travel. The necessary commands are transmitted to the following vehicles via the car-to-car communication technology. These also send data back to the towing vehicle. A wireless connection with a frequency of 5.9 GHz is used for the car-to-car communication. Diesel consumption and CO₂ emissions can be reduced by up to ten per cent.

-15 % fuel consumption and co₂ emissions is the reduction through platooning
Source: Continental

Autonomous driving on the highway

The first fully autonomous test vehicles are already out on the roads as well: in fact, the world’s first autonomous HGV – the Freightliner Inspiration Truck – received a road-driving permit for the US state of Nevada back in 2015. As soon as the HGV has safely reached the motorway, the driver can activate the Highway Pilot system. The vehicle switches to autonomous mode and adapts to the speed of the traffic. Highway Pilot uses a complex set of cameras and radar systems with lane-keeping and collision-prevention functions to brake, steer and regulate the speed. This combination of systems creates an autonomous vehicle which operates safely under all kinds of driving conditions; for example, the HGV automatically observes the legal speed limit, regulates the prescribed distance from the vehicle in front and uses the stop-and-go function during rush hour. Highway Pilot does not initiate autonomous overtaking manoeuvres – these must be carried out by the driver. The same applies to exiting the motorway and changing lanes.

Platooning: A wide range of savings

Although the costs for the technology are high, the operating costs will decrease with autonomous driving functions: “Fuel and driver cost savings are the main factors in payback of the initial investments,” says Roland Berger expert Dressler. The industry does not have to wait until the final automation stage to achieve savings: fuel cost savings of around six per cent are already possible in level 1 (through platooning, for example). The main cost reduction will come into effect in level 4, where the driver can take required rest breaks during automated driving. Driver costs will thus drop by a further six per cent. In level 5, when long-haul lorries don’t require a driver at all, driver costs will be reduced by as much as 90 per cent. Further savings result from lower insurance costs as automated driving enhances safety: and the number of HGV accidents could drop by 90 per cent by 2040.

(Picture credit: iStockphoto Milos-Muller)

Sensors, computing power and the ability to learn are the technological foundations of autonomous vehicles. The more functions that are taken over by technology, the higher the level of automation – right up to the completely driverless vehicle.

The roots of autonomous vehicles reach further back than is generally assumed: as early as the start of the 20th century, Elmer Sperry developed the first control system controlled by a gyrocompass, enabling ships to be kept on course automatically. Then, in 1928, the first automated aeroplane control system, developed by Johann Maria Boykow, was showcased at the International Air Exhibition in Berlin. However, true autonomous driving requires far more than simply keeping the vehicle on a set course: the vehicle must be able to reach a specified destination independently, without human control or detailed programming. In doing so, it must be able to respond to both obstacles and unforeseen events.

From assistance systems to self-driving vehicles

The path to a fully autonomous system is gradual, with developments on a sliding scale. A worldwide system with six levels for classifying degrees of automation is now recognised throughout the world – it was defined, among others, by SAE International (Society of Automotive Engineers), but is now also used for other vehicle segments. According to this scale, level 0 corresponds to a vehicle without any assistance system, where the driver is solely responsible for all functions. At level 1, the first assistance systems, such as cruise control, support the driver. Partly-automated vehicles with parking and lane guidance systems, which can already carry out automated steering manoeuvres, constitute level 2. At level 3, the vehicle controls itself for the most part, and the driver no longer has to oversee the vehicle at all times. The fully-automated vehicles classed as level 4 can master even high-risk situations without human help, but are restricted to known sections of road. Only at level 5 do we find completely autonomous driving, in every environment and all situations. Within limited areas, such as agriculture, intralogistics, light-rail systems or mining, highly and fully-automated vehicles classed as levels 3 and 4 have been in use for quite some time. However, only level 3 cars are currently found on the roads. The first series-production cars that can cope without drivers in real road traffic, at least under specific conditions (level 4), are set to be on offer from 2020 onwards.

Sensors pick up on the surroundings

In order for a vehicle to reach its destination autonomously, it requires various capabilities: firstly, it must pick up on the environment through which it is moving – otherwise it would simply fall at the first hurdle. To prevent this, autonomous vehicles are equipped with a very wide range of sensors: ultrasound sensors are required for automated driving, particularly for detecting close-up surroundings up to six metres away and at low speeds, for example when parking. Radar sensors provide important information on the environment at a greater distance, through 360 degrees. The main task of a radar sensor is to detect objects and to measure their speed and position compared with the movement of the vehicle on which it is fitted. A relatively new addition is lidar sensors, which “scan” the environment with invisible laser light and can generate a high-resolution 3D map of the surroundings. Video sensors, above all in stereo-video cameras, supply additional important visual information such as the colour of an object. Each of these sensor systems has its strengths and weaknesses. In order to obtain an image of the environment that is as exact and reliable as possible, multiple sensors are used together in autonomous vehicles – depending on the application – and the data from these is “merged” or drawn together.

High-resolution maps via the cloud

As well as the ability to “see” the surroundings, an autonomous vehicle must also be able to navigate. Thanks to satellite navigation systems such as GPS, the vehicles know where they are currently located and can calculate their route based on this information. In doing so, they rely on high-resolution maps that are kept extremely up to date, with these maps not just showing the topology, but also incorporating current events such as traffic jams in a dynamic manner wherever possible. These maps can be stored locally in the vehicle or in the Cloud. In the latter case, a high-performance communication system is particularly necessary, so that the map data can be updated in real time. For example, the 5G mobile telecommunications standard can form the basis for this. It enables a “tactile Internet” that, in addition to transmission rates in excess of ten gigabits per second, guarantees an ultrafast response with a delay of less than one millisecond. With this type of networking, the almost unlimited resources of Cloud computing can be called on to carry out complex calculations involved in analysing the situation or route finding.

Learning as a basis for the correct response

After all, analysing the huge data volumes that are generated by the vehicle’s sensor systems requires considerable computing capacity, as does interpreting situations. Technologies that are grouped under “artificial intelligence” are becoming increasingly important. Machine learning, in particular, is an essential part of an autonomous system: only with this is it possible for vehicles to act intelligently and independently of humans. Through machine learning, autonomous systems can generate new knowledge from data that has been collected and provided, and are able to constantly extend their knowledge base. Without this independent learning, it would be almost impossible to specify appropriate reactions to all theoretically possible situations in programming.

If people can no longer intervene in good time when a malfunction strikes, the technology needs to be especially safe. Special electronics and new testing methods are necessary to ensure the functional safety of an autonomous vehicle.

Confidence in the technology of autonomous vehicles is key to their success. The systems used need not only to be highly reliable, but must also be designed to prevent any failure endangering people or systems in case of a technical defect. “Functional safety” is an umbrella term for the relevant requirements and methods needed to accomplish this. “The automotive industry’s transformation is essentially asking the public to trust their lives to a computer and a machine,” says Zach McClellan, the former baseball star who currently runs the training division at US engineering firm LHP. “Functional safety in practical terms is defined as the steps engineers and organisations take to avoid failures that harm the public,” he adds.

No More Simple Switch-Offs

Achieving safety in autonomous vehicles requires more than simply switching off a system; drones would fall from the sky, while cars would lurch to an emergency stop. Consequently, critical systems are kept on temporarily, even after a fault has occurred. An emergency plan is needed; this must be defined as soon as an autonomous vehicle is in development. There are already standards today to define the required development and production methods for this. For instance, ISO 26262 applies to road vehicles, ISO 25119 to agricultural vehicles and ISO 15998 to construction machinery.

Safety Starts with the Electronic Components

Developing a functionally safe vehicle starts with the individual components, especially the semiconductors. These are built into the systems for environment recognition, and they contain the accumulated information from the various sensors, calculate the necessary control commands and determine how a vehicle is to operate. A malfunction could have disastrous consequences for the autonomous vehicle, for any occupants and for the environment. The semiconductor industry has therefore now created special semiconductors for use in automated driving with their architecture already developed based on an audited, ISO 26262-compliant process. The processors must provide a high degree of reliability and must continue to perform their tasks safely in the event of vibration, radiation (such as sun rays) or heavy temperature fluctuations, all of which are typical ambient conditions for their use in vehicles. Functionally safe processors self-monitor while executing processes. In these multithreading systems, each instruction is processed in parallel in two or more cores or processes. The results are then compared in real time by hardware logic. A discrepancy in the results means that a fault has occurred within one of the lines of calculation. If this is the case, the system issues a fault message or triggers a pre-set emergency action for this circumstance.

New Test Methods Needed

Safe electronics is one thing, but testing is also needed to demonstrate the functional safety of a vehicle. However, there are not yet any methods or test certificates to this effect. Testing agencies such as the TÜV are engaged in various projects to define new standards and testing criteria for autonomous driving, seeking to establish a basic safety level for the new technology’s practical application. In the course of this, it is impossible to reproduce on a test route all the potential situations encountered while operating an autonomous vehicle. As a result, additional, new methods are needed to test the effectiveness of the safety systems. Simulations play an important role in this and will be crucial as an accompaniment to real tests.

In addition, networked autonomous vehicles have to contain new technologies from different suppliers and industry sectors, which must be integrated into end-to-end systems and validated within the networked vehicle ecosystem. To this end, vehicle engineering service provider FEV has specially established a global Center of Excellence for the development of smart vehicles. Stephan Tarnutzer, Vice President of Electronics at FEV North America and Head of the Center, emphasises: “To keep control of the wide variety of interactions inside, outside and around the vehicle that occur as a result, it is essential to take the whole system into account at every stage of development.”

(picture credit: iStockphoto Edi_Eco)

Detecting small or fast-moving objects – that is where lidar excels. The laser-pulse-based technology is increasingly being seen as a valuable addition to radar and camera systems.

Various sensor technologies are being used in autonomous vehicles in order to capture as broad an image of the surrounding area as possible, while minimising the risk of error. Whereas radar technology is based on high-frequency electromagnetic vibrations, lidar – as it is termed – works with laser beams to determine the distance of objects within close range of the vehicle. Lidar is primarily used to detect smaller objects on roads.

However, roads are not this sensor technology’s only area of application: “Everyone is talking about autonomous vehicles, but autonomous systems are also trending in the industry,” emphasises Dr Dirk Rothweiler, CEO at Berlin-based company First Sensor. The company produces what are known as avalanche photodiodes, which detect laser reflections in lidar systems. Lidar technology is already a technical standard in a whole host of industrial applications, as Rothweiler explains: “When it comes to industrial length measurement, our customers have been relying on lidar for years – to measure speed or to monitor safety areas for machines, for example. A growing field of application is autonomous systems. These are more readily implemented in industrial settings where surroundings and influencing factors are controllable.” Here, lidar systems make sure that mobile robots do not collide with the people working in production systems or that autonomous transport and logistic systems can take on complex tasks such as the loading of pallets, for example.

Laser diodes as a basis

Lidar stands for “light detection and ranging”. Its basic principle is time-of-flight measurement: a very brief laser pulse is emitted, makes contact with an object, is reflected, and then recorded by a detector. The laser beam’s travel time gives the distance of the object. Incredibly high-performance infrared pulse laser diodes with a short switching time form the technical basis of lidar systems. With an optical pulse power of approximately 25 watts and a spectral range of 905 nanometres, their pulses go virtually unnoticed by humans. They are completely safe for the human eye.

Scanning lidar systems use a laser beam to scan the area around the vehicle horizontally above a certain angular segment and create a high-resolution 3D map of the surroundings. Initially, the deflection of the laser beams in scanning lidar systems was implemented with mechanically driven mirrors. However, miniature laser scanning modules with integrated silicon-based MEMS micro-mirrors – which have appeared on the market in recent times – are more cost-effective, compact and robust. The micro-mirrors combine and fine-tune the laser beams. Up-to-date systems with solid-state lasers achieve ranges of more than 200 metres and a high resolution of less than 0.1 degrees.

An accompaniment to other sensor systems

The major advantage offered by lidar over other sensor systems is its extensive data acquisition: lidar collects more information from individual data points than any other system – x, y and z coordinates, period and degree of reflection to name but a few. Reflective surfaces such as road signs or even road markings provide a stronger signal, allowing lidar to detect such surfaces faster. A camera would also be capable of this. It could, however, be dazzled by backlight or sunlight and can only be used to a limited extent at night-time or in conditions of poor visibility. However, lidar systems also only deliver limited data during torrential rain. This is where radar impresses, as it is capable of “seeing through” objects. Yet to do this, radar systems do not possess the high resolution required to register objects when they are small or great in number and while moving at high speeds. Lidar, on the other hand, can also clearly distinguish small objects.

“Even the most aggressive start-ups do not expect lidar to be the silver bullet for the detection and perception of autonomous vehicles,” says James Hodgson, Senior Analyst at ABI Research. “Still, the natural characteristics of this technology are very well suited to that of radar and cameras, which until now have constituted core elements in obstacle detection.” In his view, however, the high costs in particular still prevent their wider application. Yet it is to be noted that the systems are becoming ever more affordable: Dutch company Innoluce, which is now part of Infineon, is planning to bring automotive lidar systems to the market for less than 100 dollars in the future.

(Bildnachweis: iStockphoto: Jevtic, Kaphoto, photobac, sturti)

As connectivity advances, protection of vehicles against attacks from cyberspace is becoming ever more important. The only way to assure such protection is with a comprehensive cybersecurity concept, integrated into the development process right from the start.

Autonomous vehicles are nothing other than mobile computers with innumerable communications interfaces – exchanging data with infrastructure or other vehicles, updating on-board software, or accessing real-time navigation maps. However, the increasing number of interfaces aboard vehicles also means there are more potential vulnerabilities for cyber-attacks. “Hundreds of articles on autonomous driving appear in the media every day, but almost none mention the elephant in the room: auto-makers do not yet have a reliable defence against cyber-threats. Period. One serious hack could immediately halt progress in automated driving. But we have the remedy,” says David Uze, Trillium’s CEO. Consequently, the Japanese company founded in 2014 is planning to launch a software-based, multi-layer security solution onto the market in 2018 – at a tenth of the cost of existing solutions. “Since defence must continually evolve, our infrastructure will deliver Security as a Service (SaaS) via real-time-update platforms that auto-makers or insurers can on-sell to car owners.”

There is no wonder-weapon

Whether a purely software-based add-on cybersecurity solution is enough on its own to protect a vehicle against the highly sophisticated attacks of modern-day hackers is questionable, however. “There is no wonder-weapon capable of protecting cars against sophisticated dynamic cyber-attacks,” stresses Ofer Ben-Noon, co-founder and CEO of Israeli vehicle cybersecurity company Argus. “Our customers need protection on multiple levels, so as to be prepared for any conceivable scenario.” The company offers a multi-layer security solution for connected vehicles: it starts with the infotainment and telematics devices, encompasses the internal network communications, and also extends to selected electronic control units (ECUs). ECU security protects vital systems such as the brakes, assistance systems and other key units against attack.

Cybersecurity as part of the development process

Cybersecurity should be integrated into the development process for an autonomous vehicle right from the start as a matter of policy. That principle is affirmed in a manifesto published by FASTR, stating that cybersecurity should begin at the very foundation of the vehicle’s architecture and be coordinated throughout the supply chain. In that way, a connected vehicle can be made “organically secure”. FASTR – which stands for Future of Automotive Security Technology Research – is a neutral, non-profit consortium established in 2016 by three companies: Aeris, Intel Security and Uber.

The provision of such all-round protection should be approached according to the bottom-up principle: the security concept starts with a high-security core (root of trust), implemented by a physically secured cryptographic device such as a Hardware Security Module (HSM). It securely holds cryptographic keys and algorithms, protecting them against being read, modified or deleted. The keys are in turn used to detect and prevent manipulation of the ECU firmware. This then also ensures that the software-based security functions in the firmware can be used safely for on-board communications. At the same time, it means on-board networks on different security levels are reliably isolated from one another – preventing access to the engine management system via an entertainment interface, for example. The secure on-board network this creates then also in turn permits secure communication with other vehicles or infrastructure.

Autonomous vehicles are protected against the many types of cyber-attack by multi-layered security concepts.

Hardware security module

More secure E/E architecture

Secure on-board communication

Secure vehicle IT infrastructure

Secure control unit

Secure V2X communication

Security systems must be updateable

The challenges of cybersecurity are changing continually, however. Security experts are continually having to confront new conditions and methods of attack. That means a vehicle’s on-board security systems must be capable of being regularly updated throughout the product life-cycle. Consequently, a security solution for autonomous vehicles should be designed right from the start in such a way that vital security parameters and functions are held in modifiable storage devices (such as HSMs with firmware update facilities). Also, available IT resource capacities should not be fully utilised right from the start – leaving adequate spare storage space, for example. With appropriate update mechanisms, new security patches can then be downloaded “over the air” – and the vehicle can stay protected against attacks from cyberspace even ten years down the line.

“Car hacking is a very real threat that will continue to increase as we move towards greater connectivity and autonomous vehicles, with more and more new technologies becoming part of the Internet of Things,” says Saar Dickman, Vice President, Automotive Cyber Security at Harman. “Automotive cybersecurity is an increasingly critical piece in enabling connectivity and autonomous driving.”

(picture credit: istockphoto: Kodochigov)

Thanks to autonomous navigation, transport robots inside factories and production facilities are now becoming as versatile and flexible as a human solution.

The ability to navigate autonomously will revolutionise the world of in-house logistics. That’s what the market analysts at IDTechEx say, anyway. “Automated guided vehicles barely made a dent in this industry. This is because their navigational rigidity put a low ceiling on their total market scope,” says Dr Khasha Ghaffarzadeh, Research Director at IDTechEx. “Autonomous mobile robots are radically different, however, because they will ultimately enable automation to largely keep the flexibility and versatility of human-operated vehicles.” It is his opinion that mobile robotics in material handling and logistics will become a 75 billion dollar market by 2027. It will then more than double by 2038.

Immediate increase in productivity

We are still very much in the starting phase. Mobile robots are still relatively expensive. However, there are already some examples of practical applications. A Jungheinrich APM (Auto Pallet Mover) has been in use at Hero España, a Spanish manufacturer of baby food and jams, since 2011. The APM is a standard high-level order picker or pallet truck that has been modified with an automation package, transportation control software and a personal protection system. The vehicle transports pallets made ready in the automated warehouse to the picking stations and from there to the dispatch area. While the vehicle is in motion, a rotating laser scans the surrounding area, using reflectors that are positioned along the route to determine the exact position of the Auto Pallet Mover. The layout of the warehouse and the routes taken by the vehicle can be changed in a matter of minutes without having to adjust the reflectors. Sensors on the forks also enable the pallets to be picked up or set down up to a height of two metres in autonomous mode. Juan Francisco García Gambín, Warehouse Manager at Hero España: “Since the Jungheinrich APM was commissioned, the increase in productivity has been noticeable from the very first day as a result of the autonomy with which the machine carries out its tasks.” Soon after the system was commissioned, Hero España started to notice higher efficiency levels in its intra-logistical processes as well as a marked reduction in errors associated with goods-in, goods transfer and dispatch tasks.

Transport robots are a milestone in digitalisation

Mobile robots are also being used for transportation purposes at the BMW plant in Wackersdorf, Germany. Ten of these Smart Transport Robots move components around in warehousing and production. Able to measure the distance to wireless transmitters and equipped with an accurate digital map of the production hall, the robot can calculate its exact position and this route to its destination. The battery-powered transmitters are mounted on the walls of the hall. The solution is flexible and can be extended to other logistics areas relatively inexpensively. The vehicle’s sensors also enable it to identify and respond to critical situations so that it can share the route with human personnel and other vehicles. A future phase of development will see the introduction of a 3D camera system to make navigation even more accurate. “The development of the Smart Transport Robot is an important milestone for the BMW Group when it comes to digitalisation and autonomisation in production logistics. This innovation project makes an important contribution to the agility of the supply chain in logistics and production. It enables the supply chain to adapt to changing external conditions quickly and flexibly,” comments Dr Dirk Dreher, Vice President of Foreign Supply at the BMW Group.

Despite Transport Robots: Challenging tasks are the preserve of human beings

Tobias Zierhut, Head of Product Management Warehouse Trucks at Linde Material Handling, believes that warehouse workers will benefit too: “In the future, automated vehicles will be able to take over simple tasks that are repeated at regular intervals. This will enable human personnel to concentrate on more challenging and complex tasks.” Since 2015, Linde Material Handling has been working in close collaboration with Balyo, a French robotics specialist, on developing and producing automated vehicles. The vehicles do not rely on reflectors, induction cables or magnets installed in the warehouse. As part of the installation process, a map of the area in which the vehicles will be used is created. This information is transferred to the vehicle, which automatically positions itself using invisible laser-controlled geonavigation.

Stocktaking with a drone

Balyo and Linde have also developed a drone to be used for stocktaking in warehouses. Approximately 50 centimetres wide, equipped with six rotors, a camera, a barcode scanner and a telemeter, the “Flybox” stocktaking drone slowly makes its way up the front of each individual rack, taking a photo of every pallet storage location and capturing the barcodes of the stored goods. “The decisive new feature of this invention is that we use the drone together with an autonomous industrial truck,” says Zierhut. During its stocktaking mission, the drone is guided by an automated pallet stacker. The two vehicles are connected via a self-adjusting cable. With this innovative coupling, Linde has resolved two challenges that have hitherto impeded the use of drones in warehouses: power supply on one hand (drone batteries usually only last 15 minutes), and the localisation under the hall roof without GPS reception on the other.

The technology that will allow autonomous aircraft to fly without pilots is here and is already undergoing successful testing. This may mean a whole new way of travelling, in particular when it comes to mobility in cities.

Now that we have driverless cars and increasingly intelligent drones, an autonomous aircraft without a pilot on board hardly seems visionary any more. In fact, as far back as some ten years ago, the IFATS (Innovative Future Air Transport System) project demonstrated the technical feasibility of aircraft without pilots, although it was thought at the time that this would not happen before 2050. The biggest hurdle was considered to be passenger acceptance. However, acceptance levels are likely to increase every time there is a pilots’ strike or an aviation accident, because up to 90 per cent of aviation accidents are attributable to pilot error. This explains why all large aircraft manufacturers – along with many small start-ups – are working on unmanned aircraft.

90% Pilot Error: The majority of all aviation accidents can be traced back to human error. It is hoped that autonomous systems will considerably reduce this figure.

Nothing escapes the electronic eye

British manufacturer BAE Systems started testing a pilotless aircraft at the end of 2016. A standard Jetstream 31 plane was converted to provide a flying test bed. The machine has an antenna which detects transponder signals from other aircraft as well as a cockpit-mounted camera acting as an electronic eye. This links to the aircraft’s computer systems and enables the Jetstream to “see” potential hazards, even if no signals are being emitted. The electronic eye of the Jetstream can also recognise different cloud types and, if needed, plot a course that allows evasive action from challenging weather conditions. Pilots on board the aircraft remain responsible for take-off and landing, but as soon as the Jetstream is in the air, it flies autonomously. It has already clocked up almost 500 kilometres of flying at a height of approximately 4.5 kilometres. “Our priority, as always, is to demonstrate the safe and effective operation of autonomous systems,” explains Maureen McCue, BAE Systems’ Head of Research and Technology for the military aircraft and information business. “The trials will give us technology options that could be applied to our own manned and unmanned aircraft, as well as potentially enabling us to take some new unmanned aircraft technologies to market.”

Requiring continuous corrections about all three axes, a helicopter is much more difficult to control than a fixed wing aircraft like the Jetstream. Yet for this type of craft too, the first test flights are already under way. In June 2017, Airbus Heli-copters started test flights with a preliminary study involving the VSR700. The prototype of the light optionally piloted vehicle (OPV) helicopter is due to take off in 2018. Optionally piloted means that the machine can fly both autonomously and with a pilot. “The OPV is able to autonomously take-off, hover and perform stabilised flight and manoeuvres,” said Regis Antomarchi, Head of the VSR700 programme at Airbus Helicopters. This phase of flight trials with a safety pilot will focus on refining the automatic flight control system aboard the helicopter, eventually leading to fully autonomous flights without a safety pilot.

A new dawn for local transport 

Airbus actually wants to go much further with autonomous aircraft. The Group has a number of visions associated with this technology in its test laboratories, with the focus being on the urban traffic of the future. A3, a subsidiary company based in Silicon Valley, is currently working on a project called Vahana. Vahana is a self-piloted flying vehicle platform for individual passenger and cargo transport – an air taxi, essentially. “The ability to be transported safely and quickly through a city in a self-piloted aircraft is no longer science fiction,” says Rodin Lyasoff, CEO of A3. “Advances in propulsion, battery performance, air traffic management, autonomy and connectivity mean that this mode of transportation is capable of benefitting millions of people in years, not decades.” In his view, the only remaining challenges to be overcome are associated with reliable sense-and-avoid systems, which detect hazards or obstacles and invoke evasive action. At the current time, there are not yet any fully developed solutions for aviation. “Urban air mobility will significantly change how we live and work for the better, but bridging from feasibility to reality will require close cooperation between the public and private sectors to define appropriate regulations,” says Lyasoff. Another Airbus vision for urban air traffic is the City Airbus. In terms of technology, this aircraft is comparable to a small drone. Like a drone, it relies on several electrically driven propellers to stay in the air. Where it differs from a drone is that it will be designed to carry several passengers. To speed up its time to market, the machine will initially be controlled by a pilot. The intention, though, is that it will fly autonomously at a later point in time and passengers will use an app to hail a ride. A feasibility study has already been successfully completed.

(Picture credit: Airbus)

Radar Technology is nothing new. Yet in view of current developments, radar technology in autonomous vehicles is becoming ever more precise and powerful.

Radar is a fundamental part of the automated driving equation,” explains Peter Austen, Global Portfolio Director, Driver Assist Systems at ZF’s Active & Passive Safety Technology division – or ZF TRW for short. “When combined with cameras, intelligent control units and actuators, it can help to enable partially automated driving functions.” ZF TRW has been designing and developing radar in Brest since 1999.

Luxury early on

As early as the start of World War II, radar technology was being used on board aeroplanes and ships. Despite this, its first appearance in cars wasn’t until 1998, when Mercedes-Benz introduced a distance radar in the S-Class. Still, the cost of this technology was very high at the time, owing to the fact that until 2009, the required semiconductors could only be manufactured using gallium arsenide (GaAS) – an expensive and difficult material to process. A further disadvantage is the low level of integration; the ability to combine an increasing number of functions on an unchanging chip area. Only with the manufacturing of sensors in silicon germanium technology (SiGe) – the most frequently used materials for semiconductor manufacturing – did the systems become affordable. This enabled tried-and-tested standard processes to be implemented in mass production for the first time. What’s more, a whole host of functions could now be condensed onto just two SiGe components, where previously up to eight GaAs chips had been required.

Detection is becoming ever more powerful

Radar systems applied in autonomous vehicles work with millimetre precision – nowadays in the range of either 24/26 GHz or 77/79 GHz. This means that high resolutions are possible for detecting objects and determining their position and movement with centimetre accuracy. When compared to other technologies, such as camera-based methods, the radar works reliably even in conditions of poor visibility such as snow, fog, torrential rain and dazzling backlight. What’s more, the complete systems are not much bigger than a matchbox.

One fundamental difference can be drawn between two types of radar: the frequency-modulated continuous-wave (FMCW) and the impulse radar. Unlike an impulse radar, which emits only one pulse, FMC radars transmit pulses continually. With the FMCW method, the signal is modulated over the entire range during transmission, meaning that the frequency varies over time – this is called chirp. This chirp is repeated in cycles and enables FMC radars to simultaneously measure the absolute distance between the transmitter and object in addition to the differential speed between the two. The devices have admittedly had a weakness – until now: if objects approach at different speeds, it is possible that the radar might “overlook” one of them. Previous devices could therefore only reliably detect objects up to a relative speed of 50 km/h. One solution is to increase the modulation rate. What is referred to as fast chirp modulation (FCM) brings about an increase in the accuracy of distance measurement, enabling a wider range of target object speeds to be covered. Yet an increase in outside temperature causes the pulses of standard CMOS signal generators to slow, resulting in errors. To tackle this issue head-on, Fujitsu released a CMOS-based millimetre-wave signal generator in late 2016, which is capable of maintaining its modulation rate reliably and precisely even at temperatures of 150° Celsius. This allows detection errors to be reduced and even objects approaching the vehicle at a relative speed of 200 km/h are reliably detected.

Over 100 years old

Yet radar technology is in fact much older: it was as early as 1904 in Düsseldorf that the German engineer Christian Hülsmeyer developed the first practical application for the reflection of electromagnetic waves on objects – the Telemobiloscope. Similar to modern radar sensors, it transmitted focussed electromagnetic radiation – radio waves. In modern radar systems, analysing the reflected radiation allows objects to be detected along with their respective distance and speed.

Radar navigation

Despite all this, radar devices are not used in autonomous vehicles merely to detect and locate objects. At some point in the future at least, radar is intended to be used for navigation. With this in mind, Bosch and the Dutch map and traffic information provider TomTom have now become the first to create a high-resolution map with a localisation layer using radar reflection points – albeit solely for road vehicles. So far, video data has been used for this purpose. Bosch’s “radar road signature” is made up of billions of individual reflection points. These are formed everywhere that radar signals hit – for example, on crash barriers or road signs – and reproduce the course a road takes. Automated vehicles can use the map to determine their exact location in a lane down to a few centimetres. The huge advantage of the radar map is its robustness: localisation with the radar road signature also works reliably at night and in conditions of poor visibility. Moreover, only five kilobytes of data are transmitted to a Cloud per kilometre. This data volume is at least twice as high using a video map. It is expected that by 2020 at the latest, the first vehicles will provide data for the radar road signature in Europe and the U.S. “Cars arriving on the market in years to come with the assistance functions of tomorrow will be running the map for the automated vehicles of the future,” says Bosch Board Member Dr Dirk Hoheisel.