The widespread use of sensor networks and drones will also enable what has in principle already been achieved with intelligent points. In other words, predictive maintenance which allows maintenance and repair to be tailored to current conditions and requirements. Reliability is no longer based on safety margins, but on precise, up‐to‐date knowledge of how long a part of the infrastructure could still carry on working reliably.
Maintenance and Planning with the Aid of Augmented Reality
Augmented reality (AR) technology will take on a special role for two reasons. The HPA has collaborated with the University of Hamburg to develop an AR app. A smartphone’s camera is held over a map of the port to show the location and functions of sensors and the IT systems they are connected with. In this case, the app is a type of meta information instrument regarding the systems and tools whose purpose is to enhance the infrastructure. Other applications for the app are conceivable. For example, underground pipes and lines, or networks of all types could be shown in detail on a map of the port.
Furthermore, in the mid‐term AR will support mechanics carrying out special work on the infrastructure and under water. The technicians will have display devices which superimpose and complement the real image with additional information, for example the detailed design plan of a lock, where otherwise only a surface covered in sediment would be seen. What is more, the exact demonstration of work by video also serves as instructions for almost all repairs. Local manpower could then carry out this work immediately instead of possibly having to wait for specialists for a long time.
AR will play a third role in construction projects. Scheduled construction and infrastructure can be visualised at the design stage already in the context of real infrastructure. In fact, when planning a new cruise terminal, three‐dimensional views have already been projected into an image of the real environment, including the access roads, in order to assess traffic management alternatives.
38.3 The Importance of Simulations and Big Data to the Port
38.3.1 Optimising Logistics and Traffic Flows
Thanks to satellite and positioning technology, traffic operations in the port, in particular container transports, are becoming increasingly more efficient, faster and less prone to disruptions. But the need for further, advanced measures is still enormous. Because port business is continuing to grow unabated, one question will continue to be relevant: how can containers be directed optimally through the port and what is the best way to design intermodal interfaces between the water‐borne, rail‐ and road‐bound traffic?
In the future, this issue will increasingly rely on big data to provide the answers. On the one hand it is a question of the vast and inexorable growth in data which is gained from logistical procedures and the operation and management of the infrastructure with the aid of sensors and traffic and infrastructure control rooms. A large proportion of this information will be movement data which is supplied by Smart Tags on containers and vehicles. On the other hand, it will also be necessary to use complex sensors to work out how to enhance the intermodal interaction of shipping, rail‐ and road‐bound traffic from commercial and ecological standpoints.
38.3.2 Sediment Management
Big data analyses and simulations are also applied to sediment management, in other words preventing, controlling and removing deposits in the fairway. Due to larger and larger shipping with deeper and deeper draughts, this task is strategically important for the development of the Port of Hamburg.
In partnership with the Federal Waterways and Shipping Administration, the HPA is already analysing a variety of effects in the tidal Elbe to understand how sediment deposits occur. In future, the data will be much more comprehensive and detailed. Underwater drones will constantly measure the fairway and record parameters such as speeds of currents and the water temperature. These measurement results can be compared with other information, such as meteorological data, to detect interdependencies which would not have been uncovered with conventional analysis methods, but which play a pivotal role in sediment management.
38.3.3 Developing Data‐Driven Business Models
The digitisation of traffic, logistics and infrastructure at the Port of Hamburg creates the cornerstones for developing data‐driven business models. The data collated from all processes in the port contain values which need to be developed to the benefit of people and businesses.
Consequently, as part of the smartPORT initiative, the port industries and the HPA are collaborating with partners from the IT industry to set up a virtual market place to interconnect the port. The idea is that in the future software applications, services, apps and data are to be offered here. Apps with information on available parking spaces in the area surrounding the port, repair workshops for trucks, or an ordering service for supplies on inland waterway vessels are conceivable.
38.4 Outlook – Development of the Port into a Business and Science Hub
With these types of data‐driven business models, the Port of Hamburg also has the opportunity to evolve from a handling hub to a research, development and production site and therefore a digital market place. It is not suitable for industries requiring vast amounts of space, but does offer excellent opportunities for research‐focused and technology‐centric companies. Ideal traffic conditions, a very powerful network infrastructure with gigabit transmission rates, as well as research facilities of international repute, such as HafenCity University Hamburg and the Kühne Logistics University, create the conditions for innovative companies, start‐ups and established businesses to thrive – and not just on digital market places.
References
1.
ISL, [Online]. Available: http://www.hamburg-port-authority.de/de/presse/studien-und-berichte/Documents/Endbericht_Potenzialprognose_Mai2015_5.pdf. [Accessed 29 08 2016].
2.
Hamburg Port Authority, [Online]. Available: https://www.iaph2015.org/downloads//smartPORT-Brosch%C3%BCren/broschuere_smartportlogistics_web.pdf. [Accessed 27 07 2016].
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Dakosy, [Online]. Available: https://www.dakosy.de/en/solutions/port-community-system/import-message-platform. [Accessed 27 07 2016].
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BMWI, [Online]. Available: http://www.bmwi.de/BMWi/Redaktion/PDF/I/innovative-seehafentechnologien. [Accessed 27 07 2016].
© Springer-Verlag GmbH Germany 2018
Claudia Linnhoff-Popien, Ralf Schneider and Michael Zaddach (eds.)Digital Marketplaces Unleashedhttps://doi.org/10.1007/978-3-662-49275-8_39
39. An Overview of Technology, Benefits and Impact of Automated and Autonomous Driving on the Automotive Industry
Walter Brenner1 and Andreas Herrmann1
(1)University of St. Gallen, St. Gallen, Switzerland
Walter Brenner (Corresponding author)
Email: [email protected]
Andreas Herrmann
Email: [email protected]
39.1 Introduction
Automated and autonomous driving has become a topic, which has gained broad attention in the public during the last few years. The handling of the update for automated driving, which has been available for the Tesla S for approximately one year, demonstrates the intensity and emotionality of the public discussion. Immediately after the availability of this functionality euphoric reporting started. Autonomous driving with the Tesla S and the download of the software to the vehicle overnight was hailed by the press and by many potential customers as a masterpiece of innovation [1]. Tesla was incorrectly named as the pioneer of autonomous driving. Vehicles built by Audi, BMW or Daimler are more advanced in development and covered large distances completely autonomously or with speeds up to 240 km/h on closed racing circuits at a much earlier time [2]. However, Tesla and
Google’s small cars with panda faces dominate the discussion about autonomous driving [3]. From the start, it was predictable that automated and autonomous driving would lead to serious accidents, just the same as traditional driving. Very quickly, videos of the Tesla S appeared on YouTube, where it showed it being driven completely driverless, against the provisions of Tesla [4]. When “expectedly” information about a fatal accident with a Tesla S, which was travelling autonomously, appeared in June 2016, reporting tipped in the other direction [5]. The previously positive reporting changed to negative almost overnight. The fact that the driver of the Tesla S used the vehicle contrary to the provisions of Tesla played merely a subordinated role in the public discussion. Only a fact‐based and unemotional discussion can indicate the uses and dangers of automated and autonomous driving, and establish a base for future‐oriented decisions. This article seeks to make a contribution to this discussion. It is based on the fundamental statement that the uses of autonomous vehicles exceed the risks, and that autonomous driving as part of the digitalization of vehicles is an unstoppable development. The competitiveness of the European automobile industry will be determined decisively by the way digitalization is handled. In view of the dominance of American companies, especially the so‐called internet giants such as Amazon, Apple, Google and Facebook, the discussion about automated and autonomous driving is a central one for the industrial location Europe.
Against this backdrop this article deals with the different aspects of automated and autonomous driving. In the first section we define automated and autonomous driving. The second part of our article is concerned with the uses of automated and autonomous vehicles. The third part shows changes in the vehicle, the traffic‐related aspects and changes in the IT infrastructure. The last section handles changes in the automobile industry, and future developments in the mobility sector.
The article is based on discussions with experts and extensive literature analysis on the topic “Automated Driving”, which was carried out in cooperation with Audi AG between July 2015 and August 2016.
39.2 Automated & Autonomous Driving
The current development of vehicles, which are primarily steered by drivers to autonomous vehicles, i. e. completely self‐driving vehicles, can be divided into several developmental steps. The Society of Auto Engineers [6] and the respective department in Germany [7] has provided a structuring for those development steps. However, the model of the National Highway Traffic Administration (NHTSA), the civil regulating authority of the USA, has established itself as a “de‐facto standard” worldwide [8]: No‐Automation (Level 0): The driver is in complete and sole control of the primary vehicle controls – brake, steering, throttle, and motive power – at all times.
Function‐specific Automation (Level 1): Automation at this level involves one or more specific control functions. Examples include electronic stability control or pre‐charged brakes, where the vehicle automatically assists with braking to enable the driver to regain control of the vehicle or stop faster than possible by acting alone.
Combined Function Automation (Level 2): This level involves automation of at least two primary control functions designed to work in unison to relieve the driver of control of those functions. An example of combined functions enabling a Level 2 system is adaptive cruise control in combination with lane centering.
Limited Self‐Driving Automation (Level 3): Vehicles at this level of automation enable the driver to cede full control of all safety‐critical functions under certain traffic or environmental conditions and in those conditions to rely heavily on the vehicle to monitor for changes in those conditions requiring transition back to driver control. The driver is expected to be available for occasional control, but with sufficiently comfortable transition time. The Google car is an example of limited self‐driving automation.
Full Self‐Driving Automation (Level 4): The vehicle is designed to perform all safety‐critical driving functions and monitor roadway conditions for an entire trip. Such a design anticipates that the driver will provide destination or navigation input, but is not expected to be available for control at any time during the trip. This includes both occupied and unoccupied vehicles.
Computer‐assisted driving at level 1 and 2 is described as automated driving, while driving at level 3 is designated to be highly automated driving. The term autonomous driving is reserved for driving at level 4.
Automated and autonomous driving at the experimental stage is already reality today. Numerous vehicles already have relatively extensive packages of assisting systems, which correspond to level 1 or level 2. Examples of these assisting systems are emergency braking systems, lane or park assistant systems, and automatic vehicle interval control, sometimes called “adaptive cruise control”.
Automated driving at level 3 will probably be feasible in the next few years, for example with the highway assistant, which will probably be on the market in 2017 or 2018, for instance with the Audi A 8. Autonomous driving is currently taking place at low speeds on closed off sections or testing grounds, or with special permissions in public transport. Google is testing autonomous vehicles in the vicinity of Mountain View. A vehicle made by Audi drove autonomously from San Francisco to Las Vegas [9]. A further vehicle developed by the automotive component supplier Delphi drove from the west coast to the east coast [10].
Even optimistic estimates go on the assumption that autonomous vehicles will, at the earliest, go into production between 2025 and 2030 [11, p. 8]. Against this background, many statements and reports on the chances and dangers of autonomous driving are to be viewed as speculation rather than based on facts and research. Major leaps in development are not to be expected. The Apple iPhone and the ensuing development it created, for example the app worlds, among them being Apple and Google, who initiated an app explosion, prove that unexpected disruptive developments are possible. Like these developments, app worlds have developed disruptively along new technical possibilities in the last ten years, automated and autonomous driving will steadily continue to develop over the next years.
There could possibly be resistance against new technologies. The past has proven that organized societal resistance can lead to the end of a technology, in spite of expert opinions, as can be seen from the example of nuclear energy, to name just one. The philosopher Nida‐Rümelin demands, against this backdrop, an open discussion about the chances and dangers of automated and autonomous driving [12, 13]. Also against this background, the Tesla competitors should participate in the discussion about accidents through automated driving in a factual and businesslike manner. The discussion about the so‐called dilemma situation should also be conducted at a broad societal level. When talking about dilemma situations we mean situations in which a robot must decide between two or more catastrophic situations. A classical question is, for example: “Will an autonomous vehicle, when faced with the situation of an unavoidable driving maneuver, run over a pedestrian or will it collide with an obstacle thus endangering the driver”. Ultimately, these decisions are based on philosophical discussions, which have been conducted for years, on the so‐called “Trolley Problem”. From today’s view, it remains a thought experiment as to how an autonomous vehicle must and will react to a dilemma situation. Nevertheless, the automobile industry will have to face this discussion and forward its assessment and possible solutions at a broad societal level. The discussion as to the feasibility and future of automated and autonomous driving in the US senate [14] and many parliaments worldwide and round‐table discussions [15] in Germany all offer constructive contributions to these discourses.
Automated and autonomous driving does not only concern vehicles carrying persons, although these are currently often at the center of discussion. The starting point of the development of autonomous vehic
les were two competitions run by DARPA, part of the American Ministry of Defense, in a desert‐like area in the years 2004 and 2005, and a challenge in an urban area in the year 2007 (Urban Challenge [16]) The military continues to be an important driver in the development of automated and autonomous driving. Moreover, there is intensive work being done worldwide on automated and autonomous trucks [17–19], trucks for farming [20] and special vehicles, for example for mining [21].
39.3 Benefits of Automated & Autonomous Vehicles
The most important benefits of automated and autonomous vehicles is the reduction of the risk of accidents. Every year more than 1.2 million people die as a result of traffic accidents: In Germany approx. 3500 people die in traffic every year and more than 300,000 are injured, many of them seriously. According to coinciding estimates approx. 90% of these accidents were caused by human error [22]. Automated and autonomous vehicles, i. e. robots, work without error as long as they are programmed correctly. Robots are always attentive, never tire and carry out the programmed maneuver under all circumstances. In view of this, it can be assumed that the number of accidents will decrease with an increasing degree of automation. The higher the level of development of these robots, the more complex the traffic situations they can recognize, compute and master. Already currently the figures issued by insurances indicate that, for example, the automatic emergency brake assistant reduces rear‐end collisions by 38%, the lane keeping assistant reduces accidents by 4.4% and the lane changing assistant by 1.7%. Whoever is driving on the highway using the adaptive cruise control knows how comforting it is when the robot “thinks in advance” and initiates a braking maneuver if another vehicle surprisingly pushes in ahead of you.
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