Autonomous Driving

The fascination with the promise of automotive autonomy has historically rested primarily on human drivers’ control of the gas pedal, steering wheel and brakes.

If motor vehicles are to be truly autonomous and able to operate responsibly on our roads, they will need to replicate—or do better than—the human decision-making process. But some decisions are more than just a mechanical application of traffic laws and plotting a safe path. They seem to require a sense of ethics, and this is a notoriously difficult capability to reduce into algorithms for a computer to follow.

As agents moving through an environment that includes a range of other road users—from pedestrians and bicyclists to other human or automated drivers—automated vehicles continuously interact with the humans around them. The nature of these interactions is a result of the programming in the vehicle and the priorities placed there by the programmers.

Humans represent knowledge and learning experiences in the form of mental models. This concept from the field of cognitive psychology is one of the central theoretical paradigms for understanding and designing the interaction between humans and technical systems.

Discussions of autonomous land vehicles often invoke the example of air traffic, where the autopilot is responsible for steering except for take-off and landing. The question arises: what can we learn from air traffic? What autonomously flying aircraft and autonomously driving vehicles have in common is that the pilot or driver bears the final responsibility. But, there are a number of differences between road traffic and air traffic (besides their type of locomotion) that make transferring the systems from one to the other impractical.

Autonomous driving (self-driving) vehicles, once just a science fiction dream, are a growing reality. Although not commercially available, rapid advancements in technology are creating a situation where technological development needs are moving beyond the regulatory environment. Technological developments have put pressure on governments to make regulatory changes permitting on-road testing of autonomous vehicles. Nevada became the first government worldwide to provide licenses for the testing and operation of autonomous vehicles in the state albeit under strict conditions.

Transport is an expression for the satisfaction of mobility needs with different means of transportation—for everyday travel, people walk, cycle, drive or take public transport. There are two main groups here: people with a distinct preference for using private vehicles, and people who prefer so-called “ecomobility”—the combination of public transport with walking and cycling.

Tragically, traffic accidents continue to be an everyday aspect of motor vehicle operation as evidenced by statistics.

Mobility, transport and the physical forms of urban areas are closely bound up with each other (Cervero and Kockelman in Transportation Research Part D: Transport and Environment 2(3), 199–219, 1997). Urban structure plays an important role when households and businesses make mobility decisions, and to a considerable degree dictates what forms of transport may or may not be taken. Compact city form with high density and mixed use provide good preconditions for short trips and efficient public transportation, promote walking and cycling, and often render daily car use unnecessary.

In 2013 Willumsen, one of the most renowned researchers in transport modeling, stated, regarding automated vehicles: “We can no longer ignore them, if [the] planning horizon is 10+ years”.

Since Carl Benz invented the automobile in 1886, some very different vehicle concepts have been developed. Some can be regarded as the logical continued development and replacement of former concepts, such as the renunciation of the carriage design and the integration of the wheels and chassis under the (self supporting) body.

There is currently much discussion, development and research, both in expert circles and in the public sphere, concerning automated (or often “autonomous”) vehicles. Within this discourse, personally used vehicles assume a central position, that is to say, focus is geared toward increasing vehicle automation on city streets and highways.

This paper aims to quantify the effects of autonomous driving on the traffic management level. This involves developing a model of autonomous driving that makes it possible to use human-controlled and autonomous vehicles with only minor modifications.

Autonomous vehicles maneuver in traffic through road networks without requiring humans as supervisors or decision makers. Autonomous vehicles increase comfort for their passengers by removing the need for them to perform driving tasks. Autonomous vehicles provide new mobility opportunities for groups of people that thus far have been partially or entirely excluded from participation in public life due to mobility restrictions.

Advancing vehicle automation promises new opportunities to better meet society’s future mobility demands.

The degree of vehicle automation is continuously rising in all modes of transport both on public traffic infrastructure and in-house transport within company grounds, in order to improve the productivity, reliability, and flexibility of transport.

This chapter discusses the operational and economic aspects of autonomous mobility-on-demand (AMoD) systems, a transformative and rapidly developing mode of transportation wherein robotic, self-driving vehicles transport customers in a given environment. Specifically, AMoD systems are addressed along three dimensions: (1) modeling, that is analytical models capturing salient dynamic and stochastic features of customer demand, (2) control, that is coordination algorithms for the vehicles aimed at throughput maximization, and (3) economic, that is fleet sizing and financial analyses for case studies of New York City and Singapore. Collectively, the models and methods presented in this chapter enables a rigorous assessment of the value of AMoD systems. In particular, the case study of New York City shows that the current taxi demand in Manhattan can be met with about 8000 robotic vehicles (roughly 70 % of the size of the current taxi fleet), while the case study of Singapore suggests that an AMoD system can meet the personal mobility need of the entire population of Singapore with a number of robotic vehicles roughly equal to 1/3 of the current number of passenger vehicles. Directions for future research on AMoD systems are presented and discussed.

In the case of highly-automated and fully-automated driving it is necessary for the vehicle itself to recognize the limitations of its machine perception, as well as the functional limitations of processing modules based on this perception to react adequately.

In the future, the functions of autonomous driving could fundamentally change all road traffic; to do so, it would have to be implemented on a large scale, in series production.

With autonomous driving, a technological system will replace humans in driving automobiles. The car industry, universities, and large IT companies, are currently working on implementing functions permitting a technological system to take on vehicle operation.

The development of autonomous vehicles currently focuses on the functionality of vehicle guidance systems.

Cars have for a long time been a symbol for the freedom and autonomy of their users. Now autonomous driving raises the question how the data flows related to autonomous driving influence the privacy of these cars’ users. Therefore this chapter discusses five guiding questions on autonomous driving, data flows, and the privacy impact of vehicles interacting with other entities: (1) Which “new” or additional data are being collected and processed due to autonomous driving and which consequences result from those “new” or additional data being collected and processed? (Sect. 24.2); (2) Are certain types of data special and do they cause special hindrances? (Sect. 24.3); (3) What is required from the perspective of privacy? (Sect. 24.4); (4) When building architectures, what needs to be kept in mind to avoid creating difficult or even unsolvable privacy problems? (Sect. 24.5); (5) What needs to be considered in the long term? (Sect. 24.6). The questions will be discussed relating as much as possible to the case studies that were introduced at the beginning of this book. Sect. 24.7 concludes this text including an analysis whether more autonomy of driving vehicles leads to more privacy problems.

The “Autonomous driving on the roads of the future: Villa Ladenburg Project” by the Daimler und Benz-Stiftung looks at degrees of automation that will only become technically feasible in the distant future.

Autonomous vehicles (AVs) hold the promise of saving tens of thousands of lives each year in the U.S., and many more worldwide, reducing traffic, saving energy, and providing mobility to those who cannot drive conventional cars. Nonetheless, AVs will inevitably have some accidents. On balance, AVs are likely to prevent many more accidents than they cause, but there will be at least some accidents involving AVs that would not have occurred with conventional vehicles.

This chapter begins with two fundamental questions: How should risk be allocated in the face of significant uncertainty—and who should decide? Its focus on public actors reflects the significant role that legislatures, administrative agencies, and courts will play in answering these questions, whether through rules, investigations, verdicts, or other forms of public regulation. The eight strategies discussed in this chapter would in effect regulate that regulation. They seek to ensure that those who are injured can be compensated (by expanding public insurance and facilitating private insurance), that any prospective rules develop in tandem with the technologies to which they would apply (by privileging the concrete and delegating the safety case), that reasonable design choices receive sufficient legal support (by limiting the duration of risk and excluding the extreme), and that conventional driving is subject to as much scrutiny as automated driving (by rejecting the status quo and embracing enterprise liability).

Sensor technology and data processing are constantly improving in their performance. This enables both: Continuous further development of driver assistance systems and increasing automation of the driving task, right up to self-driving vehicles [1].

What attitudes and expectations do (potential) future users, and the public at large, bring to the new technology of autonomous driving? Alongside the technical and legal areas of research, this question is moving into ever-greater focus. The emerging debates assume that a switch from conventional to autonomous driving might bring about clear changes for all road users. From these perspectives—individual users and society—the question of acceptance arises. To what extent are individuals ready to use fully-automated vehicles, and to what extent are we as a society prepared to accept a transport system with fully automated vehicles on the road?

Technological advancement is changing societal risk constellations. In many cases the result is significantly improved safety and accordingly positive results such as good health, longer life expectancies and greater prosperity. However, the novelty of technological innovations also frequently brings with it unintended and unforeseen consequences, including new risk types.

For over a century, the automobile has shaped our physical mobility like no other means of transport. For almost as long, however, it has also been the subject of criticism surrounding the ecological, social and health consequences of automobility.

The automated, self-driving vehicle is one of the automobile industry’s major ventures in the 21st century, driven by rapid advances in information technology (Brynjolfsson and McAfee in MIT Sloan Management Review 53:53–60, 2012 [10], Burns in Nature 497:181–182, 2013 [11]). Technological innovations in the field of automated driving promise to contribute positively to the financial bottom line of automobile manufacturers (Meseko in J Econ Sustain Dev 5:24–27, 2014 [46]). Their integration as supplementary equipment increases the contribution margin of each car sold. In addition, automated mobility functions lay the foundations for new business models such as elaborated navigation services.