Legal Aspects of Autonomous Driving (original) (raw)
Related papers
2018
This chapter introduces the main technology bricks and some related socioeconomic aspects of automated driving. Vehicles automation technology is advancing at a vertiginous pace. However, the complexity behind some highly uncertain and dynamic driving scenarios imposes the need to distinguish between the different automation levels. This chapter starts from these considerations to elaborate thereafter on the maturity of the currently used technologies—situation awareness, risk assessment, decision-making, human–machine interaction, planning, control—and their near future possibilities. The introduction of connectivity among vehicles and with the digital world brings a number of new opportunities, when combined with automation, that are introduced with the focus on cooperative automated driving. After presenting this technological panorama, different relevant projects are described with the aim to understand the differences between the existing prototypes and the upcoming products an...
Autonomous Vehicles and Their Implications to Society
Industry, Innovation and Infrastructure, 2019
An autonomous vehicle is one that can drive itself without human conduction. The automation can be partial, when the automated system of the vehicle can conduct some parts of the driving tasks, or full, when the vehicle can perform all driving tasks under all conditions (geographic area, roadway type, traffic, weather, events/incidents) that a human driver could perform them. Autonomous, automated, driverless, self-driving, robotic vehicles are the terms which are usually used interchangeably. However, there is a slight difference between "automated" and "autonomous" terms. As Wood et al. 2012 suggested, "'Automated' connotes control or operation by a machine, while "autonomous" connotes acting alone or independently". While the term "automated" is more precise for the most of the existing today projects, the term "autonomous" is more widespread and is usually used to refer to both autonomous and automated vehicles. History and Levels of Vehicle Automation The history of autonomous vehicles begins in the 1920s when a first radio-controlled vehicle travelled along New York City streets. Until the 1980s the experiments to create a driverless vehicle mostly involved guiding a vehicle using radio control or cables embedded in the road. The digital revolution in the 1960s boosted research and projects in robotics with efforts to create vehicles which would be able to sense, process the received information, and drive accordingly. First truly autonomous vehicles which were able to guide themselves based on sensors and autonomous robotic control were created in the 1980s, in the United States by Carnegie Mellon University's Navlab and ALV in 1984 and in Germany by Mercedes-Benz and Bundeswehr University Munich in 1987. Since then the "intelligence" of the autonomous vehicles was gradually improving boosted by increase of computational power and by development of artificial intelligence. More driving tasks were becoming automated. In the 1990s first prototypes with parallel parking were created, and in the early
IRJET, 2022
Intelligent connected cars (ICVs) are expected to improve transportation in the near future, making it safer, cleaner, and more comfortable for passengers. Even though many ICV prototypes have been created to demonstrate the notion of autonomous driving and the viability of perfecting business effectiveness, there is still a long way to go before high-position ICVs are produced in large quantities. The goal of this study is to provide an overview of key technologies needed for future ICVs from both the current state of the art and future perspectives. Reviewing every affiliated workshop and predicting their future perspectives is a taxing effort, especially for such a complicated and diverse field of research. Advanced driver-assistance systems (ADASs) have become a salient feature for safety in ultramodern vehicles. They're also a crucial underpinning technology in arising independent vehicles. State-of-the-art ADASs are primarily vision grounded, colorful type of features for partner. Automatic Emergency Braking (AEB) and other advanced- seeing technologies are also getting popular. In this composition, this composition is organized to overview the ICV key technologies or Features of ADAS. We bandy approaches used for vision- grounded recognition and detector emulsion in ADAS results. We also punctuate benefits for the coming generation of ADASs. This abecedarian work explains in detail systems for active safety and motorist backing, considering both their structure and their function. These include the well- known standard systems similar as Electronic Stability Control (ESC) or Adaptive voyage Control (ACC), Omni View( Bird eye View), Head- up display. But it includes also new systems for guarding collisions protection, for changing the lane, or for accessible parking. The paper aims at giving a complete picture fastening on the entire Features. First, it describes the factors, which are necessary for backing systems, similar as detectors, and control rudiments. also, it explains crucial features for the stoner-friendly design of mortal- machine interfaces between motorist and backing system
Autonomous Driving — a Challenge for the Automotive Industry
Intereconomics
ZBW-Leibniz Information Centre for Economics 171 Automotive Industry ers and the position of suppliers competing for a share of the autonomous vehicles market. 2 Autonomous vehicles Autonomous driving takes a wide variety of forms. The classifi cation produced by the Society of Automotive Engineers (Table 1) has attracted considerable attention. 3 The German Federal Highway Research Institute devised a similar taxonomy that differentiates vehicles driven solely by the driver (manual driving), driver-assisted, semiautomated, highly automated and fully automated. 4 The precise differences in the defi nitions are not discussed here in greater detail, but the decisive factor is the unanimous opinion that there is no clear-cut, binary distinction between automated and non-automated vehicles. Rather, both describe a continuum of progressive steps of automation. Simply using a lane departure warning system, for example, is not considered automation. Lane departure assis-2 This paper updates H. B a r d t : Autonomes Fahren-Eine Herausforderung für die deutsche Autoindustrie, in: IW-Trends-Vierteljahresschrift zur empirischen Wirtschaftsforschung aus dem Institut der
As technology expands, automation increases with it. Autonomous cars is an example of this technology. Autonomous cars are more than most cars on the streets-they are a data center made up of networked computers performing or responsible for different tasks. The technologies explained in this report include computer systems and vision, sensor fusion, localization, path planning, and control.
Exploring Key Technologies in Autonomous Vehicles
International Journal of Science and Research (IJSR) ISSN: 2319-7064, 2019
This paper dives deep into the technologies used in Autonomous Vehicles. The Global Positioning System (GPS) is a key technology employed by autonomous cars. It is a satellite-based navigation system that determines the exact location of an object. Another technology used by autonomous cars is cameras, which are utilized to detect road signs, identify red lights, and determine lanes on the road, among other functions. Typically, the digital images of the road are presented as unrelated pixels, and this data is structured for better utilization. The algorithm for lane detection interprets this data in four basic steps: preprocessing, feature detection, fitting, and tracking. The other two technologies detailed in this paper are RADAR (Radio Detection and Ranging) and LiDAR (Light Imaging, Detection, and Ranging). RADAR technology uses radio waves to identify objects on the road and is effective in any type of weather conditions. Contrastingly, LiDAR is a laser analyzing device that allows 3D mapping of the surroundings of the vehicle. Unlike RADAR, LiDAR utilizes lasers instead of radio waves.
Autonomous Driving, 2016
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]. In the following chapter the author traces the technical improvements in vehicle safety over recent decades, factoring in growing consumer expectations. Considering Federal Court of Justice rulings on product liability and economic risks, he depicts requirements that car manufacturers must meet. For proceedings from the first idea until development to sign, he recommends interdisciplinary, harmonized safety and testing procedures. He argues for further development of current internationally agreed-upon standards including tools, methodological descriptions, simulations, and guiding principles with checklists. These will represent and document the practiced state of science and technology, which has to be implemented in a technically viable and economically reasonable way. 28.1.1 Motivation In the course of this development, technical, especially electrical/electronic systems and software are becoming far more complex in the future. Therefore, safety will be one of the key issues in future automobile development and this results in a number of major new challenges, especially for car manufacturers and their developers. In particular, changing vehicle guidance from being completely human-driven, as it has so far been, to being
Autonomous vehicles: from paradigms to technology
IOP Conference Series: Materials Science and Engineering, 2017
Mobility is a basic necessity of contemporary society and it is a key factor in global economic development. The basic requirements for the transport of people and goods are: safety and duration of travel, but also a number of additional criteria are very important: energy saving, pollution, passenger comfort. Due to advances in hardware and software, automation has penetrated massively in transport systems both on infrastructure and on vehicles, but man is still the key element in vehicle driving. However, the classic concept of 'human-in-the-loop' in terms of 'hands on' in driving the cars is competing aside from the self-driving startups working towards so-called 'Level 4 autonomy', which is defined as "a self-driving system that does not requires human intervention in most scenarios". In this paper, a conceptual synthesis of the autonomous vehicle issue is made in connection with the artificial intelligence paradigm. It presents a classification of the tasks that take place during the driving of the vehicle and its modeling from the perspective of traditional control engineering and artificial intelligence. The issue of autonomous vehicle management is addressed on three levels: navigation, movement in traffic, respectively effective maneuver and vehicle dynamics control. Each level is then described in terms of specific tasks, such as: route selection, planning and reconfiguration, recognition of traffic signs and reaction to signaling and traffic events, as well as control of effective speed, distance and direction. The approach will lead to a better understanding of the way technology is moving when talking about autonomous cars, smart/intelligent cars or intelligent transport systems.
Autonomous vehicles: basic issues
Scientific Journal of Silesian University of Technology. Series Transport, 2018
The work was dedicated to the subject of innovative autonomous vehicles on the transport market. The paper presents basic information about autonomous cars: a nomenclature characteristic of autonomous vehicles, along with the terms “automatic”, “autonomous”, “self-drive” and “driverless”. The article also presents various types of autonomous cars based on the most popular classifications in the world. The purpose of the work is to present basic issues related to autonomous vehicles.