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Architecture challenges for intelligent autonomous machines: An industrial perspective
KTH, School of Industrial Engineering and Management (ITM), Machine Design (Dept.), Embedded Control Systems.ORCID iD: 0000-0002-8629-0402
KTH, School of Industrial Engineering and Management (ITM), Machine Design (Dept.), Embedded Control Systems.ORCID iD: 0000-0001-5704-4504
KTH, School of Industrial Engineering and Management (ITM), Machine Design (Dept.). The SP Technical Research Institute, Sweden.
KTH, School of Industrial Engineering and Management (ITM), Machine Design (Dept.), Embedded Control Systems.ORCID iD: 0000-0002-4300-885X
2016 (English)In: 13th International conference on Intelligent Autonomous Systems (IAS-13), Springer, 2016, Vol. 302, 1669-1681 p.Conference paper (Refereed)
Abstract [en]

Machines are displaying a trend of increasing autonomy. This has a far reaching impact on the architectures of the embedded systems within the machine. The impact needs to be clearly understood and the main obstacles to autonomy need to be identified. The obstacles, especially from an industrial perspective, are not just technological butalso relate to system aspects like certification, development processes and product safety. In this paper, we identify and discuss some of the main obstacles to autonomy from the viewpoint of technical specialists working on advanced industrial product development. The identified obstacles cover topics like world modeling, user interaction, complexity and system safety.

Place, publisher, year, edition, pages
Springer, 2016. Vol. 302, 1669-1681 p.
, Advances in Intelligent Systems and Computing, ISSN 2194-5357 ; 302
Keyword [en]
Autonomy, Architecture, Embedded Systems
National Category
Embedded Systems
Research subject
Vehicle and Maritime Engineering
URN: urn:nbn:se:kth:diva-152534DOI: 10.1007/978-3-319-08338-4_120ISI: 000377956900120ScopusID: 2-s2.0-84945905859ISBN: 978-331908337-7OAI: diva2:750204
13th International conference on Intelligent Autonomous Systems (IAS-13),Padova 15-19 July 2014

QC 20140930

Available from: 2014-09-26 Created: 2014-09-26 Last updated: 2016-07-18Bibliographically approved
In thesis
1. Reference Architectures for Highly Automated Driving
Open this publication in new window or tab >>Reference Architectures for Highly Automated Driving
2016 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Highly automated driving systems promise increased road traffic safety, as well as positive impacts on sustainable transportation by means of increased traffic efficiency and environmental friendliness. The design and development of such systems require scientific advances in a number of areas. One area is the vehicle's electrical/electronic (E/E) architecture. The E/E architecture can be presented using a number of views, of which an important one is the functional view. The functional view describes the decomposition of the system into its main logical components, along with the hierarchical structure, the component inter-connections, and requirements. When this view captures the principal ideas and patterns that constitute the foundation of a variety of specific architectures, it may be termed as a reference architecture. Two reference architectures for highly automated driving form the principal contribution of this thesis. The first reference architecture is for cooperative driving. In a cooperative driving situation, vehicles and road infrastructure in the vicinity of a vehicle continuously exchange wireless information and this information is then used to control the motion of the vehicle. The second reference architecture is for autonomous driving, wherein the vehicle is capable of driver-less operation even without direct communication with external entities. The description of both reference architectures includes their main components and the rationale for how these components should be distributed across the architecture and its layers. These architectures have been validated via multiple real-world instantiations, and the guidelines for instantiation also form part of the architecture description. A comparison with similar architectures is also provided, in order to highlight the similarities and differences. The comparisons show that in the context of automated driving, the explicit recognition of components for semantic understanding, world modeling, and vehicle platform abstraction are unique to the proposed architecture. These components are not unusual in architectures within the Artificial Intelligence/robotics domains; the proposed architecture shows how they can be applied within the automotive domain. A secondary contribution of this thesis is a description of a lightweight, four step approach for model based systems engineering of highly automated driving systems, along with supporting model classes. The model classes cover the concept of operations, logical architecture, application software components, and the implementation platforms. The thesis also provides an overview of current implementation technologies for cognitive driving intelligence and vehicle platform control, and recommends a specific setup for development and accelerated testing of highly automated driving systems, that includes model- and hardware-in-the-loop techniques in conjunction with a publish/subscribe bus. Beyond the more "traditional" engineering concepts, the thesis also investigates the domain of machine consciousness and computational self-awareness. The exploration indicates that current engineering methods are likely to hit a complexity ceiling, breaking through which may require advances in how safety-critical systems can self-organize, construct, and evaluate internal models to reflect their perception of the world. Finally, the thesis also presents a functional architecture for the brake system of an autonomous truck. This architecture proposes a reconfiguration of the existing brake systems of the truck in a way that provides dynamic, diversified redundancy, and an increase in the system reliability and availability, while meeting safety requirements.

Place, publisher, year, edition, pages
Stockholm: KTH Royal Institute of Technology, 2016. xviii, 50 p.
TRITA-MMK, ISSN 1400-1179 ; 2105:09
Autonomous driving, E/E Architecture, Systems Engineering
National Category
Embedded Systems
Research subject
Machine Design
urn:nbn:se:kth:diva-179306 (URN)978-91-7595-757-9 (ISBN)
Public defence
2016-01-22, Kollegiesalen, Brinellvägen 8, Stockholm, 09:00 (English)

QC 20151216

Available from: 2015-12-16 Created: 2015-12-15 Last updated: 2016-01-25Bibliographically approved

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