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Improving Dependability of Vision-Based Advanced Driver Assistance Systems Using Navigation Data and Checkpoint Recognition
TTTech Computertechnik AG, Vienna, Austria.
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.ORCID iD: 0000-0001-5269-3900
TTTech Computertechnik AG, Vienna, Austria.
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
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2015 (English)In: COMPUTER SAFETY, RELIABILITY, AND SECURITY, SAFECOMP 2015, 2015, p. 59-73Conference paper, Published paper (Refereed)
Abstract [en]

Advanced Driver Assistance Systems (ADAS), like adaptive cruise control, collision avoidance, and, ultimately, autonomous driving are increasingly evolving into safety-critical systems. These ADAS frequently rely on proper function of Computer-Vision Systems (CVS), which is hard to assess in a timely manner, due to their sensitivity to the variety of illumination conditions (e.g. weather conditions, sun brightness). On the other hand, self-awareness information is available in the vehicle, such as maps and localization data (e.g. GPS).

This paper studies how the combination of diverse environmental information can improve the overall vision-based ADAS reliability. To this extent we present a concept of a Computer-Vision Monitor (CVM) that identifies predefined landmarks in the vehicles surrounding, based on digital maps and localization data, and that checks whether the CVS correctly identifies said landmarks. We formalize and assess the reliability improvement of our solution by means of a fault-tree analysis.

Place, publisher, year, edition, pages
2015. p. 59-73
Series
Lecture Notes in Computer Science, ISSN 0302-9743 ; 9337
National Category
Computer Systems
Identifiers
URN: urn:nbn:se:mdh:diva-29630DOI: 10.1007/978-3-319-24255-2_6ISI: 000366204300006Scopus ID: 2-s2.0-84969752583ISBN: 978-3-319-24254-5 (print)OAI: oai:DiVA.org:mdh-29630DiVA, id: diva2:881503
Conference
International Conference on Computer Safety, Reliability & Security SAFECOMP 2015, 23-25 Sep 2015, Delft, Netherlands
Projects
RetNet - The European Industrial Doctorate Programme on Future Real-Time NetworksAvailable from: 2015-12-10 Created: 2015-11-26 Last updated: 2019-08-26Bibliographically approved
In thesis
1. Runtime Monitoring of Automated Driving Systems
Open this publication in new window or tab >>Runtime Monitoring of Automated Driving Systems
2019 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

It is the period of the World's history, where the technological progress reached a level that enables the first steps towards the development of vehicles with automated driving capabilities. The swift response from the significant portion of the industry resulted in a race, the final line set at the introduction of vehicles with full automated driving capabilities.

Vehicles with automated driving capabilities target making driving safer, more comfortable, and economically more efficient by assisting the driver or by taking responsibilities for different driving tasks. While vehicles with assistance and partial automation capabilities are already in series production, the ultimate goal is in the introduction of vehicles with full automated driving capabilities. Reaching this level of automation will require shifting all responsibilities, including the responsibility for the overall vehicle safety, from the human to the computer-based system responsible for the automated driving functionality (i.e., the Automated Driving System (ADS)). Such a shift makes the ADS highly safe-critical, requiring a safety level comparable to an aircraft system.

It is paramount to understand that ensuring such a level of safety is a complex interdisciplinary challenge. Traditional approaches for ensuring safety require the use of fault-tolerance techniques that are unproven when it comes to the automated driving domain. Moreover, existing safety assurance methods (e.g., ISO 26262) suffer from requirements incompleteness in the automated driving context. The use of artificial intelligence-based components in the ADS further complicate the matter due to their non-deterministic behavior. At present, there is no single straightforward solution for these challenges. Instead, the consensus of cross-domain experts is to use a set of complementary safety methods that together are sufficient to ensure the required level of safety.

In the context of that, runtime monitors that verify the safe operation of the ADS during execution, are a promising complementary approach for ensuring safety. However, to develop a runtime monitoring solution for ADS, one has to handle a wide range of challenges. On a conceptual level, the complex and opaque technology used in ADS often make researchers ask the question ``how should ADS be verified in order to judge it is operating safely?".

Once the initial Runtime Verification (RV) concept is developed, researchers and practitioners have to deal with research and engineering challenges encountered during the realization of the RV approaches into an actual runtime monitoring solution for ADS. These challenges range from, estimating different safety parameters of the runtime monitors, finding solutions for different technical problems, to meeting scalability and efficiency requirements.

The focus of this thesis is to propose novel runtime monitoring solutions for verifying the safe operation of ADS. This encompasses (i) defining novel RV approaches explicitly tailored for automated driving, and (ii) developing concepts, methods, and architectures for realizing the RV approaches into an actual runtime monitoring solution for ADS. Contributions to the former include defining two runtime RV approaches, namely the Computer Vision Monitor (CVM) and the Safe Driving Envelope Verification. Contributions to the latter include (i) estimating the sufficient diagnostic test interval of the runtime verification approaches (in particular the CVM), (ii) addressing the out-of-sequence measurement problem in sensor fusion-based ADS, and (iii) developing an architectural solution for improving the scalability and efficiency of the runtime monitoring solution.

Place, publisher, year, edition, pages
Västerås: Mälardalen University, 2019
Series
Mälardalen University Press Licentiate Theses, ISSN 1651-9256 ; 281
Keywords
Runtime Monitoring, Automated Driving Systems
National Category
Embedded Systems
Research subject
Computer Science
Identifiers
urn:nbn:se:mdh:diva-45068 (URN)978-91-7485-434-3 (ISBN)
Presentation
2019-10-17, Delta, Mälardalens högskola, Västerås, 13:15 (English)
Opponent
Supervisors
Projects
RetNet
Available from: 2019-08-28 Created: 2019-08-26 Last updated: 2019-09-17Bibliographically approved

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Punnekkat, Sasikumar

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