https://www.mdu.se/

mdu.sePublications
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Early Concept Evaluation of a Runtime Monitoring Approach for Safe Automated Driving
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
TTTech Computertechnik Ag, Vienna, Austria.
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.ORCID iD: 0000-0001-5269-3900
2022 (English)In: 2022 IEEE Zooming Innovation in Consumer Technologies Conference (ZINC), IEEE, 2022, p. 53-58Conference paper, Published paper (Refereed)
Abstract [en]

Being used in key features, such as sensing and intelligent path planning, Artificial Intelligence (AI) has become an inevitable part of automated vehicles (AVs). However, their usage in the automotive industry always comes with a 'label' that questions their impact on the overall AV safety. This paper focuses on the safe deployment of AI-based AVs. Among the various ways for ensuring the safety of AI-based AVs is to monitor the safe execution of the system responsible for automated driving (i.e., Automated Driving System (ADS)) at runtime (i.e., runtime monitoring). Most of the research done in the past years focused on verifying whether the path or trajectory generated by the ADS does not immediately collide with objects on the road. However, as we will show in this paper, there are other unsafe situations that do not immediately result in a collision but the monitor should check for them. To build our case, we have looked into the National Highway Traffic Safety Administration (NHTSA) database of 5.9 million police-reported light-vehicle accidents and categorized these accidents into five main categories of unsafe vehicle operations. Furthermore, we have performed a high-level evaluation of the runtime monitoring approach proposed in [1], by estimating what percentage of the total population of 5.9 million of unsafe operations the approach would be able to detect. Lastly, we have performed the same evaluation on other existing runtime monitoring approaches to make a basic comparison of their diagnostic capabilities.

Place, publisher, year, edition, pages
IEEE, 2022. p. 53-58
Keywords [en]
accident prevention, highway accidents, motion planning, vehicles
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:mdh:diva-51849DOI: 10.1109/ZINC55034.2022.9840649Scopus ID: 2-s2.0-85136371722ISBN: 978-1-6654-8374-2 (electronic)OAI: oai:DiVA.org:mdh-51849DiVA, id: diva2:1479226
Conference
Zooming Innovation in Consumer Technologies Conference (ZINC), 25-26 May 2022, Novi Sad, Serbia
Available from: 2020-10-26 Created: 2020-10-26 Last updated: 2022-09-07Bibliographically approved
In thesis
1. Runtime Monitoring for Safe Automated Driving Systems
Open this publication in new window or tab >>Runtime Monitoring for Safe Automated Driving Systems
2020 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Mass-produced passenger vehicles are one of the greatest inventions of the 20th century that significantly changed human lives. Several safety measures such as traffic signs, traffic lights, mandatory driver education, seat belts, airbags, and anti-lock braking systems were introduced throughout the years. Today, a further increase in safety, comfort, and efficiency is being targeted by developing systems with automated driving capabilities. These systems range from those supporting the driver with a particular function (e.g., ensuring vehicle drives with constant speed while keeping a safe distance to other road participants) to taking all driving responsibilities from the driver (i.e., full driving automation). The development and series production of the former has already been accomplished, whereas reaching full driving automation still presents many challenges.

The main reason is the shift of all driving responsibilities, including the responsibility for the overall vehicle safety, from the human driver to a computer-based system responsible for the automated driving functionality (i.e., the Automated Driving System (ADS)). Such a shift makes the ADS highly safety-critical, and the consensus of cross-domain experts is that there is no “silver bullet” for ensuring the required levels of safety. Instead, a set of complementary safety methods are necessary.

In this context, runtime monitoring that continuously verifies the safe operation of the ADS, once deployed on public roads, is a promising complementary approach for ensuring safety. However, the development of a runtime monitoring solution is a challenge on its own. On a conceptual level, the complex and opaque technology used in ADS often makes researchers doubt “what” a runtime monitor should verify and “how” such verification should be performed.

This thesis proposes novel runtime monitoring solutions for verifying the safe operation of ADS. On a conceptual level, a novel Runtime Verification (RV) approach, namely the Safe Driving Envelope- Verification (SDE-V), answers the “what” and “how” of monitoring an ADS. In particular, the SDE-V approach verifies whether the ADS path planner output (i.e., a trajectory) is safe to be executed by the vehicle’s actuators. To perform this verification, the trajectory is checked against the following safety rules: (i) trajectory not leading into collision with obstacles on the road, and (ii) trajectory not leaving the road edge.

Towards realizing the proposed SDE-V concept into an actual solution, additional concepts, methods, and architectural solutions have been developed. Our contributions in this context include : (i) a concept for reducing the false positive rate of SDE-V, (ii) a method for evaluating the quality of runtime monitors by investigating to what extent they can handle faults related to different classes of real accident scenarios, (iii) a modular and scalable fail-operational architecture which enables integration of multiple RV approaches alongside the SDE-V, (iv) estimation of a “forecast horizon” to ensure the timely execution of emergency actions upon an ADS failure detection by SDE-V, and (v) an approach to tackle the out-of-sequence measurement problem in sensor fusion-based ADS. A prototype implementation of SDV-E has been realized on an automotive-grade embedded platform. Based on its promising results, a future industrial implementation Project has been initiated.

Place, publisher, year, edition, pages
Västerås: Mälardalen University, 2020
Series
Mälardalen University Press Dissertations, ISSN 1651-4238 ; 324
National Category
Computer Systems
Research subject
Computer Science
Identifiers
urn:nbn:se:mdh:diva-51850 (URN)978-91-7485-489-3 (ISBN)
Public defence
2020-11-23, Pi +(Online Zoom), Mälardalens högskola, Västerås, 14:15 (English)
Opponent
Supervisors
Available from: 2020-10-27 Created: 2020-10-26 Last updated: 2020-11-02Bibliographically approved

Open Access in DiVA

No full text in DiVA

Other links

Publisher's full textScopus

Authority records

Mehmed, AyhanCausevic, AidaPunnekkat, Sasikumar

Search in DiVA

By author/editor
Mehmed, AyhanCausevic, AidaPunnekkat, Sasikumar
By organisation
Embedded SystemsEmbedded Systems
Electrical Engineering, Electronic Engineering, Information Engineering

Search outside of DiVA

GoogleGoogle Scholar

doi
isbn
urn-nbn

Altmetric score

doi
isbn
urn-nbn
Total: 4398 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf