Runtime monitoring for safe automated driving systems

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.

• Record URL:
  http://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-51850
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/isbn/9789174854893
• Authors:
  • Mehmed, Ayhan
• Publication Date: 2020

Language

• English

Media Info

• Pagination: 106
• Serial:
  • Mälardalen University Press Dissertations
  • Issue Number: 324
  • Publisher: Mälardalen University, Sweden
  • ISSN: 1651-4238

Subject/Index Terms

• TRT Terms: Autonomous vehicles; Mathematical models; Safety; Theses
• ITRD Terms: 6473: Algorithm; 1857: Autonomous driving; 1244: Autonomous vehicle; 6473: Equation; 6473: Formula (math); 1665: Safety; 8597: Thesis
• Subject Areas: Safety and Human Factors; I91: Vehicle Design and Safety;

Filing Info

• Accession Number: 01790076
• Record Type: Publication
• Source Agency: Swedish National Road and Transport Research Institute (VTI)
• ISBN: 9789174854893
• Files: ITRD, VTI
• Created Date: Dec 1 2021 2:46PM