Validating SuperHuman Automated Driving Performance

Publikation: Beitrag in Buch/Bericht/KonferenzbandBeitrag in einem Konferenzband

Abstract

Closed-loop validation of autonomous vehicles is an open problem, significantly influencing development and adoption of this technology. The main contribution of this paper is a novel approach to reproducible, scenario-based validation that decouples the problem into several sub-problems, while avoiding to brake the crucial couplings. First, a realistic scenario is generated from the real urban traffic. Second, human participants, drive in a virtual scenario (in a driving simulator), based on the real traffic. Third, human and automated driving trajectories are reproduced and compared in the real vehicle on an empty track without traffic. Thus, benefits of automation with respect to safety, efficiency and comfort can be clearly benchmarked in a reproducible manner. Presented approach is used to benchmark performance of SBOMP planner in one scenario and validate SuperHuman driving performance.

Originalspracheenglisch
Titel2020 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2020
Seiten3860-3867
Seitenumfang8
Band2020-October
ISBN (elektronisch)9781728185262
DOIs
PublikationsstatusVeröffentlicht - 11 Okt 2020
Veranstaltung2020 IEEE International Conference on Systems, Man, and Cybernetics - Virtuell, Kanada
Dauer: 11 Okt 202014 Okt 2020

Konferenz

Konferenz2020 IEEE International Conference on Systems, Man, and Cybernetics
KurztitelIEEE SMC 2020
LandKanada
OrtVirtuell
Zeitraum11/10/2014/10/20

ASJC Scopus subject areas

  • Software
  • Human-computer interaction
  • !!Electrical and Electronic Engineering
  • !!Control and Systems Engineering
  • !!Computer Science Applications

Fingerprint Untersuchen Sie die Forschungsthemen von „Validating SuperHuman Automated Driving Performance“. Zusammen bilden sie einen einzigartigen Fingerprint.

Dieses zitieren