Validating SuperHuman Automated Driving Performance

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish
Title of host publication2020 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2020
Pages3860-3867
Number of pages8
Volume2020-October
ISBN (Electronic)9781728185262
DOIs
Publication statusPublished - 11 Oct 2020
Event2020 IEEE International Conference on Systems, Man, and Cybernetics - Virtuell, Canada
Duration: 11 Oct 202014 Oct 2020

Conference

Conference2020 IEEE International Conference on Systems, Man, and Cybernetics
Abbreviated titleIEEE SMC 2020
CountryCanada
CityVirtuell
Period11/10/2014/10/20

Keywords

  • automated driving
  • lane change
  • multi-lane driving
  • planning
  • SuperHuman
  • traffic lights
  • urban driving
  • validation

ASJC Scopus subject areas

  • Software
  • Human-Computer Interaction
  • Electrical and Electronic Engineering
  • Control and Systems Engineering
  • Computer Science Applications

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