Validating SuperHuman Automated Driving Performance

Conference paper (2020)

Authors

Zlatan Ajanovic Virtual Vehicle Research GmbH
Bakir Lacevic University of Sarajevo
B. Shyrokau Intelligent Vehicles - Mechanical, Maritime and Materials Engineering
Paolo Pretto Virtual Vehicle Research GmbH
Hassaan Islam Virtual Vehicle Research GmbH
Georg Stettinger Virtual Vehicle Research GmbH
Martin Horn Graz University of Technology
Research Group
Intelligent Vehicles (Mechanical, Maritime and Materials Engineering) (TU Delft)
Copyright: © 2020 Zlatan Ajanovic, Matthijs Klomp, Bakir Lacevic, B. Shyrokau, Paolo Pretto, Hassaan Islam, Georg Stettinger, Martin Horn
DOI
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https://doi.org/10.1109/SMC42975.2020.9282822
Planning Validation Automated driving Lane change Traffic lights Multi-lane driving SuperHuman Urban driving
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Published Date

2020

Language

English

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Faculty

Mechanical, Maritime and Materials Engineering

Department

Cognitive Robotics

Research Group

Intelligent Vehicles

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.

Files

09282822.pdf
(.pdf | 3.13 Mb)