Probabilistic Metamodels for an Efficient Characterization of Complex
Driving Scenarios
- URL: http://arxiv.org/abs/2110.02892v2
- Date: Thu, 7 Oct 2021 13:01:34 GMT
- Title: Probabilistic Metamodels for an Efficient Characterization of Complex
Driving Scenarios
- Authors: Max Winkelmann, Mike Kohlhoff, Hadj Hamma Tadjine, Steffen M\"uller
- Abstract summary: We introduce and evaluate an iterative approach to efficiently select test cases.
Our results show that regarding predictive performance, the appropriate selection of test cases is more important than the choice of metamodels.
This implies that relevant test cases have to be explored using scalable virtual environments and flexible models.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: To systematically validate the safe behavior of automated vehicles (AV), the
aim of scenario-based testing is to cluster the infinite situations an AV might
encounter into a finite set of functional scenarios. Every functional scenario,
however, can still manifest itself in a vast amount of variations. Thus,
metamodels are often used to perform analyses or to select specific variations
for examination. However, despite the safety criticalness of AV testing,
metamodels are usually seen as a part of an overall approach, and their
predictions are not further examined. In this paper, we analyze the predictive
performance of Gaussian processes (GP), deep Gaussian processes, extra-trees
(ET), and Bayesian neural networks (BNN), considering four scenarios with 5 to
20 inputs. Building on this, we introduce and evaluate an iterative approach to
efficiently select test cases. Our results show that regarding predictive
performance, the appropriate selection of test cases is more important than the
choice of metamodels. While their great flexibility allows BNNs to benefit from
large amounts of data and to model even the most complex scenarios, less
flexible models like GPs can convince with higher reliability. This implies
that relevant test cases have to be explored using scalable virtual
environments and flexible models so that more realistic test environments and
more trustworthy models can be used for targeted testing and validation.
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