Reference
E. de Gelder, J. Hof, E. Cator, J.-P. Paardekooper, O. Op den Camp, J. Ploeg,
and B. De Schutter, "Scenario parameter generation method and scenario
representativeness metric for scenario-based assessment of automated vehicles,"
IEEE Transactions on Intelligent Transportation
Systems, vol. 23, no. 10, pp. 18794-18807, Oct. 2022.
Abstract
The development of assessment methods for the performance of Automated Vehicles
(AVs) is essential to enable the deployment of automated driving technologies,
due to the complex operational domain of AVs. One candidate is scenario-based
assessment, in which test cases are derived from real-world road traffic
scenarios obtained from driving data. Because of the high variety of the
possible scenarios, using only observed scenarios for the assessment is not
sufficient. Therefore, methods for generating additional scenarios are
necessary.
Our contribution is twofold. First, we propose a method to determine the
parameters that describe the scenarios to a sufficient degree while relying
less on strong assumptions on the parameters that characterize the scenarios.
By estimating the probability density function (pdf) of these parameters,
realistic parameter values can be generated. Second, we present the Scenario
Representativeness (SR) metric based on the Wasserstein distance, which
quantifies to what extent the scenarios with the generated parameter values are
representative of real-world scenarios while covering the actual variety found
in the real-world scenarios.
A comparison of our proposed method with methods relying on assumptions of the
scenario parameterization and pdf estimation shows that the proposed method can
automatically determine the optimal scenario parameterization and pdf
estimation. Furthermore, it is demonstrated that our SR metric can be used to
choose the (number of) parameters that best describe a scenario. The presented
method is promising, because the parameterization and pdf estimation can
directly be applied to already available importance sampling strategies for
accelerating the evaluation of AVs.
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BibTeX
@article{deGHof:22-012,
author = {de Gelder, Erwin and Hof, Jasper and Cator, Eric and
Paardekooper, Jan-Pieter and Op den Camp, Olaf and Ploeg, Jeroen
and De Schutter, Bart},
title = {Scenario Parameter Generation Method and Scenario
Representativeness Metric for Scenario-Based Assessment of
Automated Vehicles},
journal = {IEEE Transactions on Intelligent Transportation Systems},
volume = {23},
number = {10},
pages = {18794--18807},
month = oct,
year = {2022}
}