Title
Scenario Parameter Generation Method and Scenario Representativeness Metric for Scenario-Based Assessment of Automated Vehicles
Author
de Gelder, E.
Hof, J.
Cator, E.
Paardekooper, J.
Camp, O.O.D.
Ploeg, J.
de Schutter, B.
Publication year
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.
Subject
Automatic testing
Autonomous vehicles
Performance evaluation
Vehicle safety
To reference this document use:
http://resolver.tudelft.nl/uuid:f5065659-9b4d-49fb-971f-2293c9d7988e
DOI
https://doi.org/10.1109/tits.2022.3154774
TNO identifier
967573
Publisher
Institute of Electrical and Electronics Engineers Inc.
ISSN
1524-9050
Source
IEEE Transactions on Intelligent Transportation Systems
Document type
article