Scenario Parameter Generation Method and Scenario Representativeness Metric for Scenario-Based Assessment of Automated Vehicles

Scenario Parameter Generation Method and Scenario Representativeness Metric for Scenario-Based Assessment of Automated Vehicles

de Gelder, E. (TU Delft Team Bart De Schutter; TNO)
Hof, Jasper (Radboud University Medical Center)
Cator, Eric (Radboud Universiteit Nijmegen)
Paardekooper, Jan Pieter (TNO; Radboud Universiteit Nijmegen)
Camp, Olaf Op den (TNO)
Ploeg, Jeroen (Eindhoven University of Technology)
De Schutter, B.H.K. (TU Delft Team Bart De Schutter)

2022

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.

automatic testing
autonomous vehicles
estimation
Intelligent vehicles
Measurement
Monte Carlo methods
performance evaluation
probability density function
roads
safety
time series analysis
vehicle safety

http://resolver.tudelft.nl/uuid:e3aa055c-eda3-4bbc-be9d-5bb81adf686e

https://doi.org/10.1109/TITS.2022.3154774

2023-07-01

1524-9050

IEEE Transactions on Intelligent Transportation Systems, 23 (10), 18794-18807

Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

Institutional Repository

journal article

© 2022 E. de Gelder, Jasper Hof, Eric Cator, Jan Pieter Paardekooper, Olaf Op den Camp, Jeroen Ploeg, B.H.K. De Schutter