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

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 without relying 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 parametrization and pdf estimation shows that the proposed method can automatically determine the optimal scenario parametrization and pdf estimation. Furthermore, we demonstrate 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.

14 pages, 11 figures, 4 tables, accepted for publication in IEEE Transactions on Intelligent Transportation Systems

References in corpus (3)

• Fast Nonparametric Conditional Density Estimation
• Real-World Scenario Mining for the Assessment of Automated Vehicles
• Constrained Sampling from a Kernel Density Estimator to Generate Scenarios for the Assessment of Automated Vehicles

Cited by in corpus (4)

• PRISMA: A Novel Approach for Deriving Probabilistic Surrogate Safety Measures for Risk Evaluation
• HAD-Gen: Human-like and Diverse Driving Behavior Modeling for Controllable Scenario Generation
• Scenario-based assessment of automated driving systems: How (not) to parameterize scenarios?
• Statistical Modelling of Driving Scenarios in Road Traffic using Fleet Data of Production Vehicles