An Approach to Systematic Data Acquisition and Data-Driven Simulation for the Safety Testing of Automated Driving Functions

TL;DR

This paper introduces a systematic method for collecting and transforming real-world traffic data to improve the accuracy of simulation models used in the safety validation of automated driving functions, especially in data-scarce scenarios.

Contribution

It presents a novel approach to acquire, unify, and utilize heterogeneous traffic data for automatic parameterization of traffic behavior models in virtual safety testing.

Findings

Enhanced simulation accuracy through data-driven parameterization

Effective data collection in public traffic environments

Improved validation of automated driving functions in virtual settings

Abstract

With growing complexity and criticality of automated driving functions in road traffic and their operational design domains (ODD), there is increasing demand for covering significant proportions of development, validation, and verification in virtual environments and through simulation models. If, however, simulations are meant not only to augment real-world experiments, but to replace them, quantitative approaches are required that measure to what degree and under which preconditions simulation models adequately represent reality, and thus, using their results accordingly. Especially in R&D areas related to the safety impact of the "open world", there is a significant shortage of real-world data to parameterize and/or validate simulations - especially with respect to the behavior of human traffic participants, whom automated driving functions will meet in mixed traffic. We present an approach to systematically acquire data in public traffic by heterogeneous means, transform it into a unified representation, and use it to automatically parameterize traffic behavior models for use in data-driven virtual validation of automated driving functions.