Scene-Extrapolation: Generating Interactive Traffic Scenarios

TL;DR

This paper introduces a method for generating diverse traffic scenarios from seed scenes using lightweight simulation and behavior models, aiding in the verification of automated driving functions.

Contribution

It presents a novel approach to scenario generation that accounts for variability in autonomous driving behaviors without relying on predefined scenarios.

Findings

Effective scenario diversity achieved through behavior modeling.

Criticality metrics enable assessment of scene significance.

Simulation environment supports rapid scenario analysis.

Abstract

Verifying highly automated driving functions can be challenging, requiring identifying relevant test scenarios. Scenario-based testing will likely play a significant role in verifying these systems, predominantly occurring within simulation. In our approach, we use traffic scenes as a starting point (seed-scene) to address the individuality of various highly automated driving functions and to avoid the problems associated with a predefined test traffic scenario. Different highly autonomous driving functions, or their distinct iterations, may display different behaviors under the same operating conditions. To make a generalizable statement about a seed-scene, we simulate possible outcomes based on various behavior profiles. We utilize our lightweight simulation environment and populate it with rule-based and machine learning behavior models for individual actors in the scenario. We analyze resulting scenarios using a variety of criticality metrics. The density distributions of the resulting criticality values enable us to make a profound statement about the significance of a particular scene, considering various eventualities.