Causality-based Transfer of Driving Scenarios to Unseen Intersections
Christoph Glasmacher Michael Schuldes Sleiman El Masri Lutz Eckstein
Scenario-based testing of automated driving functions has become a promising
method to reduce time and cost compared to real-world testing. In
scenario-based testing automated functions are evaluated in a set of
pre-defined scenarios. These scenarios provide information about vehicle
behaviors, environmental conditions, or road characteristics using parameters.
To create realistic scenarios, parameters and parameter dependencies have to be
fitted utilizing real-world data. However, due to the large variety of
intersections and movement constellations found in reality, data may not be
available for certain scenarios. This paper proposes a methodology to
systematically analyze relations between parameters of scenarios. Bayesian
networks are utilized to analyze causal dependencies in order to decrease the
amount of required data and to transfer causal patterns creating unseen
scenarios. Thereby, infrastructural influences on movement patterns are
investigated to generate realistic scenarios on unobserved intersections. For
evaluation, scenarios and underlying parameters are extracted from the inD
dataset. Movement patterns are estimated, transferred and checked against
recorded data from those initially unseen intersections.