Maneuver-based Visualization of Similarities between Recorded Traffic Scenarios

Since automated driving functions are safety-critical systems, extensive validation and verification is necessary. Scenario-based testing is a promising approach for this challenge. For selection of relevant scenarios, collected data and knowledge models are potential sources. In this paper we introduce a concept to use recorded trajectory and map data, abstracted to maneuvers, to describe the scenarios and visualize them intuitively. This enables a data-driven scenario-mining process to find relevant scenarios for the testing of automated driving functions. To compare the scenarios, a similarity measure based on the manuevers is designed and the scenarios and their similarities are represented as a graph. Graph-visualization methods, already successfully applied in other domains, structure the collected data for further analysis. The concept is exemplary applied to an urban traffic dataset.