The safety validation of motion planning algorithms for automated vehicles requires a large amount of data for virtual testing. Currently, this data is often collected through real test drives, which is expensive and inefficient, given that only a minority of traffic scenarios pose challenges to motion planners. We present a workflow for generating a database of challenging and safety-critical test scenarios that is not dependent on recorded data. First, we extract a large variety of road networks across the globe from OpenStreetMap. Subsequently, we generate traffic scenarios for these road networks using the traffic simulator SUMO. In the last step, we increase the criticality of these scenarios using nonlinear optimization. Our generated scenarios are publicly available on the CommonRoad website.
Scenario Factory: Creating Safety-Critical Traffic Scenarios for Automated Vehicles
20.09.2020
2680504 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
Generating Traffic Safety Test Scenarios for Automated Vehicles using a Big Data Technique
Springer Verlag | 2019
|Generating Traffic Safety Test Scenarios for Automated Vehicles using a Big Data Technique
Online Contents | 2019
|ArXiv | 2023
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