Synthetic Dataset Generation Using Logical Scenario Files for Automotive Perception Testing

Conducting extensive recording campaigns to asses the safety of newly developed Automated Driving Systems (ADS) or perception algorithms has been proved to be a costly and time consuming process. This is one of the reasons why the automotive industry is adopting the scenario-based testing methodology, to verify and validate the safety of the developed ADS in their expected operating domain. The exterior perception system is the first component in the sense-plan-act process of Connected Cooperative and Automated Vehicles (CCAVs). In this context, high-fidelity simulation engines are used to replicate sensor setups at reduced cost and higher scalability than driving and capturing data from real sensors. The use of logical automotive scenario descriptions allows defining certain parameter ranges, contexts and actions to execute simulations that fulfill the desired conditions. This work proposes a methodology for generating synthetic labelled datasets to test and validate automotive perception systems using logical scenario files. Decoupling the desired sensor setup from the simulation allows reproduction and testing the same situation under different sensor setups and conditions. We implement the methodology to validate three 3D LiDAR-based object detectors in three different sensor setups. The generated sample dataset will be made public here<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>1https://github.com/Vicomtech/Synthetic-OpenLABEL-Scenarios.