Validation Framework for Generic Radar Sensor Models

Abstract

Automotive radar sensors are vital in Advanced Driver Assistance Systems (ADAS). To be more precise, their ability to explicitly measure the relative velocity to its targets is essential in Adaptive Cruise Control (ACC) and Emergency Braking (EB) applications. Nevertheless, ADAS are getting more and more complex, due to constantly increasing demands regarding safety and performance. As a result, to speed up the development and validation time of ADAS, part of the testing is performed in simulations. Replacing some of the test drives by the runs in virtual environments not only reduces the cost of a product, but also helps in fully safe execution of dangerous corner cases. However, to enable reliable testing of radar-based ADAS in virtual environments, high-fidelity radar sensor models are required. In order to prove the reliability of a given model, a proper evaluation process has to be conducted. This paper presents an end-to-end, straightforward methodology for performance assessment and fine-tuning of radar sensor models. To show how the full pipeline of the framework can be executed, an exemplary radar sensor model has been incorporated. The successful fine-tuning of the model proves the usefulness of the introduced method.