Abstract
Automated driving is seen as one of the key technologies that shape our future mobility. Testing these automated driving functions (ADF) in virtual environments has the potential to speed up their development and homologation. As the automated driving functions rely on sensors to perceive the environment, a key requirement for virtual testing is the ability to simulate the environment perception of the involved sensors. In this paper we present a concept for environment perception simulation of radar sensors (EPSR)—namely radar signature and stimulation input generation (RASIG)—to be employed in the context of vehicle-in-the-loop (ViL) tests in conjunction with over-the-air (OTA) stimulation hardware. The requirements on environment perception simulation of radar sensors for integration into such a test set-up and its real-time capability along with some validation results are discussed.
Highlights
The automotive industry is working towards one of its most significant changes in history: driving automation
As automated driving functions (ADF) rely on sensors to perceive their environment, a key requirement for virtual testing is environment perception simulation of involved sensors (EPSS) which is currently being researched intensively e.g. in the research projects PEGASUS [3] and ENABLES [4]
In this article we presented OTA stimulation chain for automotive radar in ADF ViL testing and discussed requirements on environment perception simulation of radar sensors (EPSR) as well as the OTA stimulation chain
Summary
The automotive industry is working towards one of its most significant changes in history: driving automation. 3. Radar signature and stimulation input generation (RASIG) The presented RASIG employs a ray tracing based BRDF approach to RCS computation, for which knowledge of position, orientation, and object data such as material properties of all surfaces in the test scenario are required. Radar signature and stimulation input generation (RASIG) The presented RASIG employs a ray tracing based BRDF approach to RCS computation, for which knowledge of position, orientation, and object data such as material properties of all surfaces in the test scenario are required Based on this information from the environment simulation RASIG computes SPs in two steps as described in Sect. The stimulation point contains Doppler shift fD and RCS σ from the echo point and the azimuth and distance are computed by adding the computed REV to the reference point This method allows stimulation points to be updated in less than 1 ms, realizing real time capability. Both results show a good agreement in trends
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