A Data-driven Approach for Stochastic Modeling of Automotive Radar Detections for Extended Objects

Radar sensors play an important role in automated driving technologies. However, the rising number of sensors deployed to enable autonomous driving functions leads to enormous validation efforts. While simulations are a possible approach to accelerate the validation process, the development effort for realistic sensor models increases significantly. Data-driven sensor models offer the possibility to replicate sensor data accurately and efficiently. Using real measurement data, the sensor output can be simulated without the detailed parametric modeling of the wave propagation and sensor effects. In this paper, the radar signatures of a passenger vehicle under a constant aspect angle are analyzed in real measurements. Then, a data-driven approach for stochastically modeling the radar target detections is presented. The model is trained with real sensor data to achieve a high degree of realism. A qualitative comparison between the simulated and measured detections reveals promising results.