Modeling and Simulation of Automotive FMCW RADAR Sensor for Environmental Perception

Abstract

Frequency-modulated continuous wave (FMCW) radio detection and ranging (RADAR) sensors have become indispensable technologies for automated driving systems (ADS) due to their reliability in adverse weather conditions and their ability to simultaneously measure the distance to objects, relative radial velocity, and azimuth and elevation angles. The automotive industry has increasingly considered simulation-based testing of autonomous vehicles due to safety, cost, and time constraints. This raises the need for virtual environmental perception sensors that provide results close to reality. This work presents the design and structure of a ray-tracing-based, high-fidelity, tool-independent baseband FMCW RADAR sensor model. The RADAR sensor model is developed using the standardized functional mock-up interface (FMI) and open simulation interface (OSI) and is integrated into the co-simulation environment of commercial software to demonstrate its exchangeability. The RADAR FMU model incorporates a multiple input and multiple output (MIMO) 2D linear spacing virtual antenna array, non-coherent integration (NCI) of range-Doppler maps (RDMs) over receiver antennas, a constant false alarm rate (CFAR) to obtain an interim object detection list, and density-based spatial clustering of applications with noise (DBSCAN) to provide a single detection per object. The presented RADAR FMU model also includes RADAR sensor-specific impairments such as phase noise (PN), radio frequency (RF) group delay, phase imbalance (PI) of transmitter antennas, mixer non-linearity including third-order intermodulation products (IM3), and noise figure (NF) of receiver antennas. Additionally, this work presents a methodology for plausibly verifying the RADAR sensor model at the raw data level (range map (RM) and RDM) and object detection list level. The simulation results are compared with real sensor measurements to validate the modeling of sensor-specific impairments. The mean absolute percentage error (MAPE) metric is used to quantify the difference between the simulation and real sensor measurements. The results demonstrate that the complete signal processing toolchain and sensor-specific impairments of the RADAR sensor must be considered to achieve simulation results that closely resemble those of the real sensor.