Beyond the higher frequency and bandwidth requirements of automotive radar testing, the next challenge of testing future radar sensors is the validation of increasingly complex software built into sensors. A radar sensor with 1GHz or more of bandwidth produces massive amounts of raw data. To avoid overwhelming the communication buses and ECU of the vehicle, radar sensors include a processor to reduce this data into a summarized snapshot. Periodically, the radar transmits a parameterized object table with a summary of all the objects currently tracked by the sensor. Each object includes a range, velocity, radar cross-section (RCS), object ID and confidence (a measure of the radar’s confidence that an object exists). The radars software detects these objects and tracks their real-time movements. Algorithms look for inconsistencies such as an obstacle that is moving away from the sensor but has a Doppler signature that indicates the obstacle is approaching.
In the lab, engineers must validate these algorithms and the software that implements them. Lab testing with a compact radar test system allows software developers to quickly validate software changes immediately. Combined with motion systems, such as a small robotic arm to move the radar simulator antennas, the NI VRTS can generate standardized radar environments to characterize and validate radar sensor software, including simulating corner case scenarios that would be difficult or dangerous to emulate with drive testing. Lab testing with simulators is critical to maintaining the pace of innovation of automotive radar sensor design.
Within the context of the entire ADAS or autonomous driving system, engineers must also consider radar emulation for system validation test. Increasingly, these systems rely on a combination of sensors, including cameras, LIDAR and radar. Validating the overall performance of an ADAS function like Forward Collision Warning and Automatic Emergency Braking increasingly utilizes sensor fusion, the combination of two or more sensors to improve the quality or increase the confidence of an obstacle detection. For example, if the ADAS radar sensors detect an obstacle but the cameras indicate the path is clear, then the ECU may disregard the radar obstacle as a ghost or interference.