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Written By: Hwee Yng Yeo, Smart Mobility Advocate, Keysight Technologies

Hwee Yng Yeo, Smart Mobility Advocate, Keysight Technologies

Automotive radar systems need more rigorous testing as they assume critical functions in many of today’s Currently, automakers and radar module providers test the functionality of their radar modules using both software and hardware. There are two key methods for hardware-based tests.

The first uses corner reflectors that are placed at different distances and angles from the radar device under test (DUT), with each reflector representing a static target. When a change of this static scenario is needed, the corner reflectors must be physically moved to their new positions. The procedures are time-consuming and add to overall test time. Each movement of the antennas introduces a change in the echo’s angle of arrival, which might lead to errors and loss of accuracy in rendering targets if not recalculated or recalibrated.

The second uses a radar target simulator (RTS) that enables an electronic simulation of the radar targets, thus allowing for both static and dynamic targets, along with simulating the target’s distance, velocity, and size. Limitations of RTS-based functional testing arise for complex / realistic scenarios with more than 32 targets. RTS-based testing also cannot characterize 4D (including vertical positions, on top of distance, horizontal positions, and velocities) and imaging radar capabilities for detecting extended objects, which are objects represented by point clouds instead of just one reflection.

Figure 1. Testing automotive radar sensors with radar target simulation does not provide a complete traffic scene for validating autonomous driving applications.

Testing radar units against a limited number of objects delivers an incomplete view of driving scenarios for the AV. It masks the complexity of the real world, especially in urban areas with different intersections and turning scenarios involving pedestrians, cyclists, and electric scooters.

Smartening Automotive Radar Algorithms

Increasingly, machine learning is helping developers train their ADAS algorithms to better interpret and classify data from radar sensors and other sensor systems. More recently, the term “YOLO” has surfaced amongst headlines on automotive radar algorithms. The acronym stands for “You only look once.” This acronym is apt, given what the radar perceives and how the ADAS algorithms interpret the data are mission-critical processes, which can be a matter of life or death. YOLO-based radar target detection aims to accurately detect and classify multiple objects simultaneously.

It is crucial to subject both the physical radar sensors and the ADAS algorithms to rigorous testing before these self-driving systems move onto the final costly road-testing stage. To create a more realistic 360^o view of varying real-world traffic scenarios, automakers have started using radar scene emulation technology to bring the road to the lab.

One of the key challenges of upgrading autonomous driving is the ability to distinguish between dynamic obstacles on the road and autonomously deciding on the course of action versus just raising a flag or flashing a warning on the dashboard. In emulating a traffic scenario, too few points per object may cause a radar to erroneously detect closely spaced targets as a single entity. This makes it difficult to fully test not just the sensor but also the algorithms and decisions that rely on data streaming from the radar sensor.

A new radar scene emulation technology uses ray tracing and point clouds to extract the relevant data from highly realistic simulated traffic scenes and provides better detection and differentiation of objects (see Figure 2). Using novel millimetre wave (mmWave) over-the-air technology, the radar scene emulator can generate multiple static and dynamic targets from as close as 1.5 metres to as far away as 300 meters, and with velocities of 0 to 400 km/h for short, medium, and long-range automotive radars. This provides a much more realistic traffic scenario against which the automotive radar sensors can be tested.

Figure 2. A screen capture of perception algorithm testing using radar scene emulation. The right screen shows a simulated traffic scenario which is emulated by the radar scene emulator on the left. The green dots represent emulated radar reflections while the red dots show radar sensor detections.

Radar scene emulation is extremely useful for pre-roadway drive tests as both automotive radar sensors and the algorithms can undergo numerous design iterations quickly to fix bugs and finetune designs. Apart from ADAS and autonomous driving functional testing, it can help automakers with variant handling applications, such as validating the effect of different bumper designs, paintwork, and radar module positioning on radar functions.

For autonomous driving platform providers and radar systems manufacturers, enhancing the vehicle’s perception of different realistic traffic scenes through multiple repeatable and customisable scenarios can allow the radar sensors to capture vast amounts of data for Machine Learning by autonomous driving algorithms.

Nowadays, advanced digital signal processing (DSP) also plays a crucial role in enabling finetuning of individual radar detections. For instance, the radar can pick up different points of a pedestrian’s arms and legs, including the velocity, distance, cross section (size), and angle (both horizontally and vertically), as illustrated in Figure 3. This provides vital information for training radar algorithms to identify pedestrians, versus the digital 4D form factor of say, a dog, crossing the road.

Figure 3. Advanced digital processing with radar scene emulation provides greater granularity of dynamic targets, such as a moving pedestrian.

The Road to Super Sensors Begins with Reliable Testing

From chip design to its fabrication and subsequent radar module testing, each step of the automotive radar design, development, and fabrication lifecycle demands rigorous testing.

“Automotive radar systems need more rigorous testing as they assume critical functions in many of today’s Currently, automakers and radar module providers test the functionality of their radar modules using both software and hardware. There are two key methods for hardware-based tests.”

There are many test challenges when working with mmWave frequencies for automotive radar applications. Engineers need to consider the test setup, ensure the test equipment can carry out ultra-wideband mmWave measurements, mitigate signal-to-noise ratio loss, and meet emerging standards requirements by different market regions for interference testing.

At the radar module level, testing of modern 4D and imaging radar modules requires test equipment that can provide more bandwidth and better distance accuracy.

Finally, the ultimate challenge is integrating the automotive radar into the ADAS and automated driving system and subjecting the algorithms from standard driving situations to the one-in-a-million corner case. The well-trained and tested radar super sensor system will ensure smoother and safer rides for passengers as more drivers take a back seat in the future.

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