AUTOMOTIVE ELECTRONICS
the antennas introduces a change in the echo’s angle of arrival (AoA), which might lead to errors and loss of accuracy in rendering targets, if not recalculated.
Inability to generate objects at distances of less than 4 meters
Many test cases, such as the New Car Assessment Program’s (NCAP) Vulnerable Road User Protection – AEB Pedestrian, require object emulation very close to the radar unit. Most of the target simulation solutions existing on the market today are designed for long distances.
Lower resolution between objects Up until now, target simulators could only process one object as one radar signature – this leaves gaps in scene details. For example, on a crowded multi-lane boulevard, test equipment must accurately tell the difference between all the traffic participants. With only one echo per object, the algorithm might not be able to tell the difference between a bicycle and a lamp post.
New technology is needed Full-scene emulation in the lab is key to developing the robust radar sensors and algorithms needed to realise ADAS capabilities on the path to full vehicle autonomy. One method is to shift from an approach
centered on object detection via target simulation to traffic scene emulation. This will enable the ability to emulate complex scenarios, including coexisting high-resolution objects, with a wide field of view and a reduced minimum object distance.
The sensor’s entire FOV must be covered to achieve high test coverage and run comprehensive test scenarios. A wide FOV is needed, ideally with RF front ends that are static in space, to enable reproducible and accurate AoA validation.
Realistic traffic scenes require the emulation of objects very close to the radar unit. For example, at a stoplight where cars are no more than two meters apart, bikes might move into the lane or pedestrians might suddenly cross the road. Passing this test is critical for the safety features of an ADAS/AD.
Object separation, the ability to distinguish between obstacles on the road, is another test area for a smoother and faster transition to level four and five vehicles. For example, a radar detection algorithm will need to differentiate between a guard rail and a pedestrian while the car is driving on a highway.
Achieve greater confidence in ADAS functionality
More targets, shorter minimum distance, higher resolution, and a continuous field of view are
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essential to real world testing. In the lab, this will enable an increase in test coverage to not only save time, but safely run and repeat test scenarios.
A traditional radar target simulator (RTS) will return one reflection independent of distance while a radar scene emulator increases the number of reflections as the vehicle gets closer, also known as dynamic resolution. This means the number of objects varies with the distance of the object. AD and ADAS software decisions must be based on the complete picture, not only on what the test equipment allows.
Author
Silviu Tuca is the radar-based autonomous vehicle product line manager for Keysight Technologies. After obtaining an EE master’s degree in RF electronics, and a PhD in Biophysics, he has spent his professional life working with test and measurement equipment –developing new calibration methods, technical consulting, or articulating the value of those instruments. Silviu is in Stuttgart, Germany, and in his free time enjoys being outdoors, listening to audiobooks or podcasts, and a good philosophical discussion.
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