In the real world, countless variables affect road conditions, including weather, lighting, traffic, and rare road hazards. Discovering errors late in the development cycle of your autonomous driving algorithm can lead to extended development times and significantly increased costs. These errors often stem from insufficient test coverage of scenarios that are either untested or unforeseeable in the field, such as dangerous high-speed situations or edge cases. Some effects are only visible for a few cycles, making them difficult to test or reproduce in a lab environment. Additionally, training radar sensor algorithms with real road tests is both impractical and expensive. How can you confidently address these scenarios early in the lab?
Keysight will discuss overcoming limitations of radar target simulation in HIL testing:
- Verify how the real radar module perceives traffic actors and behaves before you expose it to the real world
- Stress-test the algorithm with reflections from different surfaces, different size objects, objects behind objects, guard rails, and overpasses
- Learn what you can accomplish when >1000 simultaneous reflections are possible
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