A Data-driven Waveform Adaptation Method for mm-Wave Gait Classification at the Edge

The ever-increasing need for real-time 3D perception for autonomy has resulted in development of a new generation of high-resolution perception sensors paired with powerful edge processors and advanced perception algorithms. However, processing of the large data volumes generated by such high-resolution sensors still imposes a great challenge for resource-limited edge devices and systems. In this work, we visit this problem in the context of pedestrian gait analysis using high-resolution mm-Wave sensors with application in autonomous driving. We propose reducing the total computational load of a resource-limited perception system by dynamically controlling the mm-Wave sensor's resolution on the fly. We provide results on three dynamic sensor adaptation approaches including an end-to-end Deep Q-Learning approach that can overcome the resource and performance limitations inherent to conventional static methods.