Smart Radar Critical for Improving Autonomous Vehicle Perception
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Improving Radar with AI
By mixing machine learning with synthetic aperture techniques that can keep pace with software innovations, a Radar sensor can send out an adaptive phase-modulated waveform that effectively increases the sensor’s angular resolution by up to a factor of 100.
This innovative approach relies on an adaptive phase-modulated waveform that changes dynamically in real time with the environment – no additional antennas required. This dramatically improves the resolution, increases the range, and widens the field of view without impacting the bill of materials or adding costs to the system.
Today, AI-enabled, ‘smart’ radar sensors are capable of generating images with tens of thousands of pixels per frame and tracking targets that are hundreds of meters away, which in turn enables AV systems to operate safely at high speeds. Perhaps most compelling of all, this approach can be tailored to support advanced driver-assistance systems (ADAS) or autonomous robotic applications, where low energy usage is critical.
Smart Radar Not Just for AV Applications
While the current focus of this autonomous navigation software is automotive perception, the size, power, and performance of these boosted Radar solutions may unlock new opportunities for robotics in other vertical markets.
As the automotive industry inspires advances in perception, we will see the capabilities of software powered, ‘smart’ Radars increase dramatically because they are built on machine learning algorithms that will continue to improve over time. For OEMs, this means that cars will get much better at recognizing pedestrians, objects, and other vehicles, but for scientists and engineers, these advances could be applied to myriad other projects.
While I hope that the small size, low power requirements, and low cost of new Radars entering auto designs in the near term, I am confident that these can help overcome more challenges than perception in AVs.
About the Author
Steven Hong is currently the VP / General Manager of Radar Technology at Ambarella (NASDAQ: AMBA). He joined Ambarella through its acquisition of Oculii, where he was the CEO / Co-Founder, growing the company to become the leading provider of AI Software for Radar Perception. Prior to founding Oculii, Hong was a partner at Kleiner Perkins where he invested in early stage (Seed/Series A) HardTech companies pioneering Autonomous Systems, AI + Machine Learning, IoT, 3D Printing, and Robotics. Before KP, he co-founded Kumu Networks, where he was responsible for product management, fundraising, IP strategy, business development, and marketing. Hong started his career as a management/strategy consultant at McKinsey and Uber, where he specialized in M&A diligence and expansion strategy. He holds a PhD and MS in Electrical Engineering from Stanford University, and a BS in Electrical Engineering from the University of Michigan.
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