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MIT researchers pursue better vehicle vision

22nd February 2019


Autonomous vehicles relying on light-based image sensors often struggle to see through conditions such as fog. But MIT researchers have developed a sub-terahertz radiation receiving system that could help steer driverless cars when traditional methods fail.

Sub-terahertz wavelengths, which are between microwave and infrared radiation on the electromagnetic spectrum, can be detected through fog and dust clouds with ease, whereas the infrared-based LiDAR imaging systems used in autonomous vehicles struggle. To detect objects, a sub-terahertz imaging system sends an initial signal through a transmitter; a receiver then measures the absorption and reflection of the rebounding sub-terahertz wavelengths. That sends a signal to a processor that recreates an image of the object.
But implementing sub-terahertz sensors into driverless cars is challenging. Sensitive, accurate object-recognition requires a strong output baseband signal from receiver to processor. Traditional systems, made of discrete components that produce such signals, are large and expensive. Smaller, on-chip sensor arrays exist, but they produce weak signals.

The researchers built a prototype, which has a 32-pixel array integrated on a 1.2 square millimeter device. The pixels are approximately 4,300 times more sensitive than the pixels in today’s best on-chip sub-terahertz array sensors. With a little more development, the chip could potentially be used in driverless cars and autonomous robots.
“A big motivation for this work is having better ‘electric eyes’ for autonomous vehicles and drones,” said co-author Ruonan Han, an associate professor of electrical engineering and computer science, and director of the Terahertz Integrated Electronics Group in the MIT Microsystems Technology Laboratories (MTL). “Our low-cost, on-chip sub-terahertz sensors will play a complementary role to LiDAR for when the environment is rough.”
Joining Han on the paper are first author Zhi Hu and co-author Cheng Wang, both PhD students in in the Department of Electrical Engineering and Computer Science working in Han’s research group.

 

 







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