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PRODID:IEEE vTools.Events//EN
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DTSTART:20240310T030000
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DTSTAMP:20241021T212602Z
UID:F7CA586B-608F-4339-B783-7706AD426CDF
DTSTART;TZID=America/New_York:20241018T162500
DTEND;TZID=America/New_York:20241018T173000
DESCRIPTION:Millimeter wave automotive radars are highly reliable in all-we
 ather environments and are indispensable for both advanced driver assistan
 t systems (ADAS) and autonomous vehicles. The presentation will highlight 
 recent advancements that continue to push the boundaries of what’s possi
 ble in automotive radar technology for fully autonomous driving. Key topic
 s include the use of sparse arrays synthesized with multi-input multi-outp
 ut (MIMO) radar technology to achieve enhanced angular resolution\, innova
 tive waveform designs for mitigating mutual interference between automotiv
 e radars\, and high-resolution direction-of-arrival (DOA) estimation from 
 a single snapshot. We will also address the challenges of applying MIMO ra
 dar theories in automotive contexts\, including the use of model-based dee
 p neural networks for high-resolution DOA estimation and the integration o
 f domain-knowledge-guided deep learning in radar perception for autonomous
  vehicles. Finally\, we will discuss future directions for automotive rada
 r\, with a focus on enhanced collaborative sensing through the use of mult
 iple automotive radar systems.\n\nSpeaker(s): Prof Shunqiao Sun\n\nRoom: 4
 66\, Bldg: Packard Lab\, 19 Memorial Drive West\, Lehigh University\, Beth
 lehem\, Pennsylvania\, United States\, 18015
LOCATION:Room: 466\, Bldg: Packard Lab\, 19 Memorial Drive West\, Lehigh Un
 iversity\, Bethlehem\, Pennsylvania\, United States\, 18015
ORGANIZER:rb0f@lehigh.edu
SEQUENCE:45
SUMMARY:Redefining Millimeter-Wave Automotive Radar for Autonomous Driving:
  Advanced Signal Processing and Machine Learning Approaches
URL;VALUE=URI:https://events.vtools.ieee.org/m/436257
X-ALT-DESC:Description: <br /><p>Millimeter wave automotive radars are high
 ly reliable in all-weather environments and are indispensable for both adv
 anced driver assistant systems (ADAS) and autonomous vehicles. The present
 ation will highlight recent advancements that continue to push the boundar
 ies of what&rsquo\;s possible in automotive radar technology for fully aut
 onomous driving. Key topics include the use of sparse arrays synthesized w
 ith multi-input multi-output (MIMO) radar technology to achieve enhanced a
 ngular resolution\, innovative waveform designs for mitigating mutual inte
 rference between automotive radars\, and high-resolution direction-of-arri
 val (DOA) estimation from a single snapshot. We will also address the chal
 lenges of applying MIMO radar theories in automotive contexts\, including 
 the use of model-based deep neural networks for high-resolution DOA estima
 tion and the integration of domain-knowledge-guided deep learning in radar
  perception for autonomous vehicles. Finally\, we will discuss future dire
 ctions for automotive radar\, with a focus on enhanced collaborative sensi
 ng through the use of multiple automotive radar systems.<span class="Apple
 -converted-space">&nbsp\;</span></p>
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