Dr. Shaoshan LiuBased in Fremont, CA, United States
Dr. Shaoshan Liu is the Founder and Chairman of PerceptIn (www.perceptin.io), a company focused on providing visual perception solutions for robotics and autonomous driving. Since its inception, PerceptIn has attracted over 11 million USD of funding from top-notch venture capital firms such as Walden International, Matrix Partners, and Samsung Ventures. He is a member of ACM.
Prior to founding PerceptIn, Dr. Shaoshan Liu was a founding member of Baidu U.S.A. as well as the Baidu Autonomous Driving Unit where he led the company’s system integration of autonomous driving systems. Dr. Shaoshan Liu received his Ph.D. in Computer Engineering from the University of California, Irvine and executive education from Harvard Business School.
His research focuses on Computer Architecture, Deep Learning Infrastructure, Robotics, and Autonomous Driving. Dr. Shaoshan Liu has published over 40 research papers and holds over 150 U.S. international patents on robotics and autonomous driving. He is also the lead author of the best selling textbook "Creating Autonomous Vehicle Systems,” which is the first technical overview of autonomous vehicles written for a general computing and engineering audience.
In addition, as a senior member of IEEE, Dr. Shaoshan Liu co-founded the IEEE Special Technical Community on Autonomous Driving Technologies and served as its Founding Vice President. Dr. Shaoshan Liu’s research work has made a major impact on the robotics and autonomous driving industry. His patented DragonFly visual perception technology, the “people’s autonomous vehicle,” is the world’s first safe, affordable, and reliable autonomous vehicle. The DragonFly enables reliable and low-speed autonomous driving and costs under $10,000 USD when mass-produced. The vehicle represents a breakthrough step towards the ubiquitous deployment of autonomous driving globally. Dr. Shaoshan Liu’s work has received international recognition both within and outside the technology community. Select media coverage includes Forbes, the L.A. Times, IEEE Spectrum, TechCrunch, ReadWrite, China Daily, Science and Technology Daily, Nikkei Robotics, and Wedge.
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?-BA: Bundle Adjustment Acceleration on Embedded FPGAs with Co-Observation Optimization
Bundle adjustment (BA) is a fundamental optimization technique used in many crucial applications, including 3D scene reconstruction, robotic localization, camera calibration, autonomous driving,...
?-RT: A Runtime Framework to Enable Energy-Efficient Real-Time Robotic Applications on Heterogeneous Architectures
Enabling full robotic workloads with diverse behaviors on mobile systems with stringent resource and energy constraints remains a challenge. In recent years, attempts have been made to deploy...
DragonFly+: An FPGA-based quad-camera visual SLAM system for autonomous vehicles
PerceptIn’s DragonFly system utilizes computer vision-based sensor fusion to achieve reliable localization. Specifically, DragonFly integrates four cameras (with 720p resolution) into...
- Edge Computing for Autonomous Driving: Opportunities and ChallengesSafety is the utmost important requirement for autonomous vehicles, hence the ultimate challenge of designing an edge computing ecosystem for autonomous vehicles is to deliver enough computing...
- Enabling Computer-vision-based Autonomous Driving with Affordable and Reliable sensorsAutonomous vehicles, like humans, need good eyes and a good brain to drive safely. Traditionally, LiDAR is the main sensor in autonomous driving, and it is the critical piece in both...
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- Edge Computing for Autonomous Driving: Opportunities and Challenges