Guangdong, China

Feng Pei

This inventor holds 1 USPTO granted patent and 2 published patent applications. Top assignee: Guangzhou Automobile Group Co., Ltd.. Active years: 2022.

IDiyas Innovation Intelligence. (2026). Inventor Profile: Feng Pei. Retrieved from https://idiyas.com/inventor/feng-pei

Data source: USPTO (United States Patent and Trademark Office) public patent records. Weekly synchronization. How IDiyas builds this profile

USPTO Granted Patents = 1 

% Patents Active = 100.0

Average Co-Inventor Count = 6.0

ph-index = 1


Company Filing History:


Years Active: 2022

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1 patent (USPTO):Explore Patents

Title: Innovations of Feng Pei in Unmanned Vehicle Technology.

Introduction

Feng Pei is an innovative inventor based in Guangdong, China. He has made significant contributions to the field of unmanned vehicle technology. His work focuses on enhancing the safety and efficiency of autonomous driving systems.

Latest Patents

Feng Pei holds a patent for an "Unmanned lane keeping method and device, computer device, and storage medium." This patent describes a method that involves receiving a vehicle road image collected by a data collector. The image is then processed using a deep neural network (DNN) model to determine the appropriate steering wheel angle. This technology allows the vehicle to maintain its lane autonomously, showcasing the advancements in deep learning applications for vehicle control.

Career Highlights

Feng Pei is associated with Guangzhou Automobile Group Co., Ltd., where he applies his expertise in developing cutting-edge automotive technologies. His work has contributed to the evolution of smart vehicles, making them safer and more reliable for everyday use.

Collaborations

Feng Pei collaborates with talented individuals such as Hanfei Shi and Qiaojun He. Together, they work on innovative projects that push the boundaries of automotive technology.

Conclusion

Feng Pei's contributions to unmanned vehicle technology exemplify the potential of innovation in the automotive industry. His patent on lane-keeping methods highlights the importance of deep learning in enhancing vehicle autonomy.

Profile summary based on public USPTO records.
Data Sources: USPTO Patent Grant XML, Patent Center, EPO & CIPO • Normalized by IDiyas Innovation Graph. Methodology & provenance architecturePlease report any incorrect information to [email protected]
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