Palo Alto, CA, United States of America

Henru Wang


Average Co-Inventor Count = 5.5

ph-index = 1

Forward Citations = 3(Granted Patents)


Company Filing History:


Years Active: 2020-2024

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3 patents (USPTO):Explore Patents

Title: **Henru Wang: Innovator in Perception Data Augmentation**

Introduction

Henru Wang is a distinguished inventor based in Palo Alto, California, known for his contributions to the field of perception data systems. With a total of three patents, his work focuses on enhancing the way vehicles interpret and interact with their surroundings. His innovations have far-reaching implications for the automotive and technology sectors.

Latest Patents

Henru Wang's latest patents revolve around "Systems and methods for augmenting perception data with supplemental information." These advancements involve a sophisticated computing system capable of obtaining previously-derived perception data from a sequence of frames captured by a vehicle. The innovation allows for the derivation of supplemental object-level information that enriches existing data. The augmented perception data is then stored in a structured format that captures the hierarchical relationships between numerous objects, frames, and scenes, significantly improving the vehicle's understanding of its environment.

Career Highlights

Throughout his career, Henru Wang has worked at prominent companies such as Lyft, Inc. and Ouster, Inc. His expertise in developing advanced computing systems has established him as a notable figure in the tech industry, particularly in the realm of autonomous vehicles and sensor data processing.

Collaborations

Henru has collaborated with talented individuals, including Mushfiqur Rouf and Ashesh Jain. These partnerships have not only contributed to his own innovations but also fostered advancements in the collaborative study of perception data systems.

Conclusion

Henru Wang continues to be a pioneer in enhancing vehicle perception through innovative technologies. His work stands as a testament to the importance of developing systems that can effectively interpret the complexities of surrounding environments, paving the way for safer and more intelligent vehicular navigation.

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