San Diego, CA, United States of America

Long Sha

USPTO Granted Patents = 1 

Average Co-Inventor Count = 1.0

ph-index = 1


Company Filing History:


Years Active: 2025

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1 patent (USPTO):Explore Patents

Title: Long Sha - Innovator in Autonomous Driving Technology

Introduction

Long Sha is an accomplished inventor based in San Diego, CA. He has made significant contributions to the field of autonomous driving technology. His innovative work focuses on enhancing the safety and reliability of autonomous systems through advanced perception anomaly detection.

Latest Patents

Long Sha holds a patent for "Perception anomaly detection for autonomous driving." This patent describes a unified framework for detecting perception anomalies in autonomous driving systems. The framework takes an input image from a camera in or on a vehicle and identifies anomalies as belonging to one of three categories. Lens anomalies are associated with poor sensor conditions, such as water, dirt, or overexposure. Environment anomalies are linked to unfamiliar changes in the environment. Finally, object anomalies pertain to unknown objects. After detecting these perception anomalies, the results are sent downstream to prompt a behavior change in the vehicle.

Career Highlights

Long Sha is currently employed at TuSimple, Inc., where he applies his expertise in autonomous driving technology. His work is pivotal in advancing the capabilities of self-driving vehicles. He has demonstrated a strong commitment to innovation and safety in the automotive industry.

Collaborations

Long Sha collaborates with talented professionals in his field, including Junliang Zhang and Rundong Ge. Their combined efforts contribute to the development of cutting-edge technologies in autonomous driving.

Conclusion

Long Sha's contributions to the field of autonomous driving through his patent and work at TuSimple, Inc. highlight his role as a key innovator in this rapidly evolving industry. His focus on perception anomaly detection is crucial for enhancing the safety and efficiency of autonomous vehicles.

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