Sunnyvale, CA, United States of America

Vidya Nariyambut Murali

This inventor holds 65 USPTO granted patents and 20 published patent applications, primarily in Autonomous Vehicle Technology (CPC class G06T). Top assignee: Ford Global Technolgoies, LLC. Active years: 2018-2026.

IDiyas Innovation Intelligence. (2026). Inventor Profile: Vidya Nariyambut Murali. Retrieved from https://idiyas.com/inventor/vidya-nariyambut-murali

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

Calculating rank
USPTO Granted Patents = 65 

% Patents Active = 83.1

Average Co-Inventor Count = 3.7

ph-index = 7

Forward Citations = 258(Granted Patents)

Forward Citations (Not Self Cited) = 214(Dec 10, 2025)


Inventors with similar research interests:


Location History:

  • Sunnvyale, CA (US) (2019)
  • Dearborn, MI (US) (2022)
  • Sunnyvale, CA (US) (2018 - 2024)

Company Filing History:


Years Active: 2018-2026

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Areas of Expertise:
Hitch Assist
Vehicle Localization
Radar Perception
Machine Learning
Object Detection
Deep Neural Networks
Brake Light Detection
Traffic-Light Classification
Synthetic Data
Visual Odometry
Generative Adversarial Networks
Multi-Sensor Detection
65 patents (USPTO):Explore Patents

Title: Innovations and Contributions of Vidya Nariyambut Murali

Introduction: Vidya Nariyambut Murali, a prolific inventor based in Sunnyvale, California, has made significant contributions to technology with a remarkable portfolio of 59 patents. His innovative work predominantly focuses on the development of efficient neural networks and automated vehicle systems, showcasing his profound impact on the automotive and technology sectors.

Latest Patents: Among his latest patents, the first innovation titled "Efficient Neural Networks" describes a method for determining the location of a first object within an image. This involves drawing a line based on the object's location and training a deep neural network to ascertain a relative position between the first object and a second object in the same image. The deep neural network is optimized by calculating a fitness score that balances the number of parameters with a performance score, ultimately leading to an enhanced output of the neural network.

Another notable patent is for "Brake Light Detection," which illustrates advanced systems, methods, and devices for the detection of vehicle brake lights. This system comprises a mode component that selects between night and day modes based on pixel brightness in an image frame. It also includes a vehicle region component, detecting areas corresponding to vehicles through data from a range sensor in night mode or from camera image data in day mode. Furthermore, the classification component classifies the state of the vehicle’s brake light—whether it is on or off—based on image data from the designated vehicle region.

Career Highlights: Vidya Nariyambut Murali's career is marked by his creative expertise in developing cutting-edge technologies at Ford Global Technologies, LLC. His ability to integrate advances in deep learning and image processing has positioned him as a key player in enhancing automotive safety and efficiency.

Collaborations: Throughout his career, Murali has collaborated with esteemed colleagues, including Guy Hotson and Ashley Elizabeth Micks. These partnerships have fostered a dynamic exchange of ideas, contributing to the successful development of innovative solutions in his field.

Conclusion: Vidya Nariyambut Murali stands out as a leading inventor whose work not only enriches the patent landscape but also significantly impacts the fields of technology and automotive safety. With a commitment to innovation and excellence, Murali continues to push boundaries, inspiring advancements that may shape the future of these industries.

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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