20240085914.DETERMINING PERCEPTION ZONES FOR OBJECT DETECTION IN AUTONOMOUS SYSTEMS AND APPLICATIONS simplified abstract (nvidia corporation)

DETERMINING PERCEPTION ZONES FOR OBJECT DETECTION IN AUTONOMOUS SYSTEMS AND APPLICATIONS

Organization Name

nvidia corporation

Inventor(s)

Sever Ioan Topan of Burnaby (CA)

Karen Yan Ming Leung of Los Altos CA (US)

Yuxiao Chen of Sunnyvale CA (US)

Pritish Tupekar of Santa Clara CA (US)

Edward Fu Schmerling of Los Altos CA (US)

Hans Jonas Nilsson of Los Gatos CA (US)

Michael Cox of Menlo Park CA (US)

Marco Pavone of Stanford CA (US)

DETERMINING PERCEPTION ZONES FOR OBJECT DETECTION IN AUTONOMOUS SYSTEMS AND APPLICATIONS - A simplified explanation of the abstract

This abstract first appeared for US patent application 20240085914 titled 'DETERMINING PERCEPTION ZONES FOR OBJECT DETECTION IN AUTONOMOUS SYSTEMS AND APPLICATIONS

Simplified Explanation

The patent application describes techniques for determining perception zones for object detection using dynamic models associated with an ego-machine and an object, as well as possible interactions between them. The system then uses the perception zone to validate a perception system or determine safety-critical objects.

• Dynamic models associated with an ego-machine and an object are used to determine perception zones.
• The system can validate a perception system by checking if an object is within the perception zone.
• Safety-critical objects can be identified based on their location within the perception zone.

Potential Applications

The technology can be applied in autonomous vehicles, robotics, surveillance systems, and industrial automation for improved object detection and safety.

Problems Solved

- Enhanced object detection accuracy - Identification of safety-critical objects - Validation of perception systems

Benefits

- Increased safety in various applications - Improved efficiency in object detection - Enhanced decision-making capabilities

Potential Commercial Applications

"Enhancing Object Detection and Safety in Autonomous Vehicles and Robotics"

Possible Prior Art

Prior art may include existing object detection systems in autonomous vehicles and robotics that use dynamic models for perception zones.

Unanswered Questions

How does the system handle dynamic environments where objects are constantly moving?

The system may need to continuously update the perception zones based on real-time data to account for moving objects.

What are the limitations of the perception zones in terms of object size and shape?

The system may have constraints in accurately detecting very small or irregularly shaped objects within the perception zone.

Original Abstract Submitted

in various examples, techniques for determining perception zones for object detection are described. for instance, a system may use a dynamic model associated with an ego-machine, a dynamic model associated with an object, and one or more possible interactions between the ego-machine and the object to determine a perception zone. the system may then perform one or more processes using the perception zone. for instance, if the system is validating a perception system of the ego-machine, the system may determine whether a detection error associated with the object is a safety-critical error based on whether the object is located within the perception zone. additionally, if the system is executing within the ego-machine, the system may determine whether the object is a safety-critical object based on whether the object is located within the perception zone.