Nvidia corporation (20240127454). INTERSECTION REGION DETECTION AND CLASSIFICATION FOR AUTONOMOUS MACHINE APPLICATIONS simplified abstract

INTERSECTION REGION DETECTION AND CLASSIFICATION FOR AUTONOMOUS MACHINE APPLICATIONS

Organization Name

nvidia corporation

Inventor(s)

Trung Pham of Santa Clara CA (US)

Berta Rodriguez Hervas of San Francisco CA (US)

Minwoo Park of Saratoga CA (US)

David Nister of Bellevue WA (US)

Neda Cvijetic of East Palo Alto CA (US)

INTERSECTION REGION DETECTION AND CLASSIFICATION FOR AUTONOMOUS MACHINE APPLICATIONS - A simplified explanation of the abstract

This abstract first appeared for US patent application 20240127454 titled 'INTERSECTION REGION DETECTION AND CLASSIFICATION FOR AUTONOMOUS MACHINE APPLICATIONS

Simplified Explanation

The abstract describes a patent application related to using live perception from sensors of a vehicle to detect and classify intersection contention areas in real-time or near real-time. This involves training a deep neural network to compute outputs like signed distance functions to identify boundaries of intersection contention areas, which are then decoded to determine instance segmentation masks representing the locations and classifications of intersection areas.

• The patent application leverages live perception from vehicle sensors to detect and classify intersection contention areas.
• A deep neural network is trained to compute outputs such as signed distance functions to identify boundaries of intersection contention areas.
• The signed distance functions are decoded to determine instance segmentation masks representing the locations and classifications of intersection areas.
• The locations of intersection areas are generated in image-space and converted to world-space coordinates to aid autonomous or semi-autonomous vehicles in navigating intersections according to traffic rules and priorities.

Potential Applications

This technology can be applied in autonomous vehicles, traffic management systems, and smart city infrastructure.

Problems Solved

This technology helps in improving the safety and efficiency of navigating intersections, reducing the risk of accidents and traffic congestion.

Benefits

The benefits of this technology include enhanced decision-making for vehicles at intersections, improved traffic flow, and overall safer road conditions.

Potential Commercial Applications

Potential commercial applications include integration into autonomous vehicle systems, traffic control systems, and urban planning solutions.

Possible Prior Art

Prior art may include similar patents related to using sensor data for real-time analysis in autonomous vehicles or traffic management systems.

Unanswered Questions

How does the deep neural network handle complex intersection scenarios with multiple lanes and traffic signals?

The patent abstract does not provide specific details on how the deep neural network processes complex intersection scenarios.

What are the potential limitations or challenges in implementing this technology in real-world environments?

The abstract does not address potential obstacles or limitations that may arise when deploying this technology in diverse real-world conditions.

Original Abstract Submitted

in various examples, live perception from sensors of a vehicle may be leveraged to detect and classify intersection contention areas in an environment of a vehicle in real-time or near real-time. for example, a deep neural network (dnn) may be trained to compute outputs—such as signed distance functions—that may correspond to locations of boundaries delineating intersection contention areas. the signed distance functions may be decoded and/or post-processed to determine instance segmentation masks representing locations and classifications of intersection areas or regions. the locations of the intersections areas or regions may be generated in image-space and converted to world-space coordinates to aid an autonomous or semi-autonomous vehicle in navigating intersections according to rules of the road, traffic priority considerations, and/or the like.