Nvidia corporation (20240230339). INTERSECTION POSE DETECTION IN AUTONOMOUS MACHINE APPLICATIONS simplified abstract

INTERSECTION POSE DETECTION IN AUTONOMOUS MACHINE APPLICATIONS

Organization Name

nvidia corporation

Inventor(s)

Trung Pham of Santa Clara CA (US)

Hang Dou of Fremont CA (US)

Berta Rodriguez Hervas of San Francisco CA (US)

Minwoo Park of Saratoga CA (US)

Neda Cvijetic of East Palo Alto CA (US)

David Nister of Bellevue CA (US)

INTERSECTION POSE DETECTION IN AUTONOMOUS MACHINE APPLICATIONS - A simplified explanation of the abstract

This abstract first appeared for US patent application 20240230339 titled 'INTERSECTION POSE DETECTION IN AUTONOMOUS MACHINE APPLICATIONS

Simplified Explanation: The patent application discusses using live perception from sensors in a vehicle to generate potential paths for navigating an intersection in real-time or near real-time.

Key Features and Innovation:

• Utilizing a deep neural network to compute outputs such as heat maps, vector fields, intensity maps, and classifications related to an intersection.
• Decoding and post-processing the outputs to reconstruct the intersection and determine potential paths for the vehicle.
• Leveraging sensor data to enhance real-time decision-making for safe navigation through intersections.

Potential Applications: This technology can be applied in autonomous vehicles, traffic management systems, and smart city infrastructure to improve intersection navigation and safety.

Problems Solved: Addressing the challenge of efficiently navigating intersections by utilizing sensor data and advanced computational algorithms to generate optimal paths for vehicles.

Benefits:

• Enhanced safety and efficiency in intersection navigation.
• Real-time decision-making capabilities for vehicles.
• Potential reduction in traffic congestion and accidents at intersections.

Commercial Applications: The technology can be utilized by automotive companies, transportation authorities, and urban planners to enhance traffic flow, reduce accidents, and improve overall transportation systems.

Prior Art: Researchers can explore prior patents related to deep learning in autonomous vehicles, sensor fusion technologies, and intersection navigation systems to understand the existing landscape of similar innovations.

Frequently Updated Research: Stay updated on advancements in deep learning algorithms, sensor technologies, and intersection management systems to incorporate the latest developments into this technology.

Questions about Intersection Navigation Technology: 1. How does this technology improve intersection safety and efficiency? 2. What are the potential challenges in implementing this technology in real-world traffic scenarios?

Original Abstract Submitted

in various examples, live perception from sensors of a vehicle may be leveraged to generate potential paths for the vehicle to navigate an intersection in real-time or near real-time. for example, a deep neural network (dnn) may be trained to compute various outputs—such as heat maps corresponding to key points associated with the intersection, vector fields corresponding to directionality, heading, and offsets with respect to lanes, intensity maps corresponding to widths of lanes, and/or classifications corresponding to line segments of the intersection. the outputs may be decoded and/or otherwise post-processed to reconstruct an intersection—or key points corresponding thereto—and to determine proposed or potential paths for navigating the vehicle through the intersection.