18615894. INTERSECTION POSE DETECTION IN AUTONOMOUS MACHINE APPLICATIONS simplified abstract (NVIDIA Corporation)

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 18615894 titled 'INTERSECTION POSE DETECTION IN AUTONOMOUS MACHINE APPLICATIONS

Simplified Explanation: The patent application discusses using live sensor data from a vehicle to generate potential paths for navigating intersections 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 intersection navigation.

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

Problems Solved: This technology addresses the challenge of efficiently navigating intersections by leveraging live sensor data and advanced computational techniques.

Benefits:

• Enhanced real-time decision-making for vehicle navigation.
• Improved intersection safety and efficiency.
• Potential for reducing traffic congestion and accidents at intersections.

Commercial Applications: Optimizing autonomous vehicle navigation systems for urban environments, enhancing traffic flow in smart cities, and improving overall transportation efficiency.

Prior Art: Readers can explore prior research on deep learning in autonomous vehicles, sensor fusion for navigation systems, and intersection management algorithms.

Frequently Updated Research: Stay informed on the latest advancements in deep learning for autonomous vehicles, real-time sensor data processing, and intersection navigation algorithms.

Questions about Intersection Navigation Technology: 1. How does this technology improve intersection navigation compared to traditional methods? 2. What are the potential challenges in implementing this technology in real-world traffic scenarios?

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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.