Nvidia corporation (20240096102). FREESPACE DETECTION USING MACHINE LEARNING FOR AUTONOMOUS SYSTEMS AND APPLICATIONS simplified abstract

FREESPACE DETECTION USING MACHINE LEARNING FOR AUTONOMOUS SYSTEMS AND APPLICATIONS

Organization Name

nvidia corporation

Inventor(s)

Alexander Popov of Kirkland WA (US)

David Nister of Bellevue WA (US)

Nikolai Smolyanskiy of Seattle WA (US)

PATRIK Gebhardt of Cupertino CA (US)

Ke Chen of Mountain View CA (US)

Ryan Oldja of Issaquah WA (US)

Hee Seok Lee of Bundang-gu (KR)

Shane Murray of San Jose CA (US)

Ruchi Bhargava of Redmond WA (US)

Tilman Wekel of San Jose CA (US)

Sangmin Oh of San Jose CA (US)

FREESPACE DETECTION USING MACHINE LEARNING FOR AUTONOMOUS SYSTEMS AND APPLICATIONS - A simplified explanation of the abstract

This abstract first appeared for US patent application 20240096102 titled 'FREESPACE DETECTION USING MACHINE LEARNING FOR AUTONOMOUS SYSTEMS AND APPLICATIONS

Simplified Explanation

The patent application relates to freespace detection using machine learning models.

• Data obtained from sensors is used to identify freespace within an operational environment.
• The data is annotated with freespace labels to generate freespace annotated data.
• The freespace annotated data corresponds to a viewable area in the operational environment.
• Machine learning models are trained using the freespace annotated data to detect freespace using sensor data.

Potential Applications

This technology could be applied in autonomous vehicles for detecting freespace on roads and in parking lots.

Problems Solved

This technology solves the problem of accurately detecting freespace in complex operational environments.

Benefits

The benefits of this technology include improved safety, efficiency, and accuracy in freespace detection tasks.

Potential Commercial Applications

A potential commercial application of this technology could be in the development of advanced driver assistance systems (ADAS) for vehicles.

Possible Prior Art

One possible prior art could be the use of computer vision algorithms for freespace detection in autonomous vehicles.

Unanswered Questions

How does the machine learning model handle complex operational environments with varying lighting conditions?

The patent abstract does not provide details on how the machine learning model adapts to different lighting conditions in operational environments.

What types of sensors are used in this technology and how do they contribute to freespace detection?

The patent abstract does not specify the types of sensors used or how they work together to detect freespace accurately.

Original Abstract Submitted

systems and methods are disclosed that relate to freespace detection using machine learning models. first data that may include object labels may be obtained from a first sensor and freespace may be identified using the first data and the object labels. the first data may be annotated to include freespace labels that correspond to freespace within an operational environment. freespace annotated data may be generated by combining the one or more freespace labels with second data obtained from a second sensor, with the freespace annotated data corresponding to a viewable area in the operational environment. the viewable area may be determined by tracing one or more rays from the second sensor within the field of view of the second sensor relative to the first data. the freespace annotated data may be input into a machine learning model to train the machine learning model to detect freespace using the second data.