Qualcomm incorporated (20240200969). MACHINE LEARNING BASED OCCUPANCY GRID GENERATION simplified abstract

MACHINE LEARNING BASED OCCUPANCY GRID GENERATION

Organization Name

qualcomm incorporated

Inventor(s)

Volodimir Slobodyanyuk of San Diego CA (US)

Radhika Dilip Gowaikar of San Diego CA (US)

Makesh Pravin John Wilson of San Diego CA (US)

Shantanu Chaisson Sanyal of San Diego CA (US)

Avdhut Joshi of San Marcos CA (US)

Christopher Brunner of San Diego CA (US)

Behnaz Rezaei of San Diego CA (US)

Amin Ansari of Federal Way WA (US)

MACHINE LEARNING BASED OCCUPANCY GRID GENERATION - A simplified explanation of the abstract

This abstract first appeared for US patent application 20240200969 titled 'MACHINE LEARNING BASED OCCUPANCY GRID GENERATION

The abstract describes a device that processes sensor data associated with a vehicle and a set of frames to generate an aggregated frame with cells labeled with occupancy information. The device then uses this data to train a machine learning model to create an occupancy grid, focusing on specific cells with a loss function.

• Device aggregates sensor data to generate an aggregated frame associated with cells
• Obtains occupancy labels for each cell, including first and second occupancy labels
• Trains a machine learning model using the aggregated frame data to create an occupancy grid
• Focuses on specific cells with a loss function for training the model
• Emphasizes the use of machine learning to generate an occupancy grid efficiently

Potential Applications: - Autonomous driving systems - Traffic management - Parking space optimization

Problems Solved: - Efficient processing of sensor data for occupancy detection - Creation of accurate occupancy grids for various applications

Benefits: - Improved accuracy in occupancy detection - Enhanced efficiency in generating occupancy grids - Better utilization of resources in vehicle-related applications

Commercial Applications: Title: "Advanced Occupancy Detection System for Vehicle Applications" This technology can be utilized in autonomous vehicles, smart city infrastructure, and parking management systems to enhance efficiency and accuracy in occupancy detection and management.

Prior Art: Prior research in machine learning models for occupancy detection in vehicles and smart city applications could provide valuable insights into similar technologies.

Frequently Updated Research: Stay updated on advancements in machine learning algorithms for occupancy detection and optimization in vehicle-related applications.

Questions about Advanced Occupancy Detection System: 1. How does the device differentiate between first and second occupancy labels in the cells? 2. What are the potential challenges in training the machine learning model with the aggregated frame data?

Original Abstract Submitted

in some aspects, a device may receive sensor data associated with a vehicle and a set of frames. the device may aggregate, using a first pose, the sensor data associated with the set of frames to generate an aggregated frame, wherein the aggregated frame is associated with a set of cells. the device may obtain an indication of a respective occupancy label for each cell from the set of cells, wherein the respective occupancy label includes a first occupancy label or a second occupancy label, and wherein a subset of cells from the set of cells are associated with the first occupancy label. the device may train, using data associated with the aggregated frame, a machine learning model to generate an occupancy grid, based on a loss function that only calculates a loss for respective cells from the subset of cells. numerous other aspects are described.