Qualcomm incorporated (20240249527). SENSOR DATA MAPPING FOR PERSPECTIVE VIEW AND TOP VIEW SENSORS FOR MACHINE LEARNING APPLICATIONS simplified abstract
Contents
- 1 SENSOR DATA MAPPING FOR PERSPECTIVE VIEW AND TOP VIEW SENSORS FOR MACHINE LEARNING APPLICATIONS
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 SENSOR DATA MAPPING FOR PERSPECTIVE VIEW AND TOP VIEW SENSORS FOR MACHINE LEARNING APPLICATIONS - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Key Features and Innovation
- 1.6 Potential Applications
- 1.7 Problems Solved
- 1.8 Benefits
- 1.9 Commercial Applications
- 1.10 Prior Art
- 1.11 Frequently Updated Research
- 1.12 Questions about Vehicle Driving Assistance Systems
- 1.13 Original Abstract Submitted
SENSOR DATA MAPPING FOR PERSPECTIVE VIEW AND TOP VIEW SENSORS FOR MACHINE LEARNING APPLICATIONS
Organization Name
Inventor(s)
Balaji Shankar Balachandran of San Diego CA (US)
Varun Ravi Kumar of San Diego CA (US)
Senthil Kumar Yogamani of Headford (IE)
SENSOR DATA MAPPING FOR PERSPECTIVE VIEW AND TOP VIEW SENSORS FOR MACHINE LEARNING APPLICATIONS - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240249527 titled 'SENSOR DATA MAPPING FOR PERSPECTIVE VIEW AND TOP VIEW SENSORS FOR MACHINE LEARNING APPLICATIONS
Simplified Explanation
The patent application describes a method for vehicle driving assistance systems that use image processing to create a three-dimensional representation of the area surrounding a vehicle. This is achieved by combining sensor data from different sensors on the vehicle and mapping it onto a three-dimensional surface.
- Utilizes multiple sensors on a vehicle to create a three-dimensional representation of the surrounding area.
- Combines data from perspective view sensors and top view sensors to map onto a three-dimensional surface.
- Machine learning model is used to determine characteristics of the surrounding area based on the three-dimensional representation.
Key Features and Innovation
- Integration of sensor data from different perspectives to create a comprehensive three-dimensional representation.
- Utilization of machine learning models to analyze and understand the characteristics of the surrounding area.
- Enhances vehicle driving assistance systems by providing a detailed view of the environment around the vehicle.
Potential Applications
The technology can be applied in:
- Autonomous driving systems
- Parking assistance systems
- Collision avoidance systems
Problems Solved
- Improves the accuracy and reliability of vehicle driving assistance systems.
- Enhances the safety of driving by providing a detailed view of the surrounding environment.
Benefits
- Increased safety on the road.
- Improved efficiency in driving assistance systems.
- Enhanced user experience for drivers.
Commercial Applications
Title: Advanced Vehicle Driving Assistance Systems This technology can be utilized in the automotive industry for:
- Autonomous vehicles
- Fleet management systems
- Automotive safety features
Prior Art
Readers can explore prior art related to image processing in vehicle driving assistance systems, sensor fusion technologies, and machine learning models in the field of autonomous vehicles.
Frequently Updated Research
Stay updated on the latest advancements in image processing technologies, sensor fusion techniques, and machine learning models in the automotive industry.
Questions about Vehicle Driving Assistance Systems
How does the integration of sensor data improve the accuracy of vehicle driving assistance systems?
Integrating sensor data from multiple perspectives allows for a more comprehensive understanding of the surrounding environment, leading to improved accuracy in driving assistance systems.
What are the potential challenges in implementing machine learning models for analyzing the characteristics of the surrounding area?
One potential challenge could be the need for large amounts of training data to effectively train the machine learning models for accurate analysis.
Original Abstract Submitted
this disclosure provides systems, methods, and devices for vehicle driving assistance systems that support image processing. in a first aspect, a method is provided that includes receiving sensor data from a plurality of sensors on a vehicle and determining a three-dimensional representation of an area surrounding the vehicle by mapping the sensor data onto a three-dimensional surface. the plurality of sensors may include at least one perspective view sensor and at least one top view sensor, and the three-dimensional surface may include sensor data from the at least one perspective view sensor and sensor data from the at least one top view sensor. the method may further include determining, with a machine learning model, one or more characteristics of the area surrounding the vehicle based on the three-dimensional representation. other aspects and features are also claimed and described.
