18158990. SENSOR DATA MAPPING FOR PERSPECTIVE VIEW AND TOP VIEW SENSORS FOR MACHINE LEARNING APPLICATIONS simplified abstract (QUALCOMM Incorporated)
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 18158990 titled 'SENSOR DATA MAPPING FOR PERSPECTIVE VIEW AND TOP VIEW SENSORS FOR MACHINE LEARNING APPLICATIONS
Simplified Explanation
This patent application describes a system for vehicle driving assistance using image processing technology.
- The method involves receiving data from various sensors on a vehicle to create a three-dimensional map of the surroundings.
- The sensors include perspective view and top view sensors, and the data is used to generate a detailed representation of the area.
- Machine learning is utilized to analyze and identify characteristics of the environment around the vehicle based on the three-dimensional map.
Key Features and Innovation
- Integration of multiple sensors for comprehensive data collection.
- Creation of a detailed three-dimensional representation of the vehicle's surroundings.
- Utilization of machine learning for analyzing and identifying environmental characteristics.
Potential Applications
This technology can be applied in:
- Advanced driver assistance systems.
- Autonomous vehicles.
- Traffic monitoring and management systems.
Problems Solved
- Enhances the accuracy and efficiency of vehicle driving assistance systems.
- Improves safety by providing a detailed understanding of the surroundings.
- Enables better decision-making for drivers and autonomous vehicles.
Benefits
- Increased safety on the roads.
- Enhanced driving experience.
- Improved efficiency in traffic management.
Commercial Applications
- This technology can be utilized by automotive manufacturers to enhance their vehicles' safety features.
- It can also be integrated into transportation systems for better traffic control and management.
Prior Art
Readers interested in prior art related to this technology can explore research papers and patents in the field of image processing for vehicle assistance systems.
Frequently Updated Research
Stay updated on advancements in image processing technology for vehicle driving assistance systems to ensure the latest innovations are incorporated into this technology.
Questions about Vehicle Driving Assistance Systems
How does this technology improve road safety?
This technology enhances road safety by providing a detailed three-dimensional representation of the vehicle's surroundings, enabling better decision-making for drivers and autonomous vehicles.
What are the potential commercial applications of this technology?
The commercial applications of this technology include integration into vehicles for advanced driver assistance systems and traffic management solutions.
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.
