18736741. SYSTEMS AND METHODS FOR UTILIZING MACHINE LEARNING FOR VEHICLE DETECTION OF ADVERSE CONDITIONS simplified abstract (Verizon Patent and Licensing Inc.)
Contents
SYSTEMS AND METHODS FOR UTILIZING MACHINE LEARNING FOR VEHICLE DETECTION OF ADVERSE CONDITIONS
Organization Name
Verizon Patent and Licensing Inc.
Inventor(s)
Tommaso Bianconcini of Florence (IT)
Leonardo Sarti of Florence (IT)
Leonardo Taccari of Florence (IT)
Francesco Sambo of Florence (IT)
Enrico Civitelli of Arezzo (IT)
Simone Magistri of Florence (IT)
SYSTEMS AND METHODS FOR UTILIZING MACHINE LEARNING FOR VEHICLE DETECTION OF ADVERSE CONDITIONS - A simplified explanation of the abstract
This abstract first appeared for US patent application 18736741 titled 'SYSTEMS AND METHODS FOR UTILIZING MACHINE LEARNING FOR VEHICLE DETECTION OF ADVERSE CONDITIONS
Simplified Explanation: The patent application describes a device that can analyze driving conditions from an image of a road scene using a model with multiple processing layers.
Key Features and Innovation:
- Device determines driving conditions from image features using a multi-layered model.
- Each processing layer computes a specific driving condition in parallel.
- Sequential linear layers in the model help in determining driving conditions accurately.
Potential Applications: This technology can be used in autonomous vehicles, driver assistance systems, and road safety applications.
Problems Solved: The technology helps in accurately analyzing driving conditions from images, enhancing road safety and improving driving assistance systems.
Benefits:
- Improved accuracy in determining driving conditions.
- Enhanced road safety and driver assistance systems.
- Potential for reducing accidents and improving overall driving experience.
Commercial Applications: This technology has significant commercial potential in the automotive industry for autonomous vehicles, advanced driver assistance systems, and road safety applications.
Prior Art: Readers can explore prior research on image processing, machine learning models for driving analysis, and neural networks for similar applications.
Frequently Updated Research: Stay updated on advancements in image processing, machine learning models, and neural networks for driving analysis to understand the latest developments in the field.
Questions about driving condition analysis technology: 1. How does the device determine driving conditions accurately from images? 2. What are the potential real-world applications of this technology in the automotive industry?
Original Abstract Submitted
In some implementations, a device may determine a plurality of driving conditions associated with an image of a road scene based on providing a set of features associated with the image to a plurality of processing layers of a model. Each processing layer, of the plurality of processing layers, may determine, in parallel, a respective driving condition of the plurality of driving conditions and may comprise a plurality of sequential linear layers including a first sequential, linear layer comprising a first quantity of neurons corresponding to a quantity of features included in the set of features and computing resources of the device and a last sequential, linear layer comprising a second quantity of neurons that is based on a task associated with determining the respective driving condition. The device may perform one or more actions based on the plurality of driving conditions.
