Nvidia corporation (20240160913). ALLOCATING RESPONSIBILITY FOR AUTONOMOUS AND SEMI-AUTONOMOUS MACHINE INTERACTIONS AND APPLICATIONS simplified abstract
Contents
- 1 ALLOCATING RESPONSIBILITY FOR AUTONOMOUS AND SEMI-AUTONOMOUS MACHINE INTERACTIONS AND APPLICATIONS
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 ALLOCATING RESPONSIBILITY FOR AUTONOMOUS AND SEMI-AUTONOMOUS MACHINE INTERACTIONS AND APPLICATIONS - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Potential Commercial Applications
- 1.9 Possible Prior Art
- 1.10 Original Abstract Submitted
ALLOCATING RESPONSIBILITY FOR AUTONOMOUS AND SEMI-AUTONOMOUS MACHINE INTERACTIONS AND APPLICATIONS
Organization Name
Inventor(s)
Ryan Cosner of Altadena CA (US)
Karen Yan Ming Leung of Los Altos CA (US)
Marco Pavone of Stanford CA (US)
ALLOCATING RESPONSIBILITY FOR AUTONOMOUS AND SEMI-AUTONOMOUS MACHINE INTERACTIONS AND APPLICATIONS - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240160913 titled 'ALLOCATING RESPONSIBILITY FOR AUTONOMOUS AND SEMI-AUTONOMOUS MACHINE INTERACTIONS AND APPLICATIONS
Simplified Explanation
The patent application describes systems and methods for training neural networks to generate outputs indicating estimated levels of responsibilities associated with interactions between vehicles or machines and other objects. These neural networks can be used by vehicles, such as autonomous or semi-autonomous vehicles, to determine appropriate controls when navigating based on the estimated level of responsibility.
- Neural networks trained to estimate levels of responsibilities in interactions between vehicles/machines and other objects
- Real-world data used to train neural networks, including scenes depicting actual interactions and parameters associated with the interactions
- Vehicles can use neural network outputs to determine controls for navigating based on estimated level of responsibility
Potential Applications
This technology can be applied in various industries and fields, including:
- Autonomous vehicles
- Robotics
- Industrial automation
Problems Solved
This technology addresses the following issues:
- Improving safety in interactions between vehicles/machines and other objects
- Enhancing decision-making capabilities of autonomous systems
Benefits
The benefits of this technology include:
- Increased efficiency in navigating complex environments
- Reduced risk of accidents and collisions
- Enhanced adaptability to changing scenarios
Potential Commercial Applications
This technology can be commercially applied in:
- Autonomous vehicle industry
- Robotics industry
- Transportation and logistics sector
Possible Prior Art
One possible prior art in this field is the use of machine learning algorithms to improve decision-making processes in autonomous systems.
What are the potential limitations of this technology?
The potential limitations of this technology include:
- Dependence on the quality and quantity of training data
- Challenges in accurately estimating levels of responsibility in complex interactions
How does this technology compare to existing solutions?
This technology offers a more sophisticated approach to determining responsibilities in interactions between vehicles/machines and other objects compared to traditional rule-based systems. By leveraging neural networks trained on real-world data, this technology can adapt to a wide range of scenarios and improve decision-making capabilities.
Original Abstract Submitted
in various examples, learning responsibility allocations for machine interactions is described herein. systems and methods are disclosed that train a neural network(s) to generate outputs indicating estimated levels of responsibilities associated with interactions between vehicles or machines and other objects (e.g., other vehicles, machines, pedestrians, animals, etc.). in some examples, the neural network(s) is trained using real-world data, such as data representing scenes depicting actual interactions between vehicles and objects and/or parameters (e.g., velocities, positions, directions, etc.) associated with the interactions. then, in practice, a vehicle (e.g., an autonomous vehicle, a semi-autonomous vehicle, etc.) may use the neural network(s) to generate an output indicating a proposed or estimated level of responsibility associated with an interaction between the vehicle and an object. the vehicle may then use the output to determine one or more controls for the vehicle to use when navigating.
