Nvidia corporation (20240127062). BEHAVIOR-GUIDED PATH PLANNING IN AUTONOMOUS MACHINE APPLICATIONS simplified abstract
Contents
- 1 BEHAVIOR-GUIDED PATH PLANNING IN AUTONOMOUS MACHINE APPLICATIONS
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 BEHAVIOR-GUIDED PATH PLANNING IN AUTONOMOUS MACHINE 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
BEHAVIOR-GUIDED PATH PLANNING IN AUTONOMOUS MACHINE APPLICATIONS
Organization Name
Inventor(s)
Mariusz Bojarski of Brooklyn NY (US)
Chenyi Chen of Fremont CA (US)
Bernhard Firner of Highland Park NJ (US)
BEHAVIOR-GUIDED PATH PLANNING IN AUTONOMOUS MACHINE APPLICATIONS - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240127062 titled 'BEHAVIOR-GUIDED PATH PLANNING IN AUTONOMOUS MACHINE APPLICATIONS
Simplified Explanation
The abstract describes a patent application related to training a machine learning model, such as a deep neural network, to generate trajectory points, vehicle orientation, and vehicle state using image and sensor data as inputs.
- Machine learning model trained to use image and sensor data to generate trajectory points, vehicle orientation, and vehicle state
- Sensor data collected to automatically generate trajectory for training the model
- Trajectory points, vehicle orientation, and vehicle state used by a control component for vehicle control in a physical environment
Potential Applications
This technology could be applied in autonomous vehicles, robotics, and navigation systems for precise control and trajectory planning.
Problems Solved
This technology solves the problem of accurately predicting and controlling vehicle trajectory and orientation in real-time using sensor data and machine learning algorithms.
Benefits
The benefits of this technology include improved accuracy in vehicle control, enhanced safety measures, and optimized navigation through complex environments.
Potential Commercial Applications
The potential commercial applications of this technology include autonomous driving systems, drone navigation, and robotic control systems.
Possible Prior Art
One possible prior art for this technology could be the use of machine learning models for trajectory planning and control in autonomous vehicles and robotics.
What are the limitations of using sensor data for trajectory generation in this technology?
The limitations of using sensor data for trajectory generation in this technology include potential inaccuracies in sensor readings, environmental factors affecting sensor performance, and the need for continuous calibration and maintenance of sensors.
How does this technology compare to traditional control systems in terms of accuracy and efficiency?
This technology offers improved accuracy and efficiency compared to traditional control systems by utilizing machine learning algorithms to process complex sensor data and generate precise trajectory points and vehicle orientation.
Original Abstract Submitted
in various examples, a machine learning model—such as a deep neural network (dnn)—may be trained to use image data and/or other sensor data as inputs to generate two-dimensional or three-dimensional trajectory points in world space, a vehicle orientation, and/or a vehicle state. for example, sensor data that represents orientation, steering information, and/or speed of a vehicle may be collected and used to automatically generate a trajectory for use as ground truth data for training the dnn. once deployed, the trajectory points, the vehicle orientation, and/or the vehicle state may be used by a control component (e.g., a vehicle controller) for controlling the vehicle through a physical environment. for example, the control component may use these outputs of the dnn to determine a control profile (e.g., steering, decelerating, and/or accelerating) specific to the vehicle for controlling the vehicle through the physical environment.
