17953894. SYSTEMS AND METHODS FOR TRAINING A SCENE SIMULATOR USING REAL AND SIMULATED AGENT DATA simplified abstract (TOYOTA JIDOSHA KABUSHIKI KAISHA)
Contents
- 1 SYSTEMS AND METHODS FOR TRAINING A SCENE SIMULATOR USING REAL AND SIMULATED AGENT DATA
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 SYSTEMS AND METHODS FOR TRAINING A SCENE SIMULATOR USING REAL AND SIMULATED AGENT DATA - 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
SYSTEMS AND METHODS FOR TRAINING A SCENE SIMULATOR USING REAL AND SIMULATED AGENT DATA
Organization Name
TOYOTA JIDOSHA KABUSHIKI KAISHA
Inventor(s)
Tsun-Hsuan Wang of Cambridge MA (US)
Alexander Amini of Brookline MA (US)
Wilko Schwarting of Boston MA (US)
Igor Gilitschenski of Cambridge MA (US)
Sertac Karaman of Cambridge MA (US)
SYSTEMS AND METHODS FOR TRAINING A SCENE SIMULATOR USING REAL AND SIMULATED AGENT DATA - A simplified explanation of the abstract
This abstract first appeared for US patent application 17953894 titled 'SYSTEMS AND METHODS FOR TRAINING A SCENE SIMULATOR USING REAL AND SIMULATED AGENT DATA
Simplified Explanation
The patent application describes a method for training a scene simulator to render 2D scenes using data from real and simulated agents. Here is a simplified explanation of the abstract:
- Acquiring trajectories and 3D views for multiple agents from observations of real vehicles.
- Generating a 3D scene with the multiple agents using the acquired 3D views and information from simulated agents.
- Training a scene simulator to render scene projections using the generated 3D scene.
- Outputting a 2D scene with simulated observations for a driving scene using the trained scene simulator.
Potential Applications
This technology could be applied in the development of autonomous driving systems, virtual reality simulations, and training programs for vehicle operators.
Problems Solved
This technology helps in creating realistic 2D scenes for training and testing purposes, combining data from real and simulated agents to improve accuracy and realism.
Benefits
The benefits of this technology include enhanced training simulations, improved safety in autonomous driving systems, and more efficient testing of vehicle behaviors in various scenarios.
Potential Commercial Applications
Potential commercial applications of this technology include software development for autonomous vehicles, virtual reality entertainment, and training programs for emergency responders.
Possible Prior Art
One possible prior art for this technology could be the use of 3D simulation software in the automotive industry for testing and training purposes.
Unanswered Questions
How does this technology handle complex traffic scenarios with multiple agents interacting?
The technology is designed to handle multiple agents in a scene, but the specific algorithms and methods for simulating interactions between agents are not detailed in the abstract.
What kind of computational resources are required to train and run the scene simulator efficiently?
The abstract does not provide information on the computational resources needed for training and running the scene simulator, which could be crucial for practical implementation.
Original Abstract Submitted
System, methods, and other embodiments described herein relate to training a scene simulator for rendering 2D scenes using data from real and simulated agents. In one embodiment, a method includes acquiring trajectories and three-dimensional (3D) views for multiple agents from observations of real vehicles. The method also includes generating a 3D scene having the multiple agents using the 3D views and information from simulated agents. The method also includes training a scene simulator to render scene projections using the 3D scene. The method also includes outputting a 2D scene having simulated observations for a driving scene using the scene simulator.
