Nvidia corporation (20240092390). VIRTUAL AGENT TRAJECTORY PREDICTION AND TRAFFIC MODELING FOR MACHINE SIMULATION SYSTEMS AND APPLICATIONS simplified abstract
Contents
- 1 VIRTUAL AGENT TRAJECTORY PREDICTION AND TRAFFIC MODELING FOR MACHINE SIMULATION SYSTEMS AND APPLICATIONS
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 VIRTUAL AGENT TRAJECTORY PREDICTION AND TRAFFIC MODELING FOR MACHINE SIMULATION SYSTEMS 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
VIRTUAL AGENT TRAJECTORY PREDICTION AND TRAFFIC MODELING FOR MACHINE SIMULATION SYSTEMS AND APPLICATIONS
Organization Name
Inventor(s)
Jeevan Devaranjan of Toronto (CA)
Xue Bin Peng of Vancouver (CA)
VIRTUAL AGENT TRAJECTORY PREDICTION AND TRAFFIC MODELING FOR MACHINE SIMULATION SYSTEMS AND APPLICATIONS - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240092390 titled 'VIRTUAL AGENT TRAJECTORY PREDICTION AND TRAFFIC MODELING FOR MACHINE SIMULATION SYSTEMS AND APPLICATIONS
Simplified Explanation
The patent application describes systems and methods for model-based trajectory simulation of agents in a simulated environment, specifically focusing on traffic simulators for autonomous or semi-autonomous vehicle design validation.
- Generation of multiple navigation probability distributions for defining candidate trajectories for model-controlled agents in the simulation environment.
- Selection of a trajectory for the agent based on the generated navigation probability distributions.
- Movement of the agent within the simulation environment based on the selected trajectory.
- Application of a search algorithm across multiple time-steps to identify collision-free sequences of navigation probability distributions.
Potential Applications
The technology can be applied in the development and testing of autonomous vehicle driving models, traffic management systems, and urban planning simulations.
Problems Solved
1. Validation of driving models in diverse and complex environments. 2. Efficient trajectory planning for model-controlled agents in simulation environments.
Benefits
1. Improved accuracy and realism in agent trajectory simulation. 2. Enhanced safety and performance testing for autonomous vehicles. 3. Optimization of traffic flow and navigation strategies in simulated environments.
Potential Commercial Applications
Optimized traffic flow management systems for smart cities. SEO Optimized Title: "Commercial Applications of Model-Based Trajectory Simulation Technology"
Possible Prior Art
One possible prior art could be the use of Monte Carlo simulation techniques in trajectory planning for autonomous vehicles.
What are the limitations of the search algorithm used in the patent application?
The patent application does not specify the specific type of search algorithm used or its computational efficiency in identifying collision-free sequences of navigation probability distributions.
How does the technology in the patent application compare to existing traffic simulation software?
The patent application does not provide a direct comparison to existing traffic simulation software or highlight any unique features that set it apart from current solutions.
Original Abstract Submitted
in various examples, systems and methods are presented for model-based trajectory simulation of agents in a simulated environment. traffic simulators mimic reality so that autonomous or semi-autonomous vehicle design teams can validate driving models in environments that have diversity and complexity. in some embodiments, for a model-controlled agent of a simulation environment, a plurality of navigation probability distributions are generated, each of the plurality of navigation probability distributions defining a candidate trajectory for the agent to follow. a trajectory is selected for the agent based at least on at least one of the plurality of navigation probability distributions, and the agent is moved within the simulation environment based at least on the selected trajectory. in some embodiments, a search algorithm may be applied across multiple time-steps of a simulation, for example, to identify the occurrence of collision-free sequences of navigation probability distributions.
