Waymo llc (20240262385). SPATIO-TEMPORAL POSE/OBJECT DATABASE simplified abstract
Contents
SPATIO-TEMPORAL POSE/OBJECT DATABASE
Organization Name
Inventor(s)
Brandyn Allen White of Mountain View CA (US)
Aleksei Timofeev of Campbell CA (US)
SPATIO-TEMPORAL POSE/OBJECT DATABASE - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240262385 titled 'SPATIO-TEMPORAL POSE/OBJECT DATABASE
The patent application describes methods, systems, and apparatus for selecting actions for an agent at a specific real-world location using historical data generated at the same location.
- Determining the current geolocation of an agent within an environment
- Obtaining historical data for geolocations in the vicinity of the agent's current geolocation from a database
- Generating an embedding of the obtained historical data
- Providing the embedding as input to a policy decision-making system that selects actions for the agent
Potential Applications: - Autonomous vehicle navigation - Smart city infrastructure management - Environmental monitoring and analysis
Problems Solved: - Efficient decision-making for agents based on historical data - Enhanced situational awareness for agents in real-world locations
Benefits: - Improved accuracy in selecting actions for agents - Increased efficiency in navigating real-world environments - Enhanced safety and performance of agents
Commercial Applications: Title: "Enhanced Decision-Making System for Real-World Agents" This technology could be utilized in autonomous vehicles, smart city systems, and environmental monitoring solutions, offering improved decision-making capabilities based on historical data.
Questions about the technology: 1. How does this technology improve decision-making for agents in real-world environments? 2. What are the key advantages of using historical data for selecting actions for agents?
Original Abstract Submitted
methods, systems, and apparatus, including computer programs encoded on computer storage media, for selecting actions for an agent at a specific real-world location using historical data generated at the same real-world location. one of the methods includes determining a current geolocation of an agent within an environment; obtaining historical data for geolocations in a vicinity of the current geolocation of the agent from a database that maintains historical data for a plurality of geolocations within the environment, the historical data for each geolocation comprising observations generated at least in part from sensor readings of the geolocation captured by vehicles navigating through the environment; generating an embedding of the obtained historical data; and providing the embedding as an input to a policy decision-making system that selects actions to be performed by the agent.
