Waymo llc (20240199084). ASSESSING SURPRISE FOR AUTONOMOUS VEHICLES simplified abstract
Contents
ASSESSING SURPRISE FOR AUTONOMOUS VEHICLES
Organization Name
Inventor(s)
Isaac John Supeene of Sunnyvale CA (US)
Azadeh Dinparastdjadid of Sunnyvale CA (US)
Johan Engstrom of Los Gatos CA (US)
ASSESSING SURPRISE FOR AUTONOMOUS VEHICLES - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240199084 titled 'ASSESSING SURPRISE FOR AUTONOMOUS VEHICLES
The patent application describes methods, systems, and apparatus for computing enhanced surprise metrics for autonomously driven vehicles. One method involves receiving data representing a predicted state of an agent at a particular time, data representing an actual state of the agent for the same time, and computing a surprise metric for the actual state based on the residual information between the predicted and actual states.
- Predicted state data and actual state data are used to calculate surprise metrics for autonomously driven vehicles.
- The surprise metric is computed based on the residual information between the predicted and actual states of the agent.
- Computer programs encoded on computer storage media are utilized to perform the computations.
- The technology aims to enhance the ability of autonomous vehicles to predict and react to unexpected situations.
- By measuring surprise metrics, the system can improve the overall performance and safety of autonomously driven vehicles.
Potential Applications: - Autonomous driving systems - Traffic management and control - Fleet management and logistics - Predictive maintenance in transportation industry
Problems Solved: - Enhancing the predictive capabilities of autonomous vehicles - Improving safety and efficiency in transportation systems - Addressing the challenges of unexpected events on the road
Benefits: - Increased safety for passengers and pedestrians - Enhanced efficiency in transportation operations - Reduced risks of accidents and traffic congestion
Commercial Applications: Title: "Enhanced Surprise Metrics for Autonomous Vehicles: Commercial Applications and Market Implications" This technology can be applied in the development of advanced autonomous driving systems for commercial vehicles, leading to improved safety, efficiency, and cost-effectiveness in transportation operations. The market implications include potential partnerships with automotive manufacturers, fleet operators, and technology companies to integrate the innovation into their products and services.
Prior Art: Readers interested in exploring prior art related to surprise metrics for autonomous vehicles can start by researching relevant patents, academic papers, and industry publications in the fields of artificial intelligence, machine learning, and autonomous driving technologies.
Frequently Updated Research: Researchers and developers in the field of autonomous vehicles are continuously exploring new methods and technologies to enhance the predictive capabilities and safety of autonomous driving systems. Stay updated on the latest advancements in surprise metrics and predictive modeling for autonomous vehicles to leverage cutting-edge innovations in the industry.
Questions about Enhanced Surprise Metrics for Autonomous Vehicles:
1. How do surprise metrics contribute to the overall performance of autonomous vehicles? Surprise metrics help autonomous vehicles anticipate and react to unexpected events on the road, enhancing their ability to navigate safely and efficiently in various driving conditions.
2. What are the potential implications of integrating surprise metrics into existing autonomous driving systems? Integrating surprise metrics can lead to improved safety, efficiency, and reliability in autonomous vehicles, ultimately enhancing the overall user experience and reducing the risks of accidents on the road.
Original Abstract Submitted
methods, systems, and apparatus, including computer programs encoded on computer storage media, for computing enhanced surprise metrics for autonomously driven vehicles. one of the methods includes receiving data representing a predicted state of an agent at a particular time, data representing an actual state of the agent for the particular time, and computing a surprise metric for the actual state of the agent based on a measure of the residual information between the predicted state of the agent and the actual state of the agent.
