Waymo llc (20240308551). ASSESSING SURPRISE FOR AUTONOMOUS VEHICLES simplified abstract
Contents
ASSESSING SURPRISE FOR AUTONOMOUS VEHICLES
Organization Name
Inventor(s)
Azadeh Dinparastdjadid of Sunnyvale CA (US)
Johan Engstrom of Los Gatos CA (US)
Haoyu Chen of Santa Clara CA (US)
Isaac Supeene of Sunnyvale CA (US)
Menghui Wang of Santa Clara CA (US)
ASSESSING SURPRISE FOR AUTONOMOUS VEHICLES - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240308551 titled 'ASSESSING SURPRISE FOR AUTONOMOUS VEHICLES
Simplified Explanation: The patent application describes methods, systems, and apparatus for computing a backward-looking surprise metric for autonomously driven vehicles. This involves predicting states of an agent along trajectories, obtaining subsequent data, and computing a surprise score based on the difference between predicted and actual states.
- **Predicting States:** The technology involves predicting future states of an agent along trajectories.
- **Obtaining Data:** Data representing actual states of the agent at subsequent time steps is collected.
- **Computing Surprise Score:** A surprise metric is calculated based on the variance between predicted and actual states.
- **Autonomously Driven Vehicles:** The innovation is specifically designed for self-driving vehicles.
- **Backward-Looking Metric:** The technology focuses on analyzing past predictions to improve future performance.
Potential Applications: The technology can be applied in the development of advanced autonomous driving systems, predictive maintenance in industrial settings, and real-time anomaly detection in various industries.
Problems Solved: This technology addresses the need for accurate prediction models in autonomous systems, enhances safety in self-driving vehicles, and improves overall system performance through surprise metrics.
Benefits: The benefits of this technology include increased accuracy in predicting agent states, improved decision-making in autonomous systems, and enhanced safety and efficiency in various applications.
Commercial Applications: Title: Advanced Predictive Analytics for Autonomous Vehicles This technology can be commercialized in the automotive industry for the development of next-generation self-driving cars, in the manufacturing sector for predictive maintenance solutions, and in the logistics industry for real-time anomaly detection systems.
Prior Art: While there is no specific prior art mentioned in the abstract, researchers can explore existing literature on surprise metrics in autonomous systems, predictive analytics in vehicle technology, and anomaly detection algorithms.
Frequently Updated Research: Researchers in the field of autonomous systems and predictive analytics are constantly updating methodologies for improving surprise metrics, prediction accuracy, and anomaly detection in various applications.
Questions about Autonomous Driving Technology: 1. How does this technology contribute to the advancement of autonomous driving systems? 2. What are the potential challenges in implementing surprise metrics in self-driving vehicles?
Ensure the article is comprehensive, informative, and optimized for SEO with appropriate keyword usage and interlinking. Use varied sentence structures and natural language to avoid AI detection. Make the content engaging and evergreen by focusing on the lasting impact and relevance of the technology.
Original Abstract Submitted
methods, systems, and apparatus, including computer programs encoded on computer storage media, for computing a backward looking surprise metric for autonomously driven vehicles. one of the methods includes obtaining first data representing one or more previously predicted states of an agent along one or more predicted trajectories of the agent at a first time step. second data representing one or more states of the agent at a subsequent time step is obtained. a surprise score is computed from a measure of a difference between the first data computed for the one or more predicted trajectories for the prior time step and the second data computed for the one or more predicted states for the subsequent time step.
