18065114. EXTERNAL DATA COLLECTION BASED ON PREDICTED DATA INSUFFICIENCY simplified abstract (International Business Machines Corporation)
Contents
- 1 EXTERNAL DATA COLLECTION BASED ON PREDICTED DATA INSUFFICIENCY
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 EXTERNAL DATA COLLECTION BASED ON PREDICTED DATA INSUFFICIENCY - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Key Features and Innovation
- 1.6 Potential Applications
- 1.7 Problems Solved
- 1.8 Benefits
- 1.9 Commercial Applications
- 1.10 Prior Art
- 1.11 Frequently Updated Research
- 1.12 Questions about Autonomous Vehicle Data Insufficiency
- 1.13 Original Abstract Submitted
EXTERNAL DATA COLLECTION BASED ON PREDICTED DATA INSUFFICIENCY
Organization Name
International Business Machines Corporation
Inventor(s)
Tiberiu Suto of Franklin NY (US)
Martin G. Keen of Cary NC (US)
Jeremy R. Fox of Georgetown TX (US)
Sarbajit K. Rakshit of Kolkata (IN)
EXTERNAL DATA COLLECTION BASED ON PREDICTED DATA INSUFFICIENCY - A simplified explanation of the abstract
This abstract first appeared for US patent application 18065114 titled 'EXTERNAL DATA COLLECTION BASED ON PREDICTED DATA INSUFFICIENCY
Simplified Explanation
The patent application describes a method for predicting and mitigating data insufficiency in autonomous vehicles by collecting external data from additional sources.
- The processor directs actions for the autonomous vehicle based on external data.
- Contextual data about the vehicle's environment is collected to predict data insufficiency.
- Additional external data is collected from a second data source to address the predicted insufficiency.
Key Features and Innovation
- Prediction and mitigation of data insufficiency in autonomous vehicles.
- Collection of external data from additional sources to enhance decision-making.
- Utilization of contextual data to anticipate data insufficiency.
Potential Applications
This technology can be applied in autonomous vehicles, robotics, and other AI-driven systems that rely on external data for decision-making.
Problems Solved
- Addressing data insufficiency in autonomous vehicles.
- Enhancing the accuracy and reliability of autonomous vehicle actions.
- Improving the overall performance and safety of autonomous systems.
Benefits
- Increased efficiency in autonomous vehicle operations.
- Enhanced decision-making capabilities in dynamic environments.
- Improved safety and reliability of autonomous systems.
Commercial Applications
The technology can be utilized in autonomous vehicle industries, logistics, transportation, and other sectors requiring advanced autonomous systems for operations.
Prior Art
Prior research in the field of autonomous vehicles and data prediction may provide insights into similar technologies and approaches.
Frequently Updated Research
Stay updated on advancements in autonomous vehicle technology, data prediction algorithms, and external data collection methods to enhance the performance of autonomous systems.
Questions about Autonomous Vehicle Data Insufficiency
How does the technology predict data insufficiency in autonomous vehicles?
The technology predicts data insufficiency by collecting contextual data about the vehicle's environment and analyzing it to anticipate potential gaps in external data.
What are the benefits of mitigating data insufficiency in autonomous vehicles?
Mitigating data insufficiency improves the accuracy and reliability of autonomous vehicle actions, leading to enhanced safety, efficiency, and performance.
Original Abstract Submitted
Provided is a computer-implemented method, system, and computer program product for predicting data insufficiency related to performing autonomous vehicle actions and dynamically mitigating the data insufficiency by collecting external data from additional data sources. A processor may direct, for an autonomous vehicle, an action in response to external data. The processor may collect contextual data about an environment in which the autonomous vehicle is operating. The processor may predict, based in part on the collected contextual data, an insufficiency of external data for performing the action. In response to the predicting, the processor may direct collection of additional external data from a second data source.
