Intel corporation (20240210185). ENTITY ALLOCATION FOR NAVIGATED ROUTES simplified abstract
Contents
ENTITY ALLOCATION FOR NAVIGATED ROUTES
Organization Name
Inventor(s)
Raghavendra Bhat of Bangalore (IN)
Pravin Chander Chandran of Fremont CA (US)
Sean Lawrence of Bangalore (IN)
ENTITY ALLOCATION FOR NAVIGATED ROUTES - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240210185 titled 'ENTITY ALLOCATION FOR NAVIGATED ROUTES
The patent application provides techniques to calculate a "complexity score" (CS) and qualifying allocation score to bridge the gap between a performance score and entity-based performance score.
- The CS is determined based on specific agent data, such as vehicle-based alerts or environment-based data, collected for navigation segments.
- The CS is combined with the performance score to create a qualifying allocation score, offering a context-sensitive view of performance.
- This view helps in allocating the most suitable entity (driver, vehicle, AMR, etc.) to subsequent routes with navigation segments.
Potential Applications: - Autonomous driving systems - Fleet management solutions - Logistics and supply chain optimization
Problems Solved: - Enhancing decision-making in allocating resources - Improving efficiency and performance in navigation tasks
Benefits: - Optimized resource allocation - Enhanced navigation performance - Increased operational efficiency
Commercial Applications: Title: "Enhanced Resource Allocation System for Autonomous Vehicles" This technology can be utilized in autonomous vehicle fleets, logistics companies, and transportation services to optimize resource allocation and improve overall performance.
Prior Art: Researchers can explore existing patents related to autonomous vehicle navigation systems, resource allocation algorithms, and performance optimization in fleet management.
Frequently Updated Research: Stay updated on advancements in autonomous vehicle technology, navigation systems, and resource allocation algorithms to enhance the efficiency of this innovation.
Questions about the technology: 1. How does the complexity score differ from traditional performance metrics? 2. What are the key factors considered in determining the qualifying allocation score?
Original Abstract Submitted
techniques are provided to calculate a “complexity score” (cs) and qualifying allocation score, which aims to address the gap between a performance score, which may represent a driver score of other suitable entity-based performance score. the cs may be calculated based upon the particular agent (e.g. a vehicle, an autonomous mobile robot (amr), etc.), such as via the use of vehicle-based alerts, other types of alerts, environment-based data, etc., which are collected for specific navigation segments. the cs is then combined with the performance score to provide a qualifying allocation score, which is a context-sensitive view of the performance score. this context-sensitive view of the performance score may then be utilized for a determination regarding how to allocate the most well-suited entity (a driver, vehicle, amr, etc.) to a subsequent route that includes the navigation segments.
