Waymo llc (20240378514). RESOURCE ALLOCATION FOR AN AUTONOMOUS VEHICLE TRANSPORTATION SERVICE simplified abstract
Contents
RESOURCE ALLOCATION FOR AN AUTONOMOUS VEHICLE TRANSPORTATION SERVICE
Organization Name
Inventor(s)
Ganesh Balachandran of Mountain View CA (US)
Salil Pandit of Palo Alto CA (US)
RESOURCE ALLOCATION FOR AN AUTONOMOUS VEHICLE TRANSPORTATION SERVICE - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240378514 titled 'RESOURCE ALLOCATION FOR AN AUTONOMOUS VEHICLE TRANSPORTATION SERVICE
The abstract of the patent application discusses the creation of a model to determine the maximum number of concurrent trips for an autonomous vehicle transportation service. This model utilizes historical trip data, response times for assistance requests, and available resources to calculate the optimal number of trips that can be handled simultaneously.
- The model is trained using historical trip data and the numbers of concurrent trips.
- It considers factors such as response time requirements, available resources, and time periods to provide a maximum number of concurrent trips.
- By analyzing overlapping trips and response times, the model can efficiently allocate resources and optimize the transportation service.
Potential Applications: - Autonomous vehicle fleet management systems - On-demand transportation services - Traffic management and optimization solutions
Problems Solved: - Efficient allocation of resources for concurrent trips - Optimization of response times and service quality - Improved scalability and reliability of autonomous vehicle transportation services
Benefits: - Increased efficiency and cost-effectiveness - Enhanced customer satisfaction and service reliability - Scalability and adaptability to varying demand levels
Commercial Applications: Title: "Optimizing Autonomous Vehicle Fleet Management for Maximum Efficiency" This technology can be utilized by transportation companies, ride-sharing services, and logistics firms to streamline operations, improve service quality, and maximize resource utilization.
Questions about the technology: 1. How does the model account for varying levels of demand and available resources? The model considers historical data on trip requests, response times, and resource availability to dynamically adjust the maximum number of concurrent trips. 2. What are the potential implications of this technology for the future of autonomous vehicle transportation services? This technology could revolutionize the efficiency and scalability of autonomous vehicle fleets, leading to improved service quality and customer satisfaction.
Original Abstract Submitted
aspects of the disclosure relate to generating a model to assess maximum numbers of concurrent trips for an autonomous vehicle transportation service. for instance, historical trip data, including when requests for assistance were made, response times for those requests for assistance, and a number of available resources when each of the requests for assistance were made may be received. in addition, a number of concurrent trips, or trips that overlap in time, occurring when each of the requests for assistance were made may be received. the model may be trained using the historical trip data and the numbers of concurrent trips. the model may be configured to provide a maximum number of concurrent trips given a period of time, a number of available resources, and a response time requirement.
