Autonomous Vehicle Service Slot Assignment via Centralized Scheduling
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Solution Overview
Problem
As the number of autonomous and semi-autonomous vehicles is expected to increase, there is a need for efficient systems to manage their service scheduling, including re-fueling, maintenance, and repairs, to avoid congestion at service stations and optimize service delivery.
Innovation Solution
A computer-aided autonomous driving (CA/AD) system that communicates with a remote service scheduling server to request and assign service slots, allowing vehicles to autonomously navigate to selected service providers based on user parameters and availability data, using machine learning for optimized service provider selection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the number of autonomous vehicles increases, then the usage and traffic of autonomous vehicles increases, but the congestion at service stations increases
Solution Approach 1:
The system performs preliminary scheduling and routing of autonomous vehicles to service providers before they arrive. The server determines optimal service providers and communicates assignments to vehicles in advance, allowing vehicles to be distributed across multiple service providers proactively rather than queuing at a single location, thereby preventing congestion.
2Productivity
If autonomous vehicles are scheduled for service without optimization, then service delivery is basic, but the efficiency and distribution of service utilization is poor
Solution Approach 1:
The system establishes bidirectional communication between the server, service providers, and autonomous vehicles. The server receives availability data from service providers and assignment confirmation from vehicles, then determines optimal assignments based on real-time feedback. This feedback loop enables dynamic optimization of service routing and reduces waiting times through efficient resource allocation.
3Productivity
If a centralized scheduling system is implemented, then service provider assignment is optimized, but the system complexity increases
Solution Approach 1:
The server performs multiple functions within a single centralized system: receiving availability data from service providers, determining optimal service provider assignments based on various criteria, communicating assignments to vehicles, and receiving confirmation. This multi-functional approach consolidates complexity into a single coordinating entity rather than requiring complex interactions between multiple distributed systems.
Data Source
AI summary
Embodiments include apparatuses, systems, and methods for a computer-aided or autonomous driving (CA/AD) system to transmit to a remote service scheduling server, a request message for a service slot at a service provider. In embodiments, the remote service scheduling server may assign the service slot for a semi-autonomous or autonomous driving (SA/AD) vehicle including the CA/AD system. In embodiments, the service scheduling server may perform analysis or machine learning on data received from the CA/AD system as well as from a plurality of service stations in order to assign the service provider and/or service slot to the SA/AD vehicle. In embodiments, the CA/AD system may receive an assignment message and control driving elements of the SA/AD vehicle to autonomously or semi-autonomously drive the SA/AD vehicle to the service provider to receive the service at the assigned service slot. Other embodiments may also be described and claimed.