Cloud Server Architecture for On-Demand Autonomy Vehicle Coordination
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Solution Overview
Problem
Current autonomous vehicle systems lack efficient methods for adaptive scheduling and real-time management of On-Demand Autonomy (ODA) services, particularly in coordinating leader and follower vehicles to fulfill service requests effectively, which can lead to inefficiencies and disruptions due to vehicle health issues or changing conditions during trips.
Innovation Solution
A cloud-based server architecture that includes a scheduler to receive trip requests, assess vehicle health parameters, and dynamically adjust trip plans by identifying suitable leader vehicles, monitoring health parameters in real-time, and offering options for modifying or terminating trips to ensure service continuity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a cloud-based server architecture with scheduler is implemented to coordinate leader and follower vehicles, then service efficiency and trip completion reliability are improved, but system complexity increases
Solution Approach 1:
The patent introduces a cloud-based server with a scheduler as an intermediary component that coordinates communication and resource allocation between leader vehicles and follower vehicles. The scheduler receives service requests, identifies suitable leader vehicles, establishes virtual links, and monitors trip progress, thereby improving trip completion reliability while centralizing control logic to manage system complexity.
2Reliability
If real-time monitoring of vehicle health parameters is implemented, then service continuity is improved, but data processing requirements and system complexity increase
Solution Approach 1:
The patent implements real-time monitoring of vehicle health parameters (battery level, sensor functionality, communication status) with continuous feedback to the cloud-based server. The scheduler uses this feedback information to assess whether leader vehicles can complete assigned trips and to make dynamic reassignment decisions, ensuring service continuity while processing data systematically through established monitoring protocols.
3Adaptability or versatility
If dynamic reassignment of leader vehicles is implemented based on health parameters, then adaptability is improved, but scheduling complexity increases
Solution Approach 1:
The patent implements dynamic reassignment of leader vehicles based on real-time health parameter monitoring. The scheduler continuously assesses leader vehicle status and can reassign trips when vehicles become unavailable due to battery depletion, sensor failures, or communication issues. This dynamic adaptation improves service versatility while the centralized scheduler manages the complexity of real-time decision-making.
4Adaptability or versatility
If virtual links between leader and follower vehicles are established through cloud coordination, then service flexibility is improved, but communication overhead increases
Solution Approach 1:
The patent establishes virtual links between leader and follower vehicles through cloud-based coordination. The server acts as an intermediary that manages the formation, maintenance, and termination of these virtual links. While this approach provides service flexibility by dynamically pairing vehicles based on trip requirements and vehicle availability, it generates communication overhead through continuous status reporting, request broadcasting, and coordination messages between all components.
Data Source
AI summary
Systems and methods for a cloud-based server system configured for an On-Demand Autonomy (ODA) service are disclosed. The cloud-based server in communication with a first vehicle and a second vehicle to receive a trip request for the ODA service from the first vehicle, and seek an agreement to establish a virtual link with the first vehicle to the second vehicle by identifying one second vehicle by broadcasting the trip request to the group of second vehicles; identifying a second vehicle from submitted responses based on an application using a modeled clone with a set of functionalities to perform a matching operation between the first and second vehicles; and coordinating responses based on results of the matching operation to confirm an acceptance of the agreement; and creating a trip plan for the trip request for the ODA service based on a set of preferences received from each of the vehicles.