Autonomous Vehicle Mesh Network with Dynamic Access Point Roles
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Solution Overview
Problem
Current communication networks are inadequate for supporting communication environments involving mobile and static nodes, particularly in networks of autonomous vehicles, as they fail to provide reliable and efficient connectivity and data management.
Innovation Solution
A dynamically configurable network architecture that incorporates Mobile Access Points (MAPs) and Fixed Access Points (FAPs) using long-range communication protocols like 802.11p, enabling vehicles to act as Wi-Fi hotspots and forming a mesh network with the wired infrastructure, along with a Cloud-based system for data management and decision-making.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If current communication networks are used to support autonomous vehicles, then existing infrastructure can be utilized, but reliable and efficient connectivity cannot be achieved
Solution Approach 1:
The system dynamically configures network topology by allowing vehicles to switch between acting as Mobile Access Points (MAPs) and Fixed Access Points (FAPs) based on their motion state. When vehicles are stationary, they function as FAPs to provide stable wired connectivity; when moving, they become MAPs providing wireless mesh network connectivity. This dynamic role assignment resolves the contradiction by adapting network behavior to vehicle state while maintaining reliable connectivity throughout.
Solution Approach 2:
Vehicles serve multiple functions within the network infrastructure - they can be MAPs providing wireless access, FAPs providing wired access, or both simultaneously. This multi-functionality allows the same vehicle resource to adapt to different connectivity requirements, achieving both reliability through role specialization and versatility through role flexibility.
2Reliability
If a mesh network with MAPs and FAPs is implemented, then connectivity reliability improves, but system complexity increases
Solution Approach 1:
The system implements self-service through automatic role assignment where vehicles autonomously determine whether to function as MAPs or FAPs based on their motion detection. The network self-configures routing paths and connectivity relationships without external intervention. This automation reduces operational complexity while maintaining the reliable mesh network architecture.
Solution Approach 2:
The network architecture dynamically adjusts its configuration based on real-time vehicle states. When vehicles transition between moving and stationary, the network automatically reconfigures topology and routing. This dynamic adaptation simplifies management compared to static complex networks, as the system automatically optimizes itself rather than requiring manual configuration.
3Adaptability or versatility
If vehicles act as Wi-Fi hotspots forming mesh networks, then network coverage and adaptability improve, but energy consumption increases
Solution Approach 1:
Vehicles alternate between providing network services (as MAP or FAP) and conserving energy by disconnecting, based on their motion state and network requirements. When stationary and not requiring mobility, vehicles can provide stable FAP service with lower energy consumption. When moving, they provide MAP service only when necessary for network coverage, otherwise conserving energy. This periodic activation pattern reduces overall energy consumption while maintaining adequate network coverage.
Solution Approach 2:
The system provides network services locally based on specific needs rather than universally. Vehicles that are stationary provide FAP services to local wired infrastructure, while moving vehicles provide MAP services only in their immediate vicinity. This localized service provision reduces total energy consumption compared to all vehicles continuously providing full-network coverage, while still achieving adequate overall network coverage through distributed local contributions.
4Productivity
If dynamic role assignment between MAP and FAP is implemented, then network efficiency improves, but control complexity increases
Solution Approach 1:
Vehicles autonomously determine their optimal role (MAP or FAP) based on their own motion state and network requirements, without requiring complex centralized control. Each vehicle independently monitors its own state and makes role assignment decisions, simplifying the control mechanism while achieving efficient network configuration through distributed self-determination.
Data Source
AI summary
Communication network architectures, systems and methods for supporting a network of mobile nodes. As a non-limiting example, various aspects of this disclosure provide autonomous vehicle network architectures, systems, and methods for supporting a dynamically configurable network of autonomous vehicles comprising a complex array of both static and moving communication nodes.