Vehicle Mesh Network Interface Selection via Motion State Metrics
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Solution Overview
Problem
Existing mesh networks between vehicles face inefficiencies due to transient connections caused by changing vehicle speeds and directions, which hinder effective edge computing and data sharing, as they often bias towards suboptimal Wi-Fi network interfaces.
Innovation Solution
A system that calculates radio metric scores for various network interfaces based on current motion states to select the most suitable interface, such as LTE or DSRC, for stable and efficient communication, using an abstraction layer that equally weights different network interfaces to avoid biases towards Wi-Fi.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If mesh networks establish connections based on proximity and signal strength, then network connectivity is achieved, but connection stability deteriorates as vehicles change speed and direction
Solution Approach 1:
The system dynamically adapts network interface selection based on real-time motion states of vehicles. The electronic control unit calculates radio metric scores as a function of current motion state, allowing the network configuration to change adaptively with vehicle movement, speed, and direction rather than remaining static
Solution Approach 2:
The system changes the parameter of network interface selection by calculating radio metric scores that incorporate motion state parameters. Instead of using fixed connection criteria, the system adjusts interface selection based on varying motion parameters, thereby maintaining connection stability despite vehicle movement
2Productivity
If the system biases towards Wi-Fi network interfaces, then implementation is simplified, but communication performance deteriorates in moving vehicle scenarios
Solution Approach 1:
The electronic control unit automatically performs network interface selection by calculating radio metric scores based on motion state without requiring manual configuration or complex external control systems. The system serves itself by making intelligent interface selections based on real-time conditions
Solution Approach 2:
The system uses feedback from motion state sensors and radio metric calculations to dynamically adjust network interface selection. The electronic control unit continuously monitors vehicle motion state and uses this feedback to select the most appropriate network interface, optimizing performance without manual intervention
3Measurement precision
If the system calculates radio metric scores based on motion state, then network interface selection accuracy is improved, but computational overhead increases
Solution Approach 1:
The system performs partial calculations by focusing on radio metric scores relevant to motion state rather than evaluating all possible network parameters. This selective approach achieves sufficient selection accuracy without the computational burden of comprehensive analysis
Data Source
AI summary
A mesh network system includes an electronic control unit. The electronic control unit is configured to calculate a plurality of radio metric scores for a plurality of network interfaces for a first vehicle of a plurality of vehicles of a mesh network as a function of radio metrics in a current motion state of the first vehicle. The radio metrics indicate performance of the plurality of network interfaces in the current motion state of the first vehicle. The electronic control unit is further configured to select a desired network interface from the plurality of network interfaces comprising a desired radio metric score indicative of a desired performance in the current motion state.