Predictive Connection Selection for Autonomous Vehicle Networks
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Solution Overview
Problem
Current communication networks are inadequate for supporting complex arrays of both moving and static nodes, such as those found in the Internet of Moving Things and autonomous vehicle networks, as they fail to provide dynamic and predictive selection of communication pathways.
Innovation Solution
A communication network architecture that includes a platform capable of providing always-on, robust, scalable, and secure connectivity to both mobile and static nodes, utilizing a multi-network on-board unit (OBU) with adaptive communication protocols and self-healing capabilities to manage mobility and connectivity across various environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If current communication networks are used to support moving and static nodes, then basic connectivity is provided, but dynamic and predictive selection of communication pathways is not achieved
Solution Approach 1:
The system performs predictive connection selection by evaluating future connection quality based on current trajectory data and historical performance. The OBU proactively identifies and switches to optimal access points before current connections degrade, rather than reacting after disconnection occurs. This preliminary action ensures continuous connectivity while enabling adaptive pathway selection.
Solution Approach 2:
The connection management system dynamically adjusts communication pathways based on real-time conditions including vehicle trajectory, access point density, and network performance metrics. The system continuously monitors connection quality and adapts switching decisions to maintain optimal connectivity as the vehicle moves through different environments with varying network densities.
2Reliability
If frequent connection switching is performed in dense network environments, then optimal connectivity is maintained, but system complexity and power consumption increase
Solution Approach 1:
The system changes operational parameters including connection switching thresholds, prediction time horizons, and evaluation metrics based on network conditions. In dense network environments, the system adjusts switching thresholds to balance connection quality optimization against power consumption, using historical performance data to determine optimal parameter settings that reduce unnecessary switching while maintaining connectivity quality.
Solution Approach 2:
The system implements feedback mechanisms that monitor connection quality metrics, power consumption, and switching frequency. This feedback is used to continuously refine prediction algorithms and adjust switching decisions, learning from past performance to optimize the balance between maintaining optimal connectivity and minimizing energy expenditure from frequent switching operations.
3Reliability
If predictive algorithms are implemented for connection selection, then connection reliability is improved, but computational requirements and processing time increase
Solution Approach 1:
The system performs partial predictive analysis by evaluating only the most relevant factors for connection selection based on current context, such as trajectory direction, speed, and immediate network conditions. Rather than computing all possible connection pathways, the algorithm focuses on predicting and evaluating likely candidate access points, reducing computational overhead while maintaining reliable connection selection.
Solution Approach 2:
The system pre-computes trajectory predictions and potential access point evaluations based on current motion state and network map data. By performing these computations in advance using cached historical data and preprocessed network information, the system reduces real-time processing requirements while maintaining accurate predictive connection selection capability.
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 communication network architectures, systems, and methods for supporting a dynamically configurable communication network comprising a complex array of both static and moving communication nodes (e.g., the Internet of moving things).