Autonomous Vehicle Network Topology for Reliable Collaborative Learning
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Solution Overview
Problem
Current communication networks are inadequate for supporting communication environments involving mobile and static nodes, particularly failing to effectively communicate among and with autonomous vehicles.
Innovation Solution
A dynamically configurable network of autonomous vehicles that utilizes cloud-aided and collaborative machine learning to optimize operation and planning, incorporating a platform that provides always-on, robust, scalable, and secure connectivity through a combination of fixed and mobile access points, enabling efficient data collection and processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If current communication networks are used to support autonomous vehicles, then existing infrastructure can be maintained, but communication effectiveness and reliability among mobile and static nodes deteriorates
Solution Approach 1:
The patent implements a dynamically configurable network where autonomous vehicles can switch between mobile ad-hoc mode (when moving) and fixed infrastructure mode (when stationary). The system dynamically adjusts network topology, communication protocols, and resource allocation based on vehicle state, location, and environmental conditions, resolving the contradiction between maintaining communication reliability and adapting to varying operational scenarios.
Solution Approach 2:
The patent creates a universal communication framework that handles multiple communication scenarios (vehicle-to-vehicle, vehicle-to-infrastructure, vehicle-to-cloud) through a single integrated system. The network infrastructure serves dual purposes as both mobile ad-hoc network when vehicles are in motion and fixed network when parked, eliminating the need for separate communication systems and improving overall reliability across diverse operational modes.
2Productivity
If cloud-aided machine learning is implemented for data processing, then operational optimization improves, but data transmission time and energy consumption increases
Solution Approach 1:
The patent segments data processing into three hierarchical levels: edge processing at the vehicle level for immediate decisions, regional cloud processing for fleet coordination, and central cloud processing for long-term learning. This segmentation allows time-critical operations to be handled locally without cloud transmission delays, while non-critical data is processed centrally to optimize overall fleet productivity.
Solution Approach 2:
The system performs preliminary data filtering, preprocessing, and feature extraction at the vehicle edge before cloud transmission. By preparing data in advance and transmitting only essential processed information rather than raw sensor data, the patent reduces transmission time and bandwidth requirements while maintaining the ability to perform sophisticated cloud-aided machine learning for operational optimization.
3Measurement precision
If comprehensive data collection from autonomous vehicles is performed, then learning accuracy improves, but energy consumption and data management complexity increases
Solution Approach 1:
The patent implements selective data collection where each vehicle collects and transmits data based on its specific operational context, location, sensor capabilities, and identified learning needs. Rather than uniform comprehensive collection from all vehicles, the system tailors data gathering to local requirements, maintaining high learning accuracy through relevant data while minimizing unnecessary energy consumption from sensors and communication systems.
Solution Approach 2:
The system uses data synthesis and augmentation techniques where representative data samples are created to supplement actual collected data. Through simulated scenarios, synthetic data generation, and transfer learning from other vehicles, the patent achieves high learning accuracy without requiring every vehicle to collect and transmit exhaustive real-world data, thereby reducing overall energy consumption and data management burden.
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, including systems and methods for collaborative data learning among autonomous vehicles.