Cloud Navigation Routing for Energy-Limited Autonomous Vehicles
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Solution Overview
Problem
Autonomous vehicles face limitations in energy power, computational resources, and data coverage, which hinder real-time complex data processing and route optimization, relying solely on vehicle-based intelligence and limited data from nearby vehicles.
Innovation Solution
A cloud-based automated navigation system utilizing high-speed and low-latency wireless networks, RFID technology, and advanced algorithms to provide comprehensive real-time data processing and route optimization, generating optimal route segments and operational instructions for autonomous vehicles.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Power
If vehicle-based intelligence and onboard computing resources are used for navigation, then autonomous vehicles can process data locally, but the limited computational power and energy consumption restrict real-time complex data processing and route optimization capabilities
Solution Approach 1:
The patent introduces a cloud-based automated navigation system that acts as an intermediary between autonomous vehicles and traffic environment data. The cloud system receives location and traffic data from vehicles, performs complex route optimization calculations using powerful servers, and returns optimized route segments to vehicles. This mediator approach transfers the computational burden from energy-constrained onboard systems to unlimited cloud infrastructure, resolving the contradiction between processing power and energy consumption.
Solution Approach 2:
The patent shifts the navigation computation from the vehicle dimension (onboard systems) to the network dimension (cloud infrastructure). By moving the processing location from local embedded computers to remote data centers connected via wireless networks, the system accesses vastly superior computational resources without increasing vehicle energy consumption or hardware complexity.
2Loss of information
If traditional vehicle-carried navigation systems rely solely on nearby vehicles for data supply, then data can be obtained through vehicle-vehicle interaction, but the data coverage is insufficient leading to limited predictive capabilities and incomplete traffic condition information
Solution Approach 1:
The patent merges multiple data sources into a unified cloud-based navigation system. It combines location data from the autonomous vehicle, real-time traffic environment data from infrastructure sources, and position measurements from nearby vehicles. This consolidation of diverse data streams into a comprehensive cloud system eliminates the limitations of isolated vehicle-to-vehicle communication, providing complete traffic condition information and enhancing predictive capabilities through holistic data analysis.
3Productivity
If onboard vehicle computing resources are used, then navigation can be performed autonomously, but the limited memory and storage capacity hinder the ability to process large amounts of data in real-time
Solution Approach 1:
The cloud-based navigation system serves as an intermediary that handles large-scale data storage and processing. Instead of requiring vehicles to maintain extensive local memory and storage capacity, the system stores traffic environment data, historical route information, and real-time measurements in cloud databases, accessing only necessary processed results locally. This resolves the contradiction by externalizing storage requirements to the network dimension.
Data Source
AI summary
Automated navigation systems and methods are provided. An example method includes receiving a request for automated navigation service from an autonomous vehicle, the request including vehicle information and a destination of the autonomous vehicle. The method further includes receiving continuously real-time location and position measurements data from the autonomous vehicle, determining a virtual zone centered around the autonomous vehicle, identifying nearby vehicles proximate to the autonomous vehicles within the virtual zone, receiving continuously real-time location and position measurements data of the identified nearby vehicles, obtaining and updating real-time traffic environment data of the traffic environment, generating an optimal route segment for the autonomous vehicle in the virtual zone, and transmitting an instruction to the autonomous vehicle for guiding the autonomous vehicle to adjust operational parameters comply with the optimal route segment.