Vehicle Ad Hoc Classification Sharing for Low-Bandwidth Detection
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Solution Overview
Problem
Current mapping systems and autonomous vehicle technologies face challenges due to limited wireless network coverage and bandwidth, as well as increased computing requirements for processing vast amounts of sensor data, leading to inefficiencies and potential safety issues.
Innovation Solution
The implementation of ad hoc communication and data sharing networks, such as vehicle-to-vehicle (V2V) networks, which allow vehicles to share data and resources wirelessly, reducing the reliance on central cloud networks and optimizing processing resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If vehicles rely on central cloud networks for data processing and communication, then comprehensive data processing capability is achieved, but network congestion and bandwidth limitations occur
Solution Approach 1:
The patent segments the centralized cloud processing architecture into distributed edge computing nodes deployed in vehicles. Each vehicle becomes an independent processing unit capable of local data analysis, reducing the need for continuous cloud communication and thereby decreasing network traffic volume while maintaining processing capability.
Solution Approach 2:
The patent introduces ad hoc communication networks as intermediaries between vehicles and the cloud. These mesh networks enable vehicles to exchange data directly with nearby vehicles, acting as intermediaries that reduce traffic on central cloud networks while still providing comprehensive data processing through collaborative computation.
2Measurement precision
If advanced sensor processing is performed in each autonomous vehicle, then detection precision is improved, but computing resource requirements increase
Solution Approach 1:
The patent merges computing resources across multiple vehicles through ad hoc networks. Instead of each vehicle requiring full independent processing capability, vehicles combine their computational power to achieve high detection precision collectively, reducing individual vehicle computing complexity while maintaining overall system accuracy.
Solution Approach 2:
The patent enables vehicles to perform self-service computing by processing critical data locally and only transmitting essential information to the network. This reduces the burden on individual vehicle computing systems while maintaining detection precision through selective local processing.
3Area of stationary object
If ad hoc networks are used for vehicle communication, then network coverage is extended, but system complexity increases
Solution Approach 1:
The patent makes vehicles universal communication nodes that can simultaneously act as sensors, processors, and relay stations. Each vehicle's communication system serves multiple functions: local data exchange, network routing, and cooperative sensing, extending coverage without proportionally increasing system complexity through multi-functional integration.
Data Source
AI summary
Systems and methods for sharing intermediate classifications in a dynamic ad hoc network are disclosed including determining, using a machine learning model executed by a vehicle control circuit and information from one or more sensors of the vehicle, one or more intermediate classifications and respective confidence indications for the one or more determined intermediate classifications, determining a first identification with a first confidence indication using at least one of the one or more determined intermediate classifications, receiving one or more intermediate classifications from the dynamic ad hoc network, and determining a composite identification as a function of the one or more received intermediate classifications from the dynamic ad hoc network and at least one of the one or more determined intermediate classifications by the vehicle control circuit or the determined first identification by the vehicle control circuit.