Vehicular Micro Cloud Road Hazard Prediction
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Solution Overview
Problem
Existing vehicle control systems struggle to predict and prevent future road hazards in real-time, relying on limited onboard sensors and lacking coordination with other vehicles in the vicinity.
Innovation Solution
A vehicular micro cloud system that utilizes digital data from connected vehicles to identify potential road hazards, predict their occurrence, and execute remedial actions through coordinated vehicle control systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a vehicle control system uses only onboard sensors to detect road hazards, then the system complexity is low, but the detection precision and prediction capability are insufficient
Solution Approach 1:
The patent merges sensor data from multiple vehicles in a micro cloud to create a collective environmental model. By combining observations from various vehicles, the system achieves superior detection precision without requiring each individual vehicle to have complex sensor arrays, thus resolving the contradiction between precision and complexity.
Solution Approach 2:
The vehicular micro cloud system serves multiple functions: hazard detection, environmental modeling, prediction, and coordinated response. This multi-functionality allows the system to achieve high detection precision through data fusion while maintaining reasonable individual vehicle complexity, as the computational burden is distributed across the micro cloud.
2Reliability
If a vehicle control system operates independently without coordination with other vehicles, then the device complexity is low, but the ability to predict future road hazards is limited
Solution Approach 1:
The patent introduces a vehicular micro cloud as an intermediary layer between individual vehicles and the hazard prediction function. This mediator coordinates data exchange and computation among vehicles, enabling reliable future hazard prediction while managing coordination complexity through a structured communication framework rather than direct peer-to-peer complexity.
Solution Approach 2:
The system performs preliminary actions by continuously building and updating an environmental model using current sensor data from micro cloud members. This ongoing preliminary modeling enables the system to predict future road hazards before they materialize, improving reliability while the complexity is managed through incremental updates rather than complex real-time coordination.
3Measurement precision
If real-time sensor data from multiple vehicles is collected and processed, then the road hazard prediction accuracy is improved, but the data processing time and computational load increase
Solution Approach 1:
The patent segments the data processing task by assigning different roles to micro cloud members. Some vehicles collect and transmit sensor data, others perform specific analysis functions, and the system collectively builds the environmental model. This segmentation enables high prediction accuracy through comprehensive data processing while reducing individual processing time and computational load on any single vehicle.
Data Source
AI summary
The disclosure includes embodiments for predicting an occurrence of a future road hazard in a roadway environment using digital data provided by a vehicular micro cloud. A method according to some embodiments is executed by a processor. In some embodiments, the processor is an element of a vehicle that is itself a member of the vehicular micro cloud. The method includes receiving vehicular micro cloud data describing sensor measurements of a roadway environment and a rule set. The method includes predicting, based on the sensor measurements and the rule set, that a future road hazard will occur. The method includes executing a remedial action plan that obviates the future road hazard. In some embodiments, the remedial action plan is executed by one or more members of the vehicular micro cloud. In some embodiments, digital data describing the prediction and the remedial action plan is broadcast within the roadway environment.