Cloud Collision Prediction for Unequipped Vehicles
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Solution Overview
Problem
Existing systems for predicting vehicle collisions struggle with vehicles lacking transponder-based systems, small radar signatures, and incompatible communication systems, leading to reduced monitoring capacity and increased collision risks.
Innovation Solution
A cloud computing environment aggregates vehicle position data to generate collision predictions based on trusted vehicle locations and approximate environment object locations, using machine learning models and diverse data sources to improve accuracy and intercommunication between vehicles and systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If radar systems and transponder-based systems are used for vehicle traffic control, then collision prediction accuracy is improved for equipped vehicles, but monitoring capacity is reduced for vehicles without such systems
Solution Approach 1:
The patent introduces a cloud-based intermediary system that receives position data from transponder-equipped vehicles and generates collision predictions that are then broadcast to all vehicles in the area. This mediator enables vehicles without transponders to benefit from collision prediction capabilities by receiving broadcast data from the cloud system, thus resolving the contradiction between measurement precision for equipped vehicles and monitoring capacity for all vehicles.
Solution Approach 2:
The system creates a universal collision prediction service that serves both transponder-equipped and unequipped vehicles through a single cloud-based platform. By broadcasting position data and collision predictions to all vehicles regardless of equipment status, the system achieves multi-functionality that simultaneously improves collision prediction accuracy for equipped vehicles and extends monitoring capacity to include unequipped vehicles.
2Reliability
If transponder-based systems are installed on all vehicles, then collision detection capability is improved, but device complexity and cost increase
Solution Approach 1:
The patent merges the collision detection function into a centralized cloud-based system rather than requiring individual transponders in each vehicle. By combining the computational resources and data processing capabilities into a single external system, the patent improves overall collision detection capability while reducing the complexity burden on individual vehicles, as only cloud infrastructure and broadcast receivers are needed.
3Measurement precision
If smaller vehicles are monitored using traditional radar systems, then radar detectability is reduced due to smaller radar signatures, but collision prediction is still needed
Solution Approach 1:
The patent replaces the mechanical radar detection system with a cloud-based electronic data aggregation and analysis system. Instead of relying on radar signatures that diminish with vehicle size, the system uses electronic position data from transponders and broadcasts collision predictions to all vehicles including smaller ones. This substitution eliminates the radar signature limitation and maintains collision prediction reliability for vehicles of all sizes.
Data Source
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AI summary
Embodiments of the disclosure provide for predicting collisions between vehicles and environment objects based at least in part on user input from a computing entity. Some embodiments receive, at a cloud computing environment, a user input from a computing entity, where the user input comprises an approximate location of an environment object. Some embodiments generate, using a machine learning model, a collision prediction involving the environment object and a vehicle based at least in part on the approximate location of the environment object and a trusted location of the vehicle. Some embodiments generate traffic data based at least in part on the collision prediction. Some embodiments provide a notification indicative of the traffic data to the vehicle and the computing entity.