Cloud Data Traffic Approximation for Hidden Vehicle Detection
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Solution Overview
Problem
Current navigation systems for autonomous and semi-autonomous vehicles face challenges in determining lane boundaries and detecting hidden vehicles, which can pose hazardous conditions, especially when on-board sensors are obstructed or fail to detect approaching vehicles.
Innovation Solution
A process and system that utilize cloud data analysis, including GPS and cellular roaming data, to monitor and identify potential hazards along a vehicle's planned route, filter out irrelevant data, and generate alerts for the driver, with the option for autonomous braking, employing machine learning algorithms to estimate traffic intensity and detect hidden vehicles.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Difficulty of detecting and measuring
If cloud data analysis is used to detect hidden vehicles, then detection capability is improved, but device complexity increases
Solution Approach 1:
The patent uses cloud-based cellular network data as an intermediary to detect hidden vehicles. Instead of directly sensing hidden vehicles with complex onboard sensors, the system analyzes GPS and cellular tower triangulation data from mobile devices in other vehicles, which are processed through cloud infrastructure. This intermediary approach enables detection of vehicles that are occluded or beyond direct sensor range without requiring equally complex direct detection hardware.
2Measurement precision
If multiple data sources are integrated for traffic estimation, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent merges multiple data sources including GPS data, cellular tower roaming data, and on-board sensor data into a unified traffic estimation system. By combining these diverse data streams and processing them through cloud-based algorithms, the system achieves more accurate traffic flow estimation and hidden vehicle detection than any single source could provide alone, while distributing the processing complexity across multiple components.
3Reliability
If cloud data monitoring is implemented for hazard identification, then safety is improved, but loss of time in data processing increases
Solution Approach 1:
The system performs preliminary analysis of cloud data continuously to identify potential hazards before the host vehicle reaches problematic road sections. By monitoring GPS trajectories and cellular data in advance, the system can predict hidden vehicle positions and prepare alerts ahead of time, reducing the critical response time when the vehicle actually encounters the hazard.
Data Source
AI summary
A process for local traffic approximation through analysis of cloud data is provided. The process includes, within a computerized traffic flow estimation controller of a host vehicle, operating programming to monitor a planned navigational route of the host vehicle, identify along the planned navigational route a road section including cross-traffic, monitor cloud data related to a mobile cellular device, analyze the cloud data to identify traffic posing a hazardous condition to the host vehicle within the road section, and generate a vehicle alert to a driver of the host vehicle based upon the identified traffic.