Vehicle Image Alert Matching for Nearby Hazard Detection
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Solution Overview
Problem
Existing technologies lack the capability to effectively monitor and react to nearby moving vehicles, particularly in regards to gathering information about drivers and passengers, and identifying potentially hazardous driving behaviors.
Innovation Solution
A vehicle system equipped with image sensors and processors that acquire and cross-reference image data with alert data, such as missing person alerts, to identify matches and notify the driver or authorities, while also monitoring and assessing the driving behaviors of nearby vehicles.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If image sensors and processors are added to monitor nearby vehicles, then safety awareness and threat detection capability are improved, but device complexity and energy consumption increase
Solution Approach 1:
The image sensors and processing system are designed to perform multiple functions: monitoring nearby vehicles, detecting missing persons, identifying hazardous driving behaviors, and providing notifications. This multi-functionality justifies the added complexity by delivering comprehensive safety monitoring without requiring separate dedicated systems for each function.
2Measurement precision
If continuous image data acquisition is performed to monitor driving behaviors, then detection accuracy and response time are improved, but energy consumption and data processing load increase
Solution Approach 1:
The system acquires image data continuously but processes and analyzes it at specific intervals or when triggering events occur (such as detecting unusual driving patterns or when a vehicle matches alert criteria). This periodic processing approach maintains detection accuracy while reducing overall energy consumption compared to constant full-analysis processing.
Solution Approach 2:
The system performs preliminary filtering and pre-processing of image data to identify potential matches or anomalies before conducting full analysis. This preliminary action reduces the data processing load for subsequent detailed examination, thereby lowering energy consumption while maintaining detection precision.
3Speed
If alert data cross-referencing is performed in real-time, then response time to safety threats is improved, but computational load and processing time increase
Solution Approach 1:
Alert data from missing person reports and hazardous driver databases are pre-loaded into the system's memory or cache before needed. This preliminary action allows the system to perform rapid cross-referencing with currently captured image data without requiring time-consuming queries to external databases, thus reducing processing time while maintaining real-time response capability.
Solution Approach 2:
The cross-referencing process is divided into segments: initial quick comparison of key identifiers (such as license plate numbers or vehicle descriptions), followed by more detailed matching only for potential candidates. This segmented approach reduces overall processing time while maintaining accurate threat identification.
Data Source
AI summary
A method may include receiving, via at least one processor, alert data that may include an alert associated with a vehicle identifier of an additional vehicle and location data. The method may then include cross referencing the alert data with image data to identify a match between the alert data and the image data, such that the image data is acquired over a period of time by one or more image sensors coupled to a vehicle. The method may also include presenting a notification via an electronic display of the vehicle in response to identifying a match between the alert data and the image data.