Multi-Sensor Event Verification for Accurate Vehicle Incident Detection
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Solution Overview
Problem
Existing methods for verifying events using sensor data often result in inaccuracies, leading to false determinations and inefficient resource allocation, as they lack effective mechanisms for confirming event occurrences and selecting the most accurate sensor data.
Innovation Solution
A computing platform that receives and compares source data from multiple sensors using machine learning algorithms, determines event occurrence based on thresholds, and updates algorithms for improved accuracy, while establishing wireless connections to confirm events and dispatch notifications efficiently.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If single sensor data is used to determine event occurrence, then the system is simple and fast, but the accuracy of event determination deteriorates leading to false positives
Solution Approach 1:
The patent combines data from multiple sensor devices to verify event occurrence. The system receives first source data from a first sensor device and second source data from a second sensor device, then compares both data sources to determine if an event occurred. This merging of multiple sensor inputs resolves the contradiction by improving determination accuracy through cross-verification while managing complexity through systematic data comparison protocols.
Solution Approach 2:
The computing platform acts as an intermediary that receives, compares, and verifies data from multiple sensor devices. It mediates between the raw sensor data and the final event determination, using machine learning algorithms to assess whether the event comparison output exceeds the predetermined threshold. This intermediary layer enables accurate multi-sensor verification without requiring direct complex interactions between sensor devices.
2Reliability
If multiple sensor data sources are compared for event verification, then the accuracy of event determination is improved, but the processing time and system complexity increase
Solution Approach 1:
The system performs preliminary actions by establishing predetermined comparison thresholds and machine learning models before event verification is needed. The computing platform is pre-configured with the logic to compare first source data and second source data against established criteria. When an event is detected, the system can quickly determine if the event comparison output exceeds the predetermined threshold without requiring complex real-time analysis, thus reducing processing time while maintaining high reliability.
Solution Approach 2:
The system dynamically adjusts parameters such as the predetermined comparison threshold and selects different machine learning algorithms based on the specific event type and sensor data characteristics. By changing these parameters adaptively, the system optimizes the balance between verification reliability and processing speed, allowing faster verification when confidence is high and more thorough analysis when uncertainty exists.
3Adaptability or versatility
If machine learning algorithms are used for event determination, then the accuracy and adaptability of event detection is improved, but the computational complexity and resource requirements increase
Solution Approach 1:
The computing platform implements universal machine learning algorithms that can handle multiple types of events and sensor data sources through a single integrated system. The same algorithmic framework processes different event types (collisions, harsh braking, rolling events) by comparing first source data and second source data against learned patterns. This multi-functional approach improves adaptability across various event scenarios while avoiding the need for separate complex systems for each event type.
Solution Approach 2:
The machine learning algorithms operate autonomously to determine event occurrence by self-evaluating whether the event comparison output exceeds the predetermined threshold. The system automatically updates its determinations based on the comparison of multiple sensor data sources without requiring manual intervention or complex external processing. This self-service capability enables adaptive event detection while managing computational complexity through automated decision-making protocols.
Data Source
AI summary
Aspects of the disclosure relate to computing platforms that utilize improved techniques for dynamic event verification. A computing platform may receive first source data comprising driving data associated with a vehicle over a time period. Based on the first source data, the computing device may determine that the vehicle experienced an event, resulting in an event output. In response to determining the event output, the computing device may generate a request for second source data associated with the vehicle over the time period. The computing device may receive, from a sensor device, the second source data. Based on a comparison of the first source data to the second source data, the computing platform may determine an event comparison output. The computing platform may determine that the event comparison output exceeds a predetermined comparison threshold, and may send an indication of an event in response.