Vehicle Event Validation Using Multi-Source AI Detection
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Solution Overview
Problem
Vehicle sub-systems struggle with false positive and negative event detections due to their limited capability to handle all driving scenarios, necessitating a more robust method to validate events using multiple data sources.
Innovation Solution
An apparatus with a processor and interface that combines and validates driving events from disparate data sources using a trained artificial intelligence model, operating independently of the sub-systems to provide a binary confirmation of event occurrence.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If vehicle sub-systems use single data source for event detection, then device complexity is reduced, but measurement precision deteriorates leading to false positives and negatives
Solution Approach 1:
The patent combines multiple data sources (sensors, cameras, radar) into a unified event detection system. The processor integrates data from disparate sources including accelerometers, gyroscopes, cameras, and radar systems to validate driving events, thereby improving detection accuracy while managing system complexity through centralized processing.
Solution Approach 2:
The processor serves multiple functions: it processes data from various sensor types (accelerometers, gyroscopes, cameras, radar), performs event detection, validates events through cross-referencing multiple data sources, and generates validation results. This multi-functional approach allows a single component to handle diverse detection tasks without proportionally increasing system complexity.
2Device complexity
If vehicle sub-systems operate independently with limited parameters, then device complexity is reduced, but reliability deteriorates due to inability to handle all driving scenarios
Solution Approach 1:
The processor acts as an intermediary between multiple independent data sources and the event validation process. It receives data from various sensors, performs cross-validation by comparing events from different sources, and produces a consolidated validation result, thereby improving reliability without requiring complete system integration.
Solution Approach 2:
The system implements a nested structure where the validation layer is embedded within the event detection process. The processor nests multiple data validation operations within a unified processing framework, allowing independent data sources to be processed through a hierarchical validation structure that enhances reliability while maintaining modular architecture.
3Measurement precision
If multiple data sources are combined for event validation, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent segments the event validation process into distinct operational stages: data collection from multiple sources, event detection by individual sensors, cross-validation by the processor, and result generation. This segmentation allows complex multi-source validation to be managed through modular, sequential processing steps, reducing overall system complexity while maintaining high detection accuracy.
Data Source
AI summary
An apparatus comprising an interface and a processor. The interface may be configured to receive a first event from a first source and a second event from a second source. The processor may be configured to request the second event in response to the first event and run a trained artificial intelligence model configured to aggregate at least the first event and the second event as input, perform operations on the input to evaluate the first event and generate a validation result for the first event in response to the input. The validation result may comprise a binary output. The trained artificial intelligence model may operate independently from the first source and the second source to enable the generation of the validation result to be performed separately from a detection of the first event. The validation result may provide an independent confirmation of an occurrence of the first event.