Context-Aware Driving Event Alerts for Unsafe Behavior Detection
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Solution Overview
Problem
Current vehicle monitoring systems lack context awareness, leading to ineffective detection and reporting of unsafe driving behaviors, which can result in accidents, increased insurance costs, and inefficient driving practices.
Innovation Solution
A computer-implemented method and system that detects combination driving events by identifying two distinct driving events occurring within a predetermined time interval, modifying reports to include indications of these events, and generating alerts or alerts based on environmental context to improve safety and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If driving events are reported individually without context, then the system is simple to operate, but the effectiveness of safety monitoring is reduced due to lack of context awareness
Solution Approach 1:
The patent combines multiple driving events into a single composite event for reporting. When multiple driving events occur within a predetermined time interval, the system merges them into one reportable event, providing contextual information about the sequence and relationship between events. This approach improves safety monitoring effectiveness by presenting a complete picture of driving behavior while avoiding the complexity of analyzing every individual event separately.
Solution Approach 2:
The system performs preliminary analysis of driving events to determine their temporal relationships and contextual significance before generating reports. By pre-establishing time intervals and event classifications, the system can quickly identify combination events without complex real-time processing, thus improving reliability while maintaining operational simplicity.
2Loss of information
If all driving events are reported separately, then the quantity of information is high, but the loss of time increases due to unnecessary alerts
Solution Approach 1:
The system merges multiple driving events that occur within a predetermined time interval into a single composite report. This consolidation reduces the total number of separate alerts while preserving the contextual information about event sequences. Safety reviewers receive fewer, more meaningful alerts that highlight dangerous combinations of events rather than isolated incidents, thereby reducing review time while maintaining data quality.
Solution Approach 2:
The system extracts and highlights the temporal relationship and contextual significance between driving events when reporting. By separating the essential contextual information from the raw event data, the system provides a streamlined report that focuses on the most critical safety-relevant patterns, reducing unnecessary time consumption while preserving important safety insights.
3Measurement precision
If context is added to driving event detection, then the measurement precision of safety assessment is improved, but the device complexity increases
Solution Approach 1:
The system segments driving events into distinct classes (e.g., speeding, following distance violations, lane departure) and analyzes their temporal relationships separately. This segmentation allows the system to apply simple time-interval rules to categorize combination events without requiring complex analysis algorithms. By breaking down the assessment into standardized event classes and time-based relationships, the system achieves precise safety assessment with minimal added complexity.
Data Source
AI summary
Systems and methods are provided for intelligent driving monitoring systems, advanced driver assistance systems and autonomous driving systems, and providing alerts to the driver of a vehicle. Combinations of co-occurring driving events may be detected and used to warn on anomalies, prevent accidents, provide feedback to the driver, and in general provide a safer driver experience.