Driver Health Profile System Using Event Recorder Data
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Modern vehicles lack a comprehensive characterization of their operational behavior, which is essential for monitoring driver health and detecting anomalies, as existing vehicle event recorders primarily focus on anomalous events rather than continuous driver performance metrics.
Innovation Solution
A system that includes a vehicle event recorder and a processor to determine maneuver characteristics and statistics from sensor data, such as speedometer, GPS, and braking system data, to assess driver health and adjust data collection thresholds based on driver performance, providing indications and graphic feedback to users.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If vehicle event recorders focus on detecting anomalous events, then anomaly detection capability is improved, but continuous driver performance monitoring capability deteriorates
Solution Approach 1:
The vehicle event recorder is enhanced to perform multiple functions: it continues to detect anomalous events while simultaneously collecting and analyzing continuous driver performance metrics. The system processes sensor data to generate both anomaly detections and driver health profiles, making the device universal in its monitoring capabilities.
Solution Approach 2:
The monitoring system segments driver behavior into discrete maneuver characteristics (braking, cornering, acceleration) that can be independently measured and analyzed. This segmentation allows the system to track continuous performance metrics without compromising anomaly detection, as each maneuver type can be evaluated separately to build a comprehensive driver profile.
2Measurement precision
If comprehensive sensor data collection is implemented, then driver health monitoring accuracy is improved, but system complexity and data processing requirements worsen
Solution Approach 1:
The system extracts only the essential maneuver characteristics from the comprehensive sensor data that are most relevant to driver health assessment. By selecting key metrics (braking patterns, cornering behavior, acceleration responses) rather than processing all raw sensor data, the system maintains monitoring accuracy while reducing computational complexity and data processing requirements.
Solution Approach 2:
The system transforms raw sensor data into standardized maneuver characteristic parameters that simplify analysis. By converting complex sensor readings into normalized driver health metrics, the system improves monitoring accuracy while reducing the complexity of data processing and interpretation.
3Loss of information
If continuous monitoring of all drivers is performed, then fleet-wide driver health understanding is improved, but data collection and processing time worsen
Solution Approach 1:
The system continuously collects and pre-processes driver maneuver data in the background during normal operation, building driver health profiles over time. This preliminary action ensures that when fleet-wide analysis is needed, the data is already organized and ready for rapid processing, reducing the time required for comprehensive fleet health assessment.
Solution Approach 2:
The system maintains continuous data collection and processing operations without interruption, constantly updating driver health profiles as new maneuver data becomes available. This continuous operation eliminates batch processing delays and provides real-time fleet-wide driver health understanding without significant time loss.
Data Source
AI summary
A system for driver health profiling includes an interface and a processor. The interface is configured to receive a sensor data from a vehicle event recorder. The processor is configured to determine a maneuver characteristic based at least in part on the sensor data; determine a maneuver statistic based at least in part on the maneuver characteristic; and provide an indication based at least in part on the maneuver statistic.