Onboard Event Scoring System with Calibration Feedback
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Solution Overview
Problem
Existing vehicle risk assessment systems lack a robust and reliable operational architecture for event scoring, failing to provide comprehensive reporting options and efficient data transmission, leading to suboptimal event detection and analysis.
Innovation Solution
A Driver Risk Assessment System and Method with Calibrating Automatic Event Scoring, which includes onboard event detectors that record and score driving events, compare local scores to historical values, and selectively upload data, optimizing bandwidth usage and continuously improving through manual review, offering automated and manual scoring options.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If automatic event scoring is implemented without manual calibration, then event detection speed is improved, but scoring accuracy deteriorates
Solution Approach 1:
The system implements feedback by continuously comparing automated event scores against manually scored events and using this discrepancy data to recalibrate the automated scoring algorithm. This closed-loop feedback mechanism allows the system to maintain high detection speed while progressively improving accuracy through iterative refinement of scoring parameters based on actual human judgment.
Solution Approach 2:
The system dynamically adjusts scoring parameters and weights based on calibration data from manual reviews. By changing the parameters of the automated scoring algorithm through continuous learning from manual calibration results, the system resolves the contradiction between speed and accuracy—maintaining rapid event detection while adapting its scoring criteria to improve precision over time.
2Loss of information
If all event data is transmitted to remote server, then event analysis completeness is improved, but data transmission bandwidth consumption increases
Solution Approach 1:
The system extracts and transmits only the essential event information and scored data to the remote server rather than all raw event data. By taking out only the necessary components (event type, location, time, and automated score), the system maintains analysis completeness while dramatically reducing bandwidth consumption.
Solution Approach 2:
The system segments data transmission into selective uploads based on event severity and score thresholds. High-scoring events trigger comprehensive data transmission, while low-scoring events result in minimal or no transmission. This segmentation approach preserves analytical completeness for critical events while optimizing bandwidth usage overall.
3Reliability
If manual event review is performed for all events, then scoring reliability is improved, but processing time increases
Solution Approach 1:
The system applies partial manual review to only those events that require human judgment, rather than reviewing all events. By using automated scoring as a preliminary filter and reserving manual review for edge cases or high-risk events, the system maintains high reliability for critical scoring while significantly reducing overall processing time.
Solution Approach 2:
The system performs preliminary automated scoring and filtering before manual review. This preliminary action classifies events into categories and pre-processes the data, so that when manual review is needed, only the most critical portions require human attention. This staged approach maintains scoring reliability for complex events while minimizing the time investment required for manual verification.
Data Source
AI summary
A Driver Risk Assessment System and Method Having Calibrating Automatic Event Scoring is disclosed. The system and method provide robust and reliable event scoring and reporting, while also optimizing data transmission bandwidth. The system includes onboard vehicular driving event detectors that record data related to detected driving events and selectively store or transfer data related to said detected driving events. If elected, the onboard vehicular system will score a detected driving event, compare the local score to historical values previously stored within the onboard system, and upload selective data or data types to a remote server or user if the system concludes that a serious driving event has occurred. Importantly, the onboard event scoring system, if enabled, will continuously evolve and improve in its reliability by being periodically re-calibrated with the ongoing reliability results of manual human review of automated predictive event reports. The system may further respond to independent user requests by transferring select data to said user at a variety of locations and formats.