Fleet Safety Metrics System EOBR Data Integration
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Solution Overview
Problem
Current systems for processing transport vehicle fleet safety data are cumbersome, user-unfriendly, and lack effective prioritization of safety concerns, often resulting in inefficiencies and false alarms due to arbitrary targets and a focus on driver behavior without considering other factors, and they struggle to integrate data from different EOBR devices within the same fleet.
Innovation Solution
A transport vehicle fleet data capturing system that processes and converts data from various sources into a common format, using sensors and network devices to generate user-friendly safety metrics and combined representations, allowing for dynamic and customizable reporting and filtering, and adjusting for erroneous data to improve accuracy and confidence in safety assessments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If existing EOBR data analysis systems are used, then safety data can be captured, but the systems are cumbersome and not user-friendly with excessive hardware and software resources required
Solution Approach 1:
The patent extracts and isolates only the essential safety-related metrics from the comprehensive EOBR data set, presenting a simplified view that focuses on critical safety information while eliminating unnecessary complexity. The system separates safety-critical data from operational data, delivering only what is needed for safety monitoring.
Solution Approach 2:
The system applies different levels of processing and presentation to different types of data based on their safety relevance. Critical safety parameters receive enhanced processing and prominent display, while less critical data is minimized or aggregated, creating a differentiated quality of information presentation that optimizes user focus.
2Reliability
If arbitrary targets are used for safety monitoring, then feedback can be provided, but false alarms increase and confidence in the system is lost
Solution Approach 1:
The system dynamically adjusts safety thresholds and parameters based on contextual factors such as vehicle type, operating conditions, and historical data. Rather than using fixed arbitrary targets, the system adapts parameters to reflect realistic safety boundaries, reducing false positives while maintaining sensitivity to genuine safety issues.
Solution Approach 2:
The system implements continuous feedback loops where safety assessments are refined based on actual outcomes and patterns. When safety events are identified, the system learns from these instances and adjusts its monitoring parameters, creating a self-improving system that reduces false alarms over time while maintaining high reliability.
3Adaptability or versatility
If driver-centric systems are used, then driver behavior can be monitored, but other safety factors are overlooked
Solution Approach 1:
The system is designed to universally monitor multiple types of safety factors beyond just driver behavior, including vehicle mechanical status, environmental conditions, and route-related safety concerns. The platform handles diverse data sources and types through a unified analysis framework that treats all safety factors with equal importance.
Solution Approach 2:
The safety monitoring system segments safety concerns into distinct categories such as driver behavior, vehicle condition, environmental factors, and operational parameters. This segmentation allows comprehensive coverage of all safety aspects while organizing information in a manageable and analyzable structure that can be processed systematically.
4Adaptability or versatility
If data from different EOBR device types are integrated, then complete fleet coverage is achieved, but data format conversion becomes cumbersome
Solution Approach 1:
The system introduces a standardized intermediate data format and translation layer that acts as a mediator between diverse EOBR device types and the analysis platform. This intermediary layer automatically handles format conversion and normalization, allowing the system to accept data from multiple device types without requiring complex custom integration for each device.
Solution Approach 2:
The system employs a universal data interface and processing framework that can handle multiple EOBR device types through a single standardized approach. This universal interface automatically adapts to different data formats and protocols, eliminating the need for device-specific processing pathways and reducing overall system complexity.
Data Source
AI summary
Vehicle operations data is received from a plurality of data capturing devices installed at a plurality of transport vehicles. The vehicle operations data is representative of sensor output captured by the data capturing devices. Data sets are processed to obtain different vehicle safety metrics defined by the different types of sensors. An indication of non-conformance can be output when a compared vehicle safety metric does not conform to a threshold. Vehicle safety metrics can be combined into a combined representation of a categorical aspect of vehicle safety, which can be dynamically updated whenever a metric is added or removed. Combined representations can be filtered or grouped according to a reporting criterion to generate reports for drivers, units, company divisions, and the like. Vehicle operations data can be converted from multiple different formats used by different data sources to a common format used for processing metrics and combined representations.