Driving Trip Pattern Analysis Server
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Solution Overview
Problem
Current telematics systems lack an effective method to analyze driving data and calculate driver scores based on vehicle location and operational data, failing to identify driving trip patterns and assess risks accurately.
Innovation Solution
A system comprising a driving analysis server that collects and analyzes vehicle location and operational data to identify driving trips, determine risk assessment values, and calculate driver scores by matching current trips to existing patterns, using GPS and telematics devices to transmit data and retrieve risk information from databases.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If telematics systems collect and analyze vehicle location and operational data, then driver scores can be calculated to reflect safe driving habits, but the system lacks effective methods to identify driving trip patterns and assess risks accurately
Solution Approach 1:
The patent segments driving data into distinct trip patterns by identifying start and end locations, times, and characteristics. This segmentation allows the system to analyze specific trip types (commute, errand, leisure) separately, improving risk assessment accuracy for each pattern while managing overall system complexity through modular analysis.
Solution Approach 2:
The system performs preliminary actions by pre-identifying and storing driving trip patterns in a database before actual risk assessment occurs. Historical trip data is analyzed and stored with associated risk factors, enabling faster and more accurate real-time risk assessment without repeating complex analysis for each new data point.
2Measurement precision
If the system analyzes detailed vehicle operational data to calculate driver scores, then more accurate safety analysis is achieved, but processing time and computational resources increase
Solution Approach 1:
The system performs preliminary analysis by pre-processing vehicle operational data and storing it in organized formats with associated trip patterns. This preliminary action includes categorizing driving behaviors, identifying risk factors, and preparing data structures that enable rapid retrieval and processing during actual driver score calculations.
Solution Approach 2:
The patent uses copying by creating simplified representations of complex driving trip patterns. Instead of processing all raw operational data each time, the system creates condensed trip pattern copies that capture essential characteristics (location, time, duration, risk factors) enabling fast comparison and analysis while maintaining accuracy.
3Reliability
If the system stores comprehensive driving trip pattern data in databases, then risk assessment can be improved, but data storage requirements and system resources increase
Solution Approach 1:
The system applies extraction by pulling out and storing only the essential characteristics of driving trips that are relevant to risk assessment. Instead of storing complete raw data sets, the system extracts key parameters (start/end locations, trip duration, time of day, identified risk factors) and stores these condensed representations, reducing storage requirements while maintaining assessment reliability.
Solution Approach 2:
The patent inverts the traditional approach by storing trip patterns and risk factors in advance rather than generating them during real-time analysis. This inversion allows the system to build a comprehensive database of historical patterns with pre-calculated risk assessments, improving reliability for future assessments while reducing the computational burden and storage needs for raw data.
Data Source
AI summary
A driving analysis server may be configured to receive vehicle location data and/or operation data from one or more vehicle systems, identify driving trips and/or driving patterns based on the vehicle data, determine risk assessment values corresponding to the driving trips and driving patterns, and calculate driver scores based on the analyzed driving trip and driving pattern data. Destination locations may be identified for a vehicle's driving trips, and information relating to the destination locations may be retrieved and analyzed to determine risk factors and risk assessment values associated with driving to and from the destination, as well as parking at the destination. Specific driving trip types or purposes may be identified, and driving scores may be calculated based on the vehicle location and time data, including the risk factors, risk assessment values, and the determined trip types or purposes.