Server-Based Road Safety Assessment Using Driving Pattern Analysis
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Solution Overview
Problem
Existing methods lack an effective way to identify and categorize dangerous road sections, which is crucial for road users and accident investigation, as they do not systematically utilize vehicle data to assess road safety characteristics.
Innovation Solution
A server-based system that communicates with vehicles, using an electronic map and reference datasets to identify and categorize road sections based on driving patterns, calculates driving characteristic datasets, and determines potential dangerous sections by comparing these datasets with predefined reference patterns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a server-based system collects and processes vehicle data from multiple vehicles to identify dangerous road sections, then the accuracy and reliability of road safety assessment is improved, but the system complexity and data processing requirements increase
Solution Approach 1:
The system segments the road network into multiple road sections based on electronic map data and geographic coordinates. Each road section is independently analyzed by comparing vehicle driving records against reference datasets, allowing distributed processing that maintains high accuracy while reducing overall system complexity through modular analysis units
Solution Approach 2:
Reference datasets representing normal driving patterns are pre-established and stored before actual danger identification occurs. When analyzing vehicle data, the system compares actual driving records against these pre-prepared reference datasets, eliminating the need for complex real-time pattern generation and significantly reducing processing complexity while maintaining reliability
2Measurement precision
If the system categorizes road sections into multiple section categories with different conditions, then the precision of danger identification is improved, but the complexity of data management and processing increases
Solution Approach 1:
The system applies different section categories (e.g., curve sections, intersection sections, slope sections) to different locations based on their specific characteristics. Each category has tailored reference datasets and analysis criteria, allowing precise danger identification for each road section type while managing complexity through localized rather than universal processing rules
Solution Approach 2:
Road sections are pre-categorized into different section categories based on electronic map data before vehicle data analysis. This preliminary classification organizes data management by creating structured categories with predefined reference datasets, making subsequent data processing more systematic and manageable despite the multiple categories involved
3Loss of information
If the system collects detailed vehicle datasets including moving tracks and component data from multiple vehicles, then the completeness of driving pattern analysis is improved, but the data transmission and storage requirements increase
Solution Approach 1:
The system extracts only the essential driving pattern information from vehicle datasets, specifically focusing on moving track coordinates and relevant component data that indicate driving behavior. Non-essential data is excluded, maintaining complete driving pattern analysis capability while significantly reducing the volume of data that needs to be transmitted and stored
Solution Approach 2:
Vehicle data is pre-processed on-board to extract and format only the essential driving records relevant to road section analysis before transmission to the server. This preliminary filtering at the source maintains information completeness for safety analysis while minimizing data transmission and storage requirements by eliminating redundant information
Data Source
AI summary
A method for identifying dangerous roads includes: receiving a vehicle dataset and extracting a driving record associated with a travelled road section therefrom; for a road section belonging to one of a plurality of section categories, obtaining a driving record from the vehicle dataset; calculating a driving characteristic dataset based on the driving records that are associated with an identified road section; and determining, whether the identified road section should be deemed as a potential dangerous road section based on at least the driving characteristic dataset and a corresponding one of a plurality of reference datasets that corresponds to one of the plurality of section categories.