Driving Pattern Server for Context-Aware Safe Driving Indexing
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing safe driving index systems fail to accurately reflect actual road conditions and driving patterns, leading to limitations in improving the accuracy and reliability of safe driving indices used in Usage-Based Insurance and Behavior-Based Insurance.
Innovation Solution
A server and control method that collect and analyze vehicle data to determine a safe driving index by comparing a target vehicle's driving pattern with a reference driving pattern based on traffic volume and safety class of the road segment, using communication devices to transmit the index for autonomous controls, traffic controls, or vehicle insurance purposes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If safe driving index systems use only basic driving information (mileage, driving time, speed), then the system complexity is low, but the accuracy and reliability of the safe driving index deteriorates because actual road conditions and driving patterns cannot be reflected
Solution Approach 1:
The patent combines multiple data sources including vehicle sensor data (acceleration, braking, steering), road condition data (traffic volume, weather, road type), and historical driving patterns into a unified safe driving index system. This integration allows comprehensive assessment of driving behavior while accounting for environmental factors, thereby improving measurement precision without excessive complexity increase
Solution Approach 2:
The system is designed to handle multiple types of data (real-time vehicle data, historical data, road condition data) and perform multiple functions (safe driving assessment, road condition adaptation, pattern recognition). This multi-functional approach enables the system to accurately reflect various driving scenarios while maintaining a unified processing framework
2Reliability
If the system evaluates driving patterns based on constant speed criteria only, then the ease of operation is high, but the reliability deteriorates because dangerous driving patterns (such as not slowing down when changing lanes to turn on highway) cannot be detected
Solution Approach 1:
The system dynamically adjusts driving pattern evaluation based on real-time road conditions and contextual factors. Instead of using fixed constant-speed criteria, the system adapts its assessment parameters according to traffic conditions, road type, and environmental factors, enabling accurate detection of dangerous patterns while maintaining operational simplicity through automated contextual analysis
Solution Approach 2:
The system incorporates feedback mechanisms that continuously monitor driving patterns and compare them against safe driving benchmarks. When dangerous patterns are detected (such as maintaining constant speed during lane changes on highways), the system provides real-time feedback and adjusts evaluation criteria, thereby improving reliability while maintaining ease of operation through automated monitoring
3Measurement precision
If the safe driving index does not consider traffic volume and road conditions, then the device complexity is low, but the measurement precision deteriorates because the index cannot reflect actual road conditions and driving patterns
Solution Approach 1:
The system uses road condition data (traffic volume, weather, road type) as intermediary factors that mediate between raw vehicle sensor data and the final safe driving index. These intermediary parameters enable accurate reflection of actual road conditions without requiring direct complex measurement of all environmental factors, thereby improving measurement precision while managing data collection complexity
Solution Approach 2:
The system performs preliminary analysis of road conditions and traffic patterns before evaluating driving behavior. By pre-processing and categorizing road condition data (traffic volume levels, road types, weather conditions), the system prepares contextual frameworks that enable accurate driving assessment without requiring complex real-time analysis during driving evaluation
Data Source
AI summary
A computing device includes: a communicator configured to communicate with a target vehicle and a plurality of vehicles; and a controller electrically connected to the communicator, wherein the controller is configured to collect vehicle data of the target vehicle and the plurality of vehicles, determine a traffic volume in an area in which the target vehicle is traveling, based on the collected vehicle data, determine a reference driving pattern based on the traffic volume in the area in which the target vehicle is traveling, determine a driving pattern of the target vehicle based on the vehicle data of the target vehicle, compare the driving pattern of the target vehicle with the reference driving pattern, and determine a safe driving index of the target vehicle based on a driving pattern difference between the driving pattern of the target vehicle and the reference driving pattern.