Predictive Vehicle Driving Conditions From Road-Time Segment Filtering
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Solution Overview
Problem
Traditional predictive vehicle driving condition methods rely on single types of data and involve complex computations, leading to low accuracy in prediction results.
Innovation Solution
A method and system that integrates historical and real-time data, utilizing a GPS to generate a road-point-in-time set, and applies filtering and sequencing to construct an optimal driving condition, reducing data volume and calculation load.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional prediction methods use single type of data (historical or real-time), then data processing is simpler, but prediction accuracy is low
Solution Approach 1:
The patent combines historical driving data and real-time driving data into a unified prediction framework. Historical data provides baseline patterns while real-time data captures current conditions, merging both data types to achieve accurate predictions without excessive complexity through systematic integration methods
Solution Approach 2:
The patent segments the prediction process into distinct modules: data acquisition module, feature extraction module, prediction model module, and result output module. This segmentation allows each module to handle specific tasks independently, reducing overall system complexity while maintaining high prediction accuracy through specialized processing at each stage
2Measurement precision
If traditional prediction methods use complex computations, then more thorough analysis is achieved, but error likelihood increases and processing efficiency decreases
Solution Approach 1:
The patent extracts only the most relevant features from driving data (speed, acceleration, position, time) rather than processing all available data. This feature extraction approach maintains prediction accuracy by focusing on critical parameters while significantly reducing computational load and processing time
Solution Approach 2:
The patent transforms raw driving data into standardized parameters with consistent formats and units. By normalizing data parameters and using standardized time intervals, the system achieves accurate predictions with simplified computations, avoiding complex calculations while maintaining thorough analysis
Data Source
AI summary
The present disclosure discloses a method and system for constructing a predictive vehicle driving condition, a device and a medium. The method includes: acquiring a first road set and historical vehicle driving data; screening distances less than or equal to a distance threshold from the first distance set, generating a road point-in-time set; sequencing a plurality of point-in-time corresponding to the same road segment, and cutting at two adjacent point-in-time with a time difference greater than a time threshold, to obtain a plurality of road segments; establishing road segment databases; screening segments with segment feature data meeting a second preset condition from all the road segment databases, and generating a road-condition segment set; and sequencing a plurality of condition segments corresponding to each road segment, and screening condition segments meeting a third preset condition from all the segment sequences, to generate an optimal driving condition.