Trip Classification Using Acceleration Data Segmentation
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Solution Overview
Problem
Conventional methods fail to effectively classify trips based on acceleration data alone, lacking the ability to assess the safety of a driver's journey and provide feedback for improving driving behavior.
Innovation Solution
A method and system that utilize GPS data, including timestamps, lateral, and longitudinal acceleration, to segment journeys, detect driving events, compute event scores, normalize them using percentile mapping, and classify trips as 'very good,' 'good,' 'average,' or 'bad' using fuzzy classification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional methods use multiple sensor data or maneuver based classification for driving style classification, then classification capability is improved, but device complexity and data processing requirements increase
Solution Approach 1:
The patent extracts and focuses on using only acceleration data from sensors already present in modern vehicles, eliminating the need for multiple sensor types. By concentrating on processing acceleration data through sophisticated algorithms (segmentation, event detection, scoring), the system achieves accurate trip classification without adding device complexity or requiring additional sensors.
2Productivity
If conventional methods perform driving style classification without trip-level analysis, then processing speed is improved, but the ability to assess overall journey safety and provide comprehensive feedback is worsened
Solution Approach 1:
The patent segments the entire trip into multiple sub-trips based on traffic information such as congestion levels and road conditions. This segmentation enables the system to maintain processing efficiency by analyzing smaller segments while still providing comprehensive trip-level safety assessment. The aggregation of sub-trip scores produces an overall trip classification, preserving both processing speed and holistic safety evaluation.
3Loss of information
If the system classifies trips into detailed categories (very good, good, average, bad, very bad), then feedback quality for driver improvement is improved, but classification complexity and computational requirements increase
Solution Approach 1:
The patent employs fuzzy logic classification that transforms multiple input parameters (segment scores, event scores, traffic conditions) into a five-level classification output. This parameter transformation approach enables detailed feedback categories while managing complexity through standardized fuzzy logic rules, making the system computationally feasible while delivering comprehensive safety assessment.
Data Source
AI summary
Reckless behavior of drivers like, speeding, sudden acceleration and swerving through lanes can cause fatality and financial loss. Conventional methods mainly focus on driving style classification. The conventional methods mainly focus on driver classification and are not able to provide trip classification of a driver. Hence there is a challenge in trip classification of the driver based on acceleration data. The present disclosure for trip classification addresses the problem of end to end trip classification based on the acceleration data. Here, a journey is segmented into a plurality of sub-journey segments and each sub-journey segment is associated with a plurality of driving events. An event score is calculated for each sub-journey and a normalization is performed on the event score. Further, the journey is classified into at least one of good, average or bad based on the normalized data by utilizing a fuzzy based classification.