A commercial vehicle driver safety scoring method based on CAN data

By using CAN data and stream processing technology, combined with entropy weighting method for two-stage weighted average aggregation, the static and reliability problems of commercial vehicle driving behavior safety evaluation in the prior art are solved, realizing dynamic scoring of driver behavior and improving the accuracy and adaptability of the scoring.

CN115564236BActive Publication Date: 2026-06-26XIAMEN XIZHONG INTERNET OF VEHICLES TECH CO LTD
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Patent Information

Application Number
CN202211216585.1
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2022-09-30
Publication Date
2026-06-26
Estimated Expiration
2042-09-30

AI Technical Summary

Technical Problem

In existing technologies, commercial vehicle driving behavior safety evaluation methods based on vehicle network data mainly rely on GPS data and entropy weight method. The data collection interval is large, the evaluation results are not reliable, and the entropy weight method cannot perform dynamic comprehensive evaluation and cannot adapt to changes in driver behavior over time.

Method used

Using CAN data as the source data, features are extracted in real time through stream processing, and then weighted average aggregation is performed twice using the entropy weight method. First, features are aggregated to the day in terms of trips, and then scored in terms of time periods to achieve dynamic safety scoring.

Benefits of technology

It achieves dynamic comprehensive scoring of commercial vehicle drivers, overcomes the shortcomings of static evaluation in existing technologies, and improves the reliability and dynamic adaptability of the scoring.

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Abstract

The application discloses a kind of based on CAN data's commercial vehicle driver safety score method, comprising the following steps: S1, source data is uploaded to platform in the form of stream data;S2, real-time feature extraction is carried out in unit of trip, then first weighted average aggregation is carried out in unit of day to obtain feature record;S3, score of each driver per day is obtained using entropy weight method;S4, platform interface end obtains score according to time period, and carries out second weighted average aggregation, obtains the score of the driver in the time period;S5, platform front end shows according to the result data returned by interface end.The method of the application obtains the score of a certain driver in a time period by the combination of twice weighted average aggregation and entropy weight method, so as to overcome the defect that single entropy weight method in prior art cannot dynamically comprehensively evaluate the object in a time period.
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