Real-Time Driving Ability Evaluation Algorithm for Human-Machine Co-driving Decision

doi:10.12068/j.issn.1005-3026.2023.08.003

Abstract

Abstract: To meet the needs of real-time driving ability evaluation for human-machine co-driving decision problems for intelligent assisted driving systems， a method for real-time driving ability evaluation of drivers considering driving skill， driving state and driving style is proposed taking into account the unicity problem of existing driving evaluation researches.Based on the relative and continuous attributes of driving ability， firstly， an objective entropy-weighted relative evaluation model of driving skill is proposed based on the Gaussian kernel function， the relative evaluation model of driving state based on the time scale， and the soft classification model of driving style based on unsupervised decision classification tree. Secondly， a real-time driving ability evaluation mechanism and evaluation model with“punishment”and“affirmation”mechanisms are proposed to achieve real-time driving ability evaluation that meets the needs of human-machine shared decision control.Finally， the experimental comparison analysis shows that the proposed evaluation algorithm can meet the real-time， objective， and comprehensive requirements of human-machine co-driving decision control for drivers driving ability evaluation.

Key words: driving ability evaluation; driving style; Gaussian kernel function; unsupervised decision tree; human-machine co-driving system

CLC Number:

TP391

SU Wei-xing， XUE Feng， WEN Yong-gang， LIU Fang. Real-Time Driving Ability Evaluation Algorithm for Human-Machine Co-driving Decision[J]. Journal of Northeastern University(Natural Science), 2023, 44(8): 1078-1088.

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