Comprehensive Ability Evaluation Method for Autonomous Vehicle Test Safety Officers

To address the absence of a quantifiable and operational evaluation framework for the comprehensive competence of autonomous vehicle test safety officers, and the inability of existing driver assessment systems to cover the full"monitor-takeover-feedback"responsibility chain, a three-dimensional evaluation index system encompassing reaction ability, driving ability, and description ability was constructed based on the job characteristics of test safety officers.Fifty frontline test safety officers participated in a staged experiment covering three modes: no-driving task, manual driving, and autonomous driving.Six categories comprising thirteen raw data indicators were collected, including reaction time, driving operations, and problem descriptions.For reaction ability evaluation, reaction time mean, variance, and error count were adopted as core indicators.Six algorithmic combinations involving Gaussian Mixture Model (GMM), Extremely Randomized Trees (ET), Random Forest (RF), and eXtreme Gradient Boosting (XGBoost) were preliminarily compared and screened through a four-level progressive strategy of clustering quality, information retention, variable utilization sufficiency, and classification accuracy, based on the silhouette coefficient (0.82), Akaike information criterion (AIC) of 186.3, weight distribution uniformity, and PRF (Precision, Recall, $F_{1}$-score).The GMM+ET fusion model was identified as the optimal evaluation model, achieving precision, recall, and $F_{1}$-score all equal to 1.00, with a mean accuracy of 0.99 under ten-fold cross-validation. For driving ability evaluation, a multiple linear regression model (adjusted $R^{2}=0.552, \operatorname{Prob}$ (F-statistic) $<0.01$) with accident frequency as the dependent variable was constructed, identifying maximum braking value, average throttle input, and optimal speed deviation as the three core influencing indicators.For description ability evaluation, a multi-expert grading method was employed to quantify the accuracy and conciseness of safety officers'problem feedback.Based on accident attribution statistics and Delphi method expert calibration, the weights of reaction ability, driving ability, and description ability were determined as 0.402, 0.496, and 0.102, respectively.The results demonstrate that the three ability dimensions are mutually independent, with a Pearson correlation coefficient of-0.123 between reaction and driving abilities.Driving ability score exhibits the strongest correlation with comprehensive ranking $(r=0.87)$, representing the core factor influencing overall competence.With a ten-fold cross-validation mean accuracy of 0.99 and $F_{1}$-score of 1.00, the GMM+ET fusion model significantly outperforms GMM+RF (mean $F_{1}$-score of 0.63) and k-means+XGBoost (mean $F_{1}$-score of 0.58) in variable utilization adequacy and evaluation accuracy.