LAUSR

For highly automated vehicles, effective take-over performance measures are essential for establishing quantitative take-over models and exploring approaches to improve take-over performance. However, there is a lack of comprehensive take-over performance measures that suitably combine multiple objective metrics based on an average evaluation from human drivers. In this study, we proposed a human-centered comprehensive measure of take-over performance (HCMTP). There are four main building blocks for the HCMTP. First, we adopted sparse principal component analysis to identify the main aspects of take-over performance based on multiple original objective take-over performance metrics. Second, we developed a scale of take-over performance assessment to obtain drivers’ original subjective self-assessments of take-over performance. Third, we established nonlinear individual mapping functions to acquire different drivers’ evaluation criteria for take-over performance. Fourth, we proposed a relabeling algorithm to obtain drivers’ average evaluation of take-over performance. To verify the effectiveness of the HCMTP, we conducted a verification experiment involving 68 participants. The results indicate that the HCMTP is effective and able to reduce the interference of individual differences, stochasticity, and data imbalance. This study contributes to identifying the main aspects of take-over performance, systematically understanding how human drivers subjectively evaluate take-over performance, and evaluating drivers’ take-over performance comprehensively.