A comprehensive approach to evaluate human–machine conflicts in shared steering systems

The shared control authority between drivers and the steering system may lead to human–machine conflicts, threatening both traffic safety and driving experience of collaborative driving systems. Previous evaluation methods relied on subjective judgment and had a singular set of evaluation criteria, making it challenging to obtain a comprehensive and objective assessment. Therefore, we propose a two-phase novel method that integrates eye-tracking data, electromyography signals and vehicle dynamic features to evaluate human–machine conflicts. Firstly, through driving simulation experiments, the correlations between subjective driving experience and objective indices are analyzed. Strongly correlated indices are screened as the effective criteria. In the second phase, the indices are integrated through sparse principal component analysis (SPCA) to formulate a comprehensive objective measure. Subjective driving experience collected from post-drive questionnaires was applied to examine its effectiveness. The results show that the error between the two sets of data is less than 7%, proving the effectives of the proposed method. This study provides a low-cost, high-efficiency method for evaluating human–machine conflicts, which contributes to the development of safer and more harmonious human–machine collaborative driving.