Driver’s under-arousal occurred in automated driving systems (ADS) impairs takeover safety. This study aims to determine electrodermal activity (EDA) features’ importance for driver’s arousal quantification. A car-following simulator study was conducted with participants concurrently executing four levels of cognitive tasks, triggering four levels of arousal. Participants’ skin conductance (SC) data were collected and decomposed into tonic (skin conductance level, SCL) and phasic (skin conductance response, SCR) components. Seventeen features extracted from SC, SCL and SCR were compared. As a result, SCR-relevant features showed higher significance and larger effect size than SC and SCL features in response to cognitive load, which suggests the phasic component dominates changes in EDA under varying cognitive load. Moreover, the SCR rate TTP.nSCRs, identified by $0.03 ~\mu \text{S}$ thresholds, attained the largest effect size among all features for driver’s arousal measurement. A varying time windows (TW) analysis showed that TTP.nSCRs was the most suggested arousal metric when TW was over 20 s, whereas the sum of SCRs amplitudes TTP.AmpSum was preferred when TW was less than 20 s. For driver’s arousal quantification with multi-features, the top five suggested features were TTP.nSCRs, SC_Rate5, CDA.SCR (or CDA.ISCR), CDA.AmpSum, and TTP.AmpSum. Although male drivers showed higher values of EDA features than female drivers, the sensitivity of the proposed EDA features stands across gender and individuals. This study promotes an improved understanding of EDA changes in human cognitive process. The sensitive EDA features proposed could be used from uni- or multi-modalities in driver state management and takeover-safety prediction for ADS.

Driver distraction is one of the main causes of motor-vehicle accidents. However, the impact on traffic safety of tasks that impose cognitive (non-visual) distraction remains debated. One particularly intriguing finding is that cognitive load seems to improve lane keeping performance, most often quantified as reduced standard deviation of lateral position (SDLP). The main competing hypotheses, supported by current empirical evidence, suggest that cognitive load improves lane keeping via either increased physical arousal, or higher gaze concentration toward the road center, but views are mixed regarding if, and how, these possible mediators influence lane keeping performance. Hence, a simulator study was conducted, with participants driving on a straight city road section whilst completing a cognitive task at different levels of difficulty. In line with previous studies, cognitive load led to increased physical arousal, higher gaze concentration toward the road center, and higher levels of micro-steering activity, accompanied by improved lane keeping performance. More importantly, during the high cognitive task, both physical arousal and gaze concentration changed earlier in time than micro-steering activity, which in turn changed earlier than lane keeping performance. In addition, our results did not show a significant correlation between gaze concentration and physical arousal on the level of individual task recordings. Based on these findings, various multilevel models for micro-steering activity and lane keeping performance were conducted and compared, and the results suggest that all of the mechanisms proposed by existing hypotheses could be simultaneously involved. In other words, it is suggested that cognitive load leads to: (i) an increase in arousal, causing increased micro-steering activity, which in turn improves lane keeping performance, and (ii) an increase in gaze concentration, causing lane keeping improvement through both (a) further increased micro-steering activity and (b) a tendency to steer toward the gaze target.