# Determining Key Parameters with Data-Assisted Analysis of Conditionally Automated Driving
## 1 Introduction
### Related Work on Common Take-Over Parameters  
In recent years, researchers have been trying to evaluate TOs and TO performance in many ways. One quality aspect that is common to all of them, is the _reaction time_ after a TOR (_RT_, sometimes referred to as take-over time) [27; 28; 29; 30; 31; 32; 33; 34; 35; 36; 37; 38; 39]. Some authors also distinguish between reaction time to first steering (movement of the steering wheel angle by more than 2 degrees) and reaction time to first braking maneuver (application of the brake pedal by more than 10%) [40; 41; 42]. In many recent studies, RT is further subdivided into attention time (until the first glance at the dashboard and/or the road), time until motor readiness is established (i.e., hands on the steering wheel, foot on the pedal), and time until a reaction is executed [43; 44; 45; 46].  
However, measuring RT alone is not sufficient to determine the overall quality of TO [47; 48; 49; 50]. In fact, [PERSON] et al. have shown that driver reactions are generally of poorer quality when they are faster [51]. To evaluate the TO quality, most researchers considered _minimal time to collision (TTC)_, i.e., the minimum of the measured current times it would take a vehicle to collide with an obstacle, assuming constant speed and acceleration [52; 53; 54; 30; 27; 39; 30; 39]. In addition, various measures of _lane deviation_ are usually considered: steering wheel angle variability (standard deviation) [55; 29; 53], maximal lateral acceleration (deceleration) [30; 39; 43; 54], standard deviation of lane position [56; 31; 39; 44], distance to centerline [34], and slope of obstacle avoiding trajectory [50].  
Among physiological parameters, _eye-tracking data_ such as pupil size, duration and count of fixations and saccades, blink rate, gaze direction, and gaze dispersion appear most frequently [57; 58; 59; 60; 42; 43; 33; 40], followed by _heart rate_ (HR) or _heart rate variability_ (root mean square of successive differences) [57; 57; 61; 62; 31; 63] and _skin conductance_ (_SC_), e.g., phasic SC, count and magnitude of SC appearances above threshold, and SC response time [51; 42; 31]. A few studies also featured an _electroencephalogram_ (EEG), usually relying on theta to beta waves ratio (TBR) [52; 64]. These physiological parameters, in different combinations, are generally considered indicators of drivers' mental workload [62], autonomic arousal [58; 61], and situational awareness [59; 63]. Although physiological data seem to have great potential in research, there is still little consensus on their actual meaning and potential use cases [65]. In addition to physiological measurements, authors have measured drivers' mental workload during a TO by performing the n-back task [66], the detection-response task (DRT) [62; 67], or the non-driving task itself [29], as well as through questionnaires such as NASA's task load index (TLX) [68; 57; 69].  
In terms of driver background, aging has been shown to most often negatively affect TO performance, i.e., increasing reaction time [24; 29; 30; 35; 53; 70]. Other than aging, of the demographic data, only previous experience with TORs has shown a (positive) impact on TO performance [52].  
To include all available parameters in a single metric, [PERSON] and [PERSON] proposed the take-over performance index (TOPI), which consists of three parts: risk of collision, response intensity, and trajectory quality [52]. Similarly, [PERSON] et al. proposed a risk evaluation method based on the driving safety field [71], and [PERSON] et al. proposed the driver risk field-based estimation as a prediction of driver perceived risk [72].