An important question in automated driving research is how quickly drivers take over control of the vehicle in response to a critical event or a takeover request. Although a large number of studies have been performed, results vary strongly. In this study, we investigated mean takeover times from 129 studies with SAE level 2 automation or higher. We used three complementary approaches: (1) a within-study analysis, in which differences in mean takeover time were assessed for pairs of experimental conditions, (2) a between-study analysis, in which correlations between experimental conditions and mean takeover times were assessed, and (3) a linear mixed-effects model combining betweenstudy and within-study effects. The three methods showed that a shorter mean takeover time is associated with a higher urgency of the situation, not using a handheld device, not performing a visual non-driving task, having experienced another takeover scenario before in the experiment, and receiving an auditory or vibrotactile takeover request as compared to a visual-only or no takeover request. A consistent effect of age was not observed. We also found the mean and standard deviation of the takeover time were highly correlated, indicating that the mean is predictive of variability. Our findings point to directions for new research, in particular concerning the distinction between drivers' ability and motivation to take over, and the roles of urgency and prior experience.

Automated vehicles are expected to have a substantial impact on traffic flow efficiency, safety levels, and levels of emissions. However, field operational tests suggest that drivers may prefer to disengage adaptive cruise control (ACC) and resume manual control in dense traffic conditions and for maneuvers such as changing lanes. These so-called authority transitions can have substantial effects on traffic flow. To gain insight into these effects, a better understanding is needed of the relationships between these transitions, longitudinal dynamics of vehicles, and behavioral adaptations of drivers. In this context, a driving simulator experiment was set up to gain insight into the effects of authority transitions between ACC and manual driving on longitudinal dynamics of vehicles. Participants were assigned randomly to one of three conditions. In the control condition, participants drove manually. In the first experimental condition, a sensor failure was simulated at a specific location where drivers were expected to resume manual control. In the second experimental condition, drivers switched ACC off and on by pressing a button whenever they desired. Statistical tests indicated that the distributions of speed, acceleration, and time headway differed significantly between the three conditions. In the first experimental condition, the speed dropped after the sensor failure, and the time headway increased after the discretionary reactivation of ACC. These results seem to be consistent with previous findings and suggest that authority transitions between ACC and manual driving may significantly influence the longitudinal dynamics of vehicles and potentially mitigate the expected benefits of ACC on traffic flow efficiency.

Automated vehicles and driving assistance systems such as adaptive cruise control (ACC) are expected to reduce traffic congestion, accidents, and levels of emissions. Field operational tests have found that drivers may prefer to deactivate ACC in dense traffic flow conditions and before changing lanes. Despite the potential effects of these control transitions on traffic flow efficiency and safety, most mathematical models evaluating the impact of ACC do not adequately represent that process. This research aimed to identify the main factors influencing drivers’ choice to resume manual control. A mixed logit model that predicted the choice to deactivate the system or overrule it by pressing the gas pedal was estimated. The data set was collected in an on-road experiment in which 23 participants drove a research vehicle equipped with full-range ACC on a 35.5-km freeway in Munich, Germany, during peak hours. The results reveal that drivers were more likely to deactivate the ACC and resume manual control when approaching a slower leader, when expecting vehicles cutting in, when driving above the ACC target speed, and before exiting the freeway. Drivers were more likely to overrule the ACC system by pressing the gas pedal a few seconds after the system had been activated and when the vehicle decelerated. Everything else being equal, some drivers had higher probabilities to resume manual control. This study concludes that a novel conceptual framework linking ACC system settings, driver behavior characteristics, driver characteristics, and environmental factors is needed to model driver behavior in control transitions between ACC and manual driving.

Adaptive Cruise Control (ACC) and automated vehicles can contribute to reduce traffic congestion and accidents. Recently, an on-road study has shown that drivers may prefer to deactivate full-range ACC when closing in on a slower leader and to overrule it by pressing the gas pedal a few seconds after the activation of the system. Notwithstanding the influence of these control transitions on driver behaviour, a theoretical framework explaining driver decisions to transfer control and to regulate the target speed in full-range ACC is currently missing. This research develops a modelling framework describing the underlying decisionmaking process of drivers with full-range ACC at an operational level, grounded on Risk Allostasis Theory (RAT). Based on this theory, a driver will choose to resume manual control or to regulate the ACC target speed if its perceived level of risk feeling and task difficulty falls outside the range considered acceptable to maintain the system active. The feeling of risk and task difficulty evaluation is formulated as a generalized ordered probit model with random thresholds, which vary between drivers and within drivers over time. The ACC system state choices are formulated as logit models and the ACC target speed regulations as regression models, in which correlations between system state choices and target speed regulations are captured explicitly. This continuous-discrete choice model framework is able to address interdependencies across drivers' decisions in terms of causality, unobserved driver characteristics, and state dependency, and to capture inconsistencies in drivers' decision making that might be caused by human factors. The model was estimated using a dataset collected in an on-road experiment with fullrange ACC. The results reveal that driver decisions to resume manual control and to regulate the target speed in full-range ACC can be interpreted based on the RAT. The model can be used to forecast driver response to a driving assistance system that adapts its settings to prevent control transitions while guaranteeing safety and comfort. The model can also be implemented into a microscopic traffic flow simulation to evaluate the impact of ACC on traffic flow efficiency and safety accounting for control transitions and target speed regulations.