Driving Simulator Study Research Articles

Car-Following Behavior of Human-Driven Vehicles in Mixed-Flow Traffic: A Driving Simulator Study

Connected and autonomous vehicles (CAVs) and human-driven vehicles (HDVs) will inevitably coexist on roads in the future, creating mixed-flow traffic. The heterogeneous car-following (CF) behavior of HDVs can degrade the control performance of CAVs and introduce inefficiencies in CAV operations. To address these challenges, it is necessary to comprehend HDV CF behavior in mixed-flow traffic. This driving simulator-based study investigates HDV CF behavior in mixed-flow traffic under three different CAV control settings (string-stable, string-unstable, and HDV-like). The effects of traffic congestion level and demographic characteristics on CF behavior are also considered. Statistical analysis and CF model calibration, based on trajectory data collected from 72 participants in driving simulator experiments, are performed to examine the impacts of these factors on string stability, traffic efficiency, and safety. Then, online parameter estimation is conducted to illustrate the time-varying desired time headway, and sensitivity to spacing and speed variations (i.e., CF behavior evolution). Additionally, analysis of post-experiment interview results and eye-tracking data show that the string-stable CAV control setting is preferred by most HDV drivers, but can trigger driver distraction. The results also provide insights for CF behavior prediction and the optimal mixed platoon formation to enhance CAV benefits for traffic flow.

Impact of level 2 automation and ADHD symptomatology on young drivers’ attention maintenance

Attention Deficit Hyperactivity Disorder (ADHD) is a common mental disorder that affects drivers’ attention maintenance skills. Vehicle automation is another factor that has an increasingly important effect on drivers’ attention or concentration level during driving. This driving simulator study aimed to investigate how drivers’ glance behavior changed under different levels of vehicle automation and according to high or low ADHD symptomology. During the drive, participants were asked to complete a map task on a tablet while their glance behavior was recorded. Results imply that the participants tend to have a longer mean off-road glance duration and a larger long off-road glance ratio when they were under the L2 automation level compared to manual mode. ADHD symptomatology and the interaction between ADHD and vehicle automation were not found to be significant. These findings indicate that vehicle automation negatively impacts drivers’ attention maintenance skills. Future work would consider more kinds of secondary tasks that can assess the participants’ attention maintenance skills from various aspects.

Fifty shades of frustration: Intra- and interindividual variances in expressing frustration

Negative emotions like frustration can lead to risky driving behavior in manual driving and may hinder the acceptance of innovative automated vehicles. The option to automatically recognize such negative emotions of vehicle users with so called affect-aware systems has gained increasing attention within the last few years. This offers the possibility to adapt vehicle functions, such as the human–machine interface, in real-time. Emotional expressions in face and body form potential indicators for user frustration. Previous studies have investigated expressions of frustration in the context of driving and mobility, but have neglected situational and interindividual differences. In this paper, we examined the possibility to improve the recognition of frustration by considering individual differences. For this, a driving simulator study with 50 participants and a real-world driving study with 23 participants were conducted. An analysis of participants’ facial expressions during frustrating driving situations confirms previously reported expressions of frustration (Brow Lowerer, Dimpler, Brow Raiser, Smile and Lip Press). In addition, the results also hint towards high variance between and low variance within participants for all other expressions, suggesting the existence of individual-typical expressions of frustration. Hence, future frustration-aware systems could benefit from considering these individual differences by using a universally trained algorithm that is then customized towards each individual.

Happiness and high reliability develop affective trust in in-vehicle agents.

The advancement of Conditionally Automated Vehicles (CAVs) requires research into critical factors to achieve an optimal interaction between drivers and vehicles. The present study investigated the impact of driver emotions and in-vehicle agent (IVA) reliability on drivers' perceptions, trust, perceived workload, situation awareness (SA), and driving performance toward a Level 3 automated vehicle system. Two humanoid robots acted as the in-vehicle intelligent agents to guide and communicate with the drivers during the experiment. Forty-eight college students participated in the driving simulator study. The participants each experienced a 12-min writing task to induce their designated emotion (happy, angry, or neutral) prior to the driving task. Their affective states were measured before the induction, after the induction, and after the experiment by completing an emotion assessment questionnaire. During the driving scenarios, IVAs informed the participants about five upcoming driving events and three of them asked for the participants to take over control. Participants' SA and takeover driving performance were measured during driving; in addition, participants reported their subjective judgment ratings, trust, and perceived workload (NASA-TLX) toward the Level 3 automated vehicle system after each driving scenario. The results suggested that there was an interaction between emotions and agent reliability contributing to the part of affective trust and the jerk rate in takeover performance. Participants in the happy and high reliability conditions were shown to have a higher affective trust and a lower jerk rate than other emotions in the low reliability condition; however, no significant difference was found in the cognitive trust and other driving performance measures. We suggested that affective trust can be achieved only when both conditions met, including drivers' happy emotion and high reliability. Happy participants also perceived more physical demand than angry and neutral participants. Our results indicated that trust depends on driver emotional states interacting with reliability of the system, which suggested future research and design should consider the impact of driver emotions and system reliability on automated vehicles.

Impact of duration of monitoring before takeover request on takeover time with insights into eye tracking data

Safety has become the primary concern of automated driving system (ADS) in recent years. Compared with highly automated driving (L4 and above), conditionally automated driving (L3/L3+ ADS) seems to be a moderate choice, where drivers are required to respond to the takeover request (TOR) whenever necessary. It is the system’s responsibility to make sure that the takeovers would be safe at the time of issuing the TOR. To realize that, a lot of factors need to be taken into consideration. As it has been found that drivers’ eyes-on-road gazes increase slowly in the first few seconds while transferring to manual driving from automated driving and drivers’ gaze behaviors are related with situation awareness, the main aim of this study is to investigate the impact of duration of monitoring before the TOR on takeover time and whether there is a positive or negative relationship between the two. To verify these, we designed a driving simulator study where the TOR was issued 0 s, 5 s, 10 s and ≥ 15 s after the non-driving-related task has ended. Twelve scenarios were designed, and the results from 36 participants showed that there was indeed a statistically significant difference, however, the relationship was neither positive nor negative, which was close to a parabola. Analyzing results of eye movements and gaze behavior further supported this conclusion. It is therefore concluded the duration of monitoring before the TOR should neither be too short nor too long, and 5–7 s would be appropriate choices. This is desirable not only for improving takeover performance of drivers but also for improving the prediction model for predicting takeover performance of drivers that has yet to be studied, so as to improve safety, reliability and acceptance of the ADS.

Texting While Driving: A Literature Review on Driving Simulator Studies.

Road safety is increasingly threatened by distracted driving. Studies have shown that there is a significantly increased risk for a driver of being involved in a car crash due to visual distractions (not watching the road), manual distractions (hands are off the wheel for other non-driving activities), and cognitive and acoustic distractions (the driver is not focused on the driving task). Driving simulators (DSs) are powerful tools for identifying drivers' responses to different distracting factors in a safe manner. This paper aims to systematically review simulator-based studies to investigate what types of distractions are introduced when using the phone for texting while driving (TWD), what hardware and measures are used to analyze distraction, and what the impact of using mobile devices to read and write messages while driving is on driving performance. The review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analysis extension for Scoping Reviews (PRISMA-ScR) guidelines. A total of 7151 studies were identified in the database search, of which 67 were included in the review, and they were analyzed in order to respond to four research questions. The main findings revealed that TWD distraction has negative effects on driving performance, affecting drivers' divided attention and concentration, which can lead to potentially life-threatening traffic events. We also provide several recommendations for driving simulators that can ensure high reliability and validity for experiments. This review can serve as a basis for regulators and interested parties to propose restrictions related to using mobile phones in a vehicle and improve road safety.

Sonification Use Cases in Highly Automated Vehicles: Designing and Evaluating Use Cases in Level 4 Automation

The introduction of highly automated driving systems is expected to significantly change in-vehicle interactions, creating opportunities for the design of novel use cases and interactions for occupants. In this study, we sought to identify and extract these novel use cases and determine preliminary auditory display recommendations for these novel situations. We developed and generated use cases for level 4 automated vehicles through an expert workshop (N = 17) and online focus group interviews (N = 12). Most of the use cases we generated were then tested, apart from meditation, and user opinions were collected in a driving simulator study (N = 20). Results indicated participants were interested in functions that support their experience with both driving and non-driving related interactions in highly automated vehicles. Three categories of use cases for level 4 automated vehicles were developed: driving automation use cases, immersion use cases, and in-vehicle notification use cases. For the driving simulator study, we tested three display modalities for interaction with drivers: visual alert only, non-speech with visual, and speech with visual. In terms of situation awareness (SA), the non-speech with visual display was associated with significantly better SA for the use case consisting of a planned increase in automation level than the speech-with visual display. This study will provide guidance on sonification design to advance user experiences in highly automated vehicles.

Pilot study: Effect of roles and responsibility training on driver's use of adaptive cruise control between younger and older adults

With the development of driver support systems (SAE Levels 1 – 2), drivers must take on new monitoring and supervision tasks in additional to manual driving. Training is necessary to clarify drivers' new roles and promote safe usage and trust in these systems. Providing training for lower-levels of automation may also benefit drivers’ acceptance of future Fully Automated Vehicles (FAVs, SAE Level 5). However, younger and older drivers differ in training preferences (e.g., owner's manual vs on-road trial and error) and hold different attitudes towards automation. This study investigates the effects of additional training on drivers' roles and responsibilities when using Adaptive Cruise Control (ACC, SAE Level 1) for younger and older drivers. Thirty-nine adults (20 younger + 19 older) were trained on one of two ACC training protocols: basic (system functionality, operational procedures, and limitations) and comprehensive (basic training + ACC background and roles of responsibilities). Participants’ situational trust and ACC usage was evaluated before, during, and after experiencing an emergency event while using ACC in a driving simulator study. Results showed that the comprehensive training promoted drivers' situational trust in ACC, ACC usage, and the acceptance of FAvs Compared to younger drivers, older drivers used ACC less, reported less dynamic situational trust, higher levels of workload, and lower acceptance. Overall, comprehensive training resulted in older drivers behaving similarly to younger drivers. The comprehensive training also promoted the acceptance of FAVs for both younger and older drivers. In conclusion, training of drivers’ roles and responsibilities has an impact on drivers’ usage of ACC and may be particularly useful for older drivers.

Adding visual contextual information to continuous sonification feedback about low-reliability situations in conditionally automated driving: A driving simulator study

In conditionally automated driving, control transitions where drivers are asked to take over manual control quickly are a known challenge as drivers are likely “out-of-the-loop” due to automated driving. We previously demonstrated a useful sonification feedback design to support drivers for takeover events by manipulating background music to convey real-time changes in the automation reliability level. The present research examines two types of auxiliary information—explanations about the potential sources for reduced reliability and recommended actions to prepare for a potential takeover event—to communicate event-specific context about the low-reliability situations indicated by the sonification feedback. In a driving simulator with SAE level 3 automation, forty participants were assigned to one of four interface conditions: (i) sonification feedback only, (ii) sonification feedback and visual auxiliary information providing explanations, (iii) sonification feedback and visual auxiliary information providing action recommendations, or (iv) sonification feedback and both explanatory and action-recommending auxiliary information. Participants experienced six low-reliability events that belonged to three types of situations that may eventually lead to a takeover event: operational problems, system limitations, and unexpected events. Two events became system-requested takeover events. Our results showed that auxiliary information impacted visual information processing but not the takeover decisions or response times to takeover requests. The situation type—characterized by the availability of cues and presence of hazards in the environment—also played a significant role in both participants’ takeover decisions and visual information processing behaviors. These findings have implications for designing effective feedback for not only Level 3 automation, which we studied, but also for Level 4 dual-mode vehicles that could allow transitions to manual driving when exiting their operational design domains and for Level 2 automation, where drivers may need more support to monitor the road effectively and make more timely takeover decisions.

How Do Cognitive Interventions Impact Driver Aggressiveness in China?—A Driving Simulator Study

Aggressive driving behavior is one of the main reasons for traffic crashes in China. However, how cognitive interventions affect impulsive driving behavior is still unknown. In this study, a simulated drive was constructed to evaluate the influence of different cognitive interventions. In addition to speeding behavior in limit zones, speed when passing pedestrians, at intersections, and on the whole drive was adopted as a measure to evaluate aggressive driving behaviors. Forty-eight young drivers were recruited with monetary rewards. Compared to the control group, the penalty feedback intervention significantly reduced the mean speed in the 40 km/h zone, the 80 km/h zone, and when passing pedestrians. The combined feedback intervention significantly reduced the distance ratio of speeding in the 40 km/h and 80 km/h zones, as well as the mean speed at the intersection and on the whole drive. However, the interaction effects between the driving task and intervention method were not remarkably observed during aggressive driving behaviors. The findings from this study provide evidence that different cognitive interventions contribute distinctively to improving aggressive driving behaviors. These conclusive results have possible implications for the design of vehicle warning systems and traffic safety interventions.

Assessing the Impact of Safety Countermeasures on Dilemma Zones at Signalized Intersections of Urban Roads: a Driving Simulator Study

When drivers approach a signalized intersection at the start of the yellow signal, they might be reluctant to decide whether to stop or go through the intersection due to the dilemma zone (DZ), with the consequent risk of improper behaviors that can lead to rear-end collisions and right-angle crashes. The main goal of this research is to analyze the driver's behavior at the onset of the yellow signal and to identify the most effective countermeasures for the resolution of the dilemma zone in order to improve the safety and efficiency of urban signalized intersections. A driving simulator study was carried out, and three different countermeasures were specifically designed and tested: Green Signal Countdown Timers (GSCT) (C1); a new scheme of vertical and horizontal warning signs (C2); and an in-vehicle advanced driving assistance system that uses Augmented Reality (AR) and connected vehicle technologies (C3). Forty-six volunteers took part in the experiments, driving the same scenario four times (three with countermeasures and one as a baseline condition). The results demonstrated that with countermeasure C1, the length of the DZ increased with respect to the baseline condition; moreover, the stop/go drivers’ decisions were found to be more inconsistent, increasing the potential risk of rear-end crashes. Conversely, countermeasures C2 and C3 resulted in a reduction of DZ length (-30.5% and 21.6%, respectively); in addition, C2 was found to be the one that recorded the greatest consistency of drivers’ decision-making behaviors, while C3 provided the drivers with timely and personalized early-stop warnings and recorded fewer wrong behaviors. Based on the results of this study, C2 and C3 countermeasures might be a good way to improve safety and operations at urban signalized intersections and cut down on the number of drivers who aren't sure what to do when the yellow light comes on.

The Effect of Driving Style on Responses to Unexpected Vehicle Cyberattacks

Vehicle cybersecurity is a serious concern, as modern vehicles are vulnerable to cyberattacks. How drivers respond to situations induced by vehicle cyberattacks is safety critical. This paper sought to understand the effect of human drivers’ risky driving style on response behavior to unexpected vehicle cyberattacks. A driving simulator study was conducted wherein 32 participants experienced a series of simulated drives in which unexpected events caused by vehicle cyberattacks were presented. Participants’ response behavior was assessed by their change in velocity after the cybersecurity events occurred, their post-event acceleration, as well as time to first reaction. Risky driving style was portrayed by scores on the Driver Behavior Questionnaire (DBQ) and the Brief Sensation Seeking Scale (BSSS). Half of the participants also received training regarding vehicle cybersecurity before the experiment. Results suggest that when encountering certain cyberattack-induced unexpected events, whether one received training, driving scenario, participants’ gender, DBQ-Violation scores, together with their sensation seeking measured by disinhibition, had a significant impact on their response behavior. Although both the DBQ and sensation seeking have been constantly reported to be linked with risky and aberrant driving behavior, we found that drivers with higher sensation seeking tended to respond to unexpected driving situations induced by vehicle cyberattacks in a less risky and potentially safer manner. This study incorporates not only human factors into the safety research of vehicle cybersecurity, but also builds direct connections between drivers’ risky driving style, which may come from their inherent risk-taking tendency, to response behavior to vehicle cyberattacks.

Evaluating the effects of the route guidance variable message signs on driving behaviors—a driving simulation study

Objective Route guidance variable message signs (VMS) are widely applied in traffic incident management on highways by providing real-time spatiotemporal guidance information. The improper panel of route guidance VMS is likely to diminish the compliance with guidance and induce risky driving behaviors, disrupting the traffic flow or even causing crashes. This article aims to investigate the effects of route guidance VMS on driving behaviors in three aspects, vehicle operation, visual perception, and route choice. Methods A driving simulation study with 32 participants was carried out to investigate the driving performance under four different VMS recognizing conditions: baseline (a typical guide sign) and three route guidance VMS panel schemes. Results Significant differences in average speed, speed fluctuation, average acceleration, and fixation proportion were found under various VMS recognition conditions. VMS3 had the least negative effects on vehicle operation and visual perception, and the compliance rate under VMS3 was the highest. The possible reasons are as follows: VMS3 has the simplified highway network structure and highlights the road directions with an eye-catching symbol, which can increase the comprehensibility of the guidance information while driving. Conclusions Drivers need to take multiple actions under high-speed driving conditions while recognizing VMS, including reading the route guidance VMS, remaking route decisions, and operating the vehicle concurrently. Under such circumstances, the improper VMS panel would attract more drivers’ attention and induce excessive risk compensatory behaviors, reducing drivers’ compliance with guidance and situation awareness, and increasing crash risks. In addition, some VMS related traffic management strategies were proposed to improve safety and mobility of highways and further provide a basis for the formulation of related standards.

Comparing Subjective Similarity of Automated Driving Styles to Objective Distance-Based Similarity.

This study explores subjective and objective driving style similarity to identify how similarity can be used to develop driver-compatible vehicle automation. Similarity in the ways that interaction partners perform tasks can be measured subjectively, through questionnaires, or objectively by characterizing each agent's actions. Although subjective measures have advantages in prediction, objective measures are more useful when operationalizing interventions based on these measures. Showing how objective and subjective similarity are related is therefore prudent for aligning future machine performance with human preferences. A driving simulator study was conducted with stop-and-go scenarios. Participants experienced conservative, moderate, and aggressive automated driving styles and rated the similarity between their own driving style and that of the automation. Objective similarity between the manual and automated driving speed profiles was calculated using three distance measures: dynamic time warping, Euclidean distance, and time alignment measure. Linear mixed effects models were used to examine how different components of the stopping profile and the three objective similarity measures predicted subjective similarity. Objective similarity using Euclidean distance best predicted subjective similarity. However, this was only observed for participants' approach to the intersection and not their departure. Developing driving styles that drivers perceive to be similar to their own is an important step toward driver-compatible automation. In determining what constitutes similarity, it is important to (a) use measures that reflect the driver's perception of similarity, and (b) understand what elements of the driving style govern subjective similarity.

Ehmi on the Vehicle or Just a Traffic Light? A Driving Simulator Study

Ehmi on the Vehicle or Just a Traffic Light? A Driving Simulator Study

Impacts of countermeasures on driving performance through drivers' attention in rural curves: a driving simulation study

Impacts of countermeasures on driving performance through drivers' attention in rural curves: a driving simulation study

Evaluating the Effectiveness of Countermeasures in Improving the Safety of Highway Merging Zones

The merging process from an acceleration lane to the through lane of a motorway is an important aspect at interchanges, both in terms of safety and operating conditions. Several studies have demonstrated that the merging zones of interchanges are the main sources of interference between through and entering vehicles. In this context, incorrectly assessed gaps and headways can lead to severe crashes and traffic delays at interchanges. This study investigates the effectiveness of different countermeasures to improve the safety of merging zones and the ability of through drivers to adopt safe headways to vehicles entering from the motorway on-ramp. Four different countermeasures were tested in a driving simulator study: i) a gap metering signalization, consisting of a Variable Message Sign (VMS) and new pavement markings; ii) a static symbol projected on the vehicle's windshield by means of a Head-Up Display (HUD) that informed the driver to maintain safe headways to the entering vehicle; iii) a dynamic symbol based on Augmented Reality (AR) technology, built into connected vehicle technology, that provided additional visual information to the driver about his current headway to the entering vehicle; iv) the AR system was also tested with an additional audio warning. The driving behavior of forty-four drivers under the four different configurations based on the tested countermeasures was then compared to the driving behavior recorded under a configuration without any countermeasures (baseline condition). The results revealed significant positive effects of all the countermeasures, especially of the AR systems that helped through drivers to adjust their headway to the entering vehicle and thus the most effective solution for improving the safety of the merging zone. This study confirmed the great potential of AR and connected vehicle technologies to improve general safety conditions on the road network, especially in risky situations and difficult maneuvers.

Validation of a Driving Simulator for Road Tunnel Behavioural Studies

Introduction. According to European regulations, road tunnel safety is strategic in the management of national and international road corridors. Although the accident rate is lower in tunnels than on open roads, the severity of crashes in tunnels is higher due to the presence of hard lateral obstacles and limited space in case of lane departure. Driving simulation studies can support design decisions to assess the impact of any safety improvement albeit driving simulators must be validated to understand how the experimental results relate to real driving conditions. Method. This study deals with the behavioural validation of the fixed base driving simulator of the RSDS Lab for safety studies for tunnels. Field speed and lateral position data for vehicles were collected by image analysis of video sequences collected from the CCTV cameras in five sections of the Fréjus tunnel (Italy-France). The tunnel was faithfully modelled in the virtual scenario, and the same data were collected by extracting records at the same cameras’ stations. Thirty-five participants were involved in a between-subject experiment. Fifteen drivers with Italian B licenses drove a car, and twenty professional drivers with Italian C and/or D licenses drove a heavy truck. Results and Conclusions. Normality tests for data distributions and t-tests for the comparison between real and simulated data were conducted. The simulator achieved the relative validation for truck speeds (with values observed in the simulation always lower than those observed in real driving), and absolute validation with regard to truck lateral position. Opposite outcomes were obtained for cars, with absolute validity for speed and relative validity for lateral position. The relative-absolute validation of the driving simulator enables us to establish how experimental outcomes can be generalized to understand the impact of any safety countermeasure.

Detection of Driver Cognitive Distraction Using Machine Learning Methods

Driver distraction is one of the primary causes of crashes. As a result, there is a great need to continuously observe driver state and provide appropriate interventions to distracted drivers. Cognitive distraction refers to the “look but not see” situations when the drivers’ eyes are focused on the forward roadway, but their mind is not. Typically, cognitive distractions can result from fatigue, conversation with a co-passenger, listening to the radio, or other similarly loading secondary tasks that do not necessarily take a driver’s eyes off the roadway. This makes it one of the hardest distractions to detect as there are no visible clues of driver distraction. In this study, we have identified features from different sources including eye-tracking, physiological, and vehicle kinematics data that are relevant towards the classification of distracted and non-distracted drivers via the analysis of data collected from a driving simulator study involving 40 drivers across multiple driving scenarios. The key classification algorithms implemented include Random Forest, Decision Trees and Support Vector Machines. A reduced feature set including pupil area, pupil vertical and horizontal motion was found to be predictive of driver distraction while maintaining an average accuracy of 90% across various road types. Additionally, the impact of road types on driver behaviour was also identified. The findings of the study has practical application towards the design of driver distraction monitoring systems.

Drivers’ gaze patterns when resuming control with a head-up-display: Effects of automation level and time budget

The removal of drivers’ active engagement in driving tasks can lead to erratic gaze patterns in SAE Level 2 (L2) and Level 3 (L3) automation, which has been linked to their subsequential degraded take-over performance. To further address how changes in gaze patterns evolve during the take-over phase, and whether they are influenced by the take-over urgency and the location of the human-machine interface, this driving simulator study used a head-up display (HUD) to relay information about the automation status and conducted take-over driving experiments where the ego car was about to exit the highway with variations in the automation level (L2, L3) and time budget (2 s, 6 s). In L2 automation, drivers were required to monitor the environment, while in L3, they were engaged with a visual non-driving related task. Manual driving was also embodied in the experiments as the baseline. Results showed that, compared to manual driving, drivers in L2 automation focused more on the HUD and Far Road (roadway beyond 2 s time headway ahead), and less on the Near Road (roadway within 2 s time headway ahead); while in L3, drivers’ attention was predominantly allocated on the non-driving related task. After receiving take-over requests (TORs), there was a gradual diversion of attention from the Far Road to the Near Road in L2 take-overs. This trend changed nearly in proportion to the time within the time budget and it exaggerated given a shorter time budget of 2 s. While in L3, drivers’ gaze distribution was similar in the early stage of take-overs for both time budget conditions (2 s vs. 6 s), where they prioritized their early glances to Near Road with a gradual increase in attention towards Far Road. The HUD used in the present study showed the potential to maintain drivers’ attention around the road center during automation and to encourage drivers to glance the road earlier after TORs by reducing glances to the instrument cluster, which might be of significance to take-over safety. These findings were discussed based on an extended conceptual gaze control model, which advances our understanding of gaze patterns around control transitions and their underlying gaze control causations. Implications can be contributed to the design of autonomous vehicles to facilitate the transition of control by guiding drivers’ attention appropriately according to drivers’ attentional state and the take-over urgency.