# Effects of Level 3 Automated Vehicle Drivers’ Fatigue on Their Take-Over Behaviour: A Literature Review

Authors: Hanying Guo; Yuhao Zhang; Shanshan Cai; Xin Chen
Journal: Journal of Advanced Transportation
 (2021)

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## Abstract

With advancements in automated driving technology, Level 3 vehicle automation has been proliferating. In Level 3, drivers can turn their attention to things unrelated to driving and only takeover in cases of emergency. However, the transition to the main driving task can be affected by different physical and psychological changes in drivers such as fatigue and distraction. Although existing research in the field of manual driving has demonstrated the effects of fatigue on driver performance, there have been relatively few studies on fatigue in the field of automated driving. Fatigue decreases drivers’ alertness, which can prevent drivers from conducting necessary emergency takeover manoeuvres in a safe and timely manner. This systematic literature review was conducted to establish relationships between driving fatigue and takeover behaviour in the existing literature on Level 3 automated driving systems. Different from existing reviews of driving fatigue, this paper focuses on the commonly used and most effective evaluation indicators of driving fatigue and further discusses the methods to improve the takeover efficiency of fatigued drivers. This is the first detailed systematic review of general relationships between fatigue in automated driving and drivers’ takeover behaviours when the vehicle sends a takeover request. Researchers are invited to expand on the findings here to further clarify the findings on the associations between driver fatigue indicators in automated vehicle driving and drivers’ takeover response times.


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## Body

## 1. Introduction

InSAE J3016 [1], the Society of Automotive Engineers ranked vehicle automation capabilities from Level 0 (no automation) to Level 5 (complete automation) [2], and only vehicles ranked Level 3 and above are capable of automated driving. Presently, because of limitations such as the high cost of available sensors, most vehicles on the road today are ranked at Level 1 or 2 [3], with features that generally consist of automated emergency braking, adaptive cruise control, and/or blind spot detection which have improved road traffic safety to a certain extent. Nevertheless, according to statistics released by the World Health Organization in 2015 [4], more than 1.2 million people die each year on the road, with millions of people sustaining serious injuries. Among these accidents, 90% are caused by human error [5].Hence, research on improving driving safety has focused on advanced automated vehicles such that drivers might no longer be needed [6], a position that is not without foundation. Boggs et al. said that the introduction of advanced automated driving represents an opportunity to significantly mitigate the risk associated with the main contributing factor [7]. Automating a large portion of the driving work eliminates the human risk factors, possibly increasing driving safety [8]. The current research focus is on Level 3, also known as conditionally automated driving in the transition to complete automated driving.Level 3 automated vehicles perform most of the driving tasks in excellent traffic and environmental conditions, but the driver must rapidly takeover vehicle control under special circumstances that might occur at any time [9]. Such special circumstances are precipitating emergency takeover events that the autopilot detects, notifying the driver through a takeover request (TOR) that manual intervention is required, such as unexpected lane obstacles, sensor limitations, or crossing the unknown [10].Compared with manual vehicles, Level 3 automated vehicles appear to have unprecedented advantages in driving safety. As early as 2002, Young and Stanton [11] emphasised that vehicle automation systems could reduce the mental work for driving and drivers could delegate some of their driving tasks to the autopilot. However, under the definition of Level 3, as the driver’s workload is reduced, the driver’s role shifts from “manipulator” of the vehicle to “supervisor” of its operations [12], which means that the safe operation of an automated vehicle still depends on a driver’s nearly continuous monitoring of the environment outside of the vehicle [13].Unfortunately, when drivers are no longer effectively controlling their vehicles, they can experience fatigue from having been reduced to monotonous continuous monitoring [14]. Fatigue is one of the most hazardous conditions a driver can experience. Fatigued driving is the culprit in 20%–30% of fatal traffic accidents [15] and one of the major causes of rear-end collisions; indeed, previous researchers have shown that fatigued driving is closely related to serious traffic accidents, which represent a significant safety hazard and economic burden to communities [16, 17].In drivers of Level 3 automated vehicles, fatigue will seriously affect their alertness and their response times to autopilot TORs [18, 19]. Banks and Stanton established that drivers’ ability to safely takeover vehicles depends on timely and correct perceptions of critical events in the vehicle’s operating state and external driving environment [20], called situational awareness (SA). Lu et al. demonstrated in a 2020 study [21] that drivers with low SA had more difficulty responding correctly to hazardous events on the road and showed the impaired perception of other drivers’ speeds and distances; additionally, drivers’ SA correlates closely with their own mental states [22]. Hence, fatigued driving and poor takeover behaviour can lead to vehicle instability and increase the risk of traffic accidents [23].Because of the serious consequences of fatigued driving, the mechanism of driving fatigue and its influence on driving behaviour has long been a research focus in the field of driving safety. However, although many researchers have investigated driving fatigue in manually operated vehicles [24–28], far too little attention has been paid to driving fatigue in Level 3 vehicle operation. Particularly, a high degree of uncertainty exists around how fatigue affects driver takeover behaviour, a problem that must be solved to improve the safety of automated driving.With this paper, we recognised the limitations of the extant studies and reviewed the literature on automatic driving fatigue in detail. Using “Level 3 automated vehicles,” “conditionally automated driving,” “driver fatigue,” “takeover,” and synonyms as search keywords and “OR” and “AND” as logical symbols, we searched four databases: Elsevier, Web of Science, Google Scholar, and IEEE Xplore. We identified 65 articles from the past 10 years, from which we excluded investigations of effects other than fatigue on driver takeover ability because they were not relevant to the topic we were discussing. We ultimately extracted eight studies related to this paper’s topic, which we elaborate in Sections2 and 3 of this paper.Following this introduction, we discuss the causes and types of driving fatigue and summarise the different methods of assessing fatigue for Level 3 driving including its effects on driver takeover efficiency. Finally, we discuss methods of improving driver takeover efficiency in Level 3 driving to offer valuable insights into improving the driving safety of automated vehicles.


## 2. Occurrence of Driving Fatigue

Fatigue in driving builds gradually with driving time [29]. May and Baldwin [30] identified that driving fatigue can be caused by the time of day (circadian rhythms), sleep deprivation and sleep restriction, and repetitive, monotonous driving tasks that demand high mental load. Researchers have divided driving fatigue into sleep related or task related based on the causal factors contributing to the fatigued state. Sleep-related fatigue is a subjective feeling related to natural human circadian rhythms [31]. Task-related fatigue is further divided into active or passive, where active fatigue is triggered by high-density traffic or mental overload and passive fatigue tends to appear in highly predictable environments that are low in complexity [32]. Figure 1 summarises the triggers of different types of fatigue. To conserve space in the article, we focus on passive fatigue here, although both sleep-related fatigue and active fatigue are significant as well.Figure 1
Predisposing factors of driving fatigue.


### 2.1. Monotonous Manual Driving

Researchers have demonstrated that long periods of monotonous manual driving induce passive fatigue; when the driving task or environment is highly predictable, drivers tend to decrease the attention they allocate to driving tasks, and underload is likely to occur. For example, Ting et al. [33] measured fatigue in drivers who completed a 90 min simulation of highway driving, and results showed significantly poorer performance with longer driving time. Schmidt et al. [34] studied the effects of three hours of monotonous driving on drivers’ alertness and found that drivers’ response times to emergencies on the freeway increased with longer driving time. This finding is consistent with results by Farahmand and Boroujerdian [35], who investigated 14 Iranian drivers and found the same associations with repetitive, unstimulating driving.


### 2.2. Conditionally Automated Driving

When most or all of a vehicle’s actual operation is automated, the driver’s main task is to monitor the driving environment, and on monotonous roadways with little traffic, drivers are at risk of fatigue over extended periods of driving time. Given the above results for manual vehicle operation, it is unsurprising that we uncovered findings of passive fatigue among drivers of Level 3 automated vehicles that increased with more time under automation [36].In 2019, Vogelpohl et al. [22] described a faster and more extreme progression of fatigue for drivers of automated vehicles in the same driving time. Drivers in the conditionally automated driving condition exhibited facial signs of fatigue inch by inch after 15–35 min of driving, whereas it took closer to 40 min before manual drivers showed fatigue.One finding that we did not anticipate was that Gonçalves et al. [37] investigated automated driving fatigue, and drivers in their study rated themselves as subjectively tired after periods of automated driving. These results align with those of Körber et al. [38], who found no significant differences in response times between drivers of automated and manual vehicles after 45 min of automated driving; instead, the respondents in that study gave subjective reports that their alertness and mental state were significantly reduced. We speculate that self-reported fatigue of drivers appears earlier than some physiological or behavioural fatigue manifestations after a long period of driving a Level 3 automated vehicle. A possible explanation for this might be that long periods of underload may result in mental wandering in drivers [39], which impairs their cognitive ability and leads to increased self-reported fatigue. These and many other findings confirm that passive fatigue is a common phenomenon during extended, monotonous driving whether or not a vehicle is automated. However, in contrast to manual driving tasks, even the driving task itself is monotonous with Level 3 automated vehicle operation, which is more likely to lead to passive than active fatigue, and it has been well established that fatigued drivers have difficulty being vigilant and show poor response times to unexpected takeover events.


## 2.1. Monotonous Manual Driving

Researchers have demonstrated that long periods of monotonous manual driving induce passive fatigue; when the driving task or environment is highly predictable, drivers tend to decrease the attention they allocate to driving tasks, and underload is likely to occur. For example, Ting et al. [33] measured fatigue in drivers who completed a 90 min simulation of highway driving, and results showed significantly poorer performance with longer driving time. Schmidt et al. [34] studied the effects of three hours of monotonous driving on drivers’ alertness and found that drivers’ response times to emergencies on the freeway increased with longer driving time. This finding is consistent with results by Farahmand and Boroujerdian [35], who investigated 14 Iranian drivers and found the same associations with repetitive, unstimulating driving.


## 2.2. Conditionally Automated Driving

When most or all of a vehicle’s actual operation is automated, the driver’s main task is to monitor the driving environment, and on monotonous roadways with little traffic, drivers are at risk of fatigue over extended periods of driving time. Given the above results for manual vehicle operation, it is unsurprising that we uncovered findings of passive fatigue among drivers of Level 3 automated vehicles that increased with more time under automation [36].In 2019, Vogelpohl et al. [22] described a faster and more extreme progression of fatigue for drivers of automated vehicles in the same driving time. Drivers in the conditionally automated driving condition exhibited facial signs of fatigue inch by inch after 15–35 min of driving, whereas it took closer to 40 min before manual drivers showed fatigue.One finding that we did not anticipate was that Gonçalves et al. [37] investigated automated driving fatigue, and drivers in their study rated themselves as subjectively tired after periods of automated driving. These results align with those of Körber et al. [38], who found no significant differences in response times between drivers of automated and manual vehicles after 45 min of automated driving; instead, the respondents in that study gave subjective reports that their alertness and mental state were significantly reduced. We speculate that self-reported fatigue of drivers appears earlier than some physiological or behavioural fatigue manifestations after a long period of driving a Level 3 automated vehicle. A possible explanation for this might be that long periods of underload may result in mental wandering in drivers [39], which impairs their cognitive ability and leads to increased self-reported fatigue. These and many other findings confirm that passive fatigue is a common phenomenon during extended, monotonous driving whether or not a vehicle is automated. However, in contrast to manual driving tasks, even the driving task itself is monotonous with Level 3 automated vehicle operation, which is more likely to lead to passive than active fatigue, and it has been well established that fatigued drivers have difficulty being vigilant and show poor response times to unexpected takeover events.


## 3. Evaluating Fatigue in Conditionally Automated Driving

Much of the literature since the mid-1990s has emphasised fatigue detection technology, which has been widely studied in manual driving [14]. Scholars have used various methods to collect fatigue indicators from different perspectives, including but not limited to vehicle operating characteristics [40], driver physiological signals [41], behavioural characteristics [42], and subjective scores [34] related to human or vehicle features. However, although there are now several effective fatigue detection and assessment methods for manual driving, this does not appear to be the case for automated driving. In one longitudinal study, there were significant differences in driver behaviour between manual and automated driving independent of driver fatigue [43]. This finding was unexpected and suggests that most current methods of assessing fatigue in manual drivers have limitations in doing so for automated driving. For this reason, we set out to identify previous research methods of evaluating fatigue in Level 3 automated driving. As shown in Table 1, we divided the fatigue indicators from the previous studies into three categories: physiological index, behavioural index, and subjective score. Next, we discuss some of these common fatigue indicators.Table 1
Evaluation indicators of automated driving fatigue.
StudiesYearEvaluation indicatorsPhysiological indexBehavioural indexSubjective score[38]2015Pupil diameterPERCLOSThe Dundee Stress State QuestionnaireBlink durationBlink frequency[43]2016PERCLOSThe Karolinska Sleepiness Scale (KSS)Blink durationHead movement[44]2016Saccadic eye movementsBlink durationThe amplitude of the P1 component of EFRP[45]2017PERCLOSKSSBlink durationBlink frequency[46]2018PERCLOSBlink durationHead movement[47]2019Blink durationKSS[48]2019PERCLOSKSS[22]2020PERCLOSKSSBlink durationYawningRubbing of the face


### 3.1. Blink Behaviour

Table1 shows that driver characteristics have been one of the most frequently studied potential indicators of fatigue in automated driving, especially blink behaviour. Although some physiological signals, such as electroencephalogram (EEG) [49], electrocardiogram [34], and electromyography [50], have good accuracy in fatigue detection, these methods are highly disruptive; they require direct contact between the sensor and the body for collecting data, which can interfere with normal driving operation. By contrast, researchers determined that data on blink behaviour can be collected with less interference to the driver [51]; eye blink data can be captured by a camera on the vehicle dashboard or by an eye detector worn by the driver.Blink behaviour is a highly valued driver fatigue evaluation index [52]. Fernández et al. [53] considered the human eye to be the easiest and most remarkable data source for facial analysis because the eyes reflect mental states and the focus of attention. Eye movements such as PERCLOS (percentage of eyelid closure time) and blink duration can reflect the physiological states of drivers [42, 54] and are the main means of detecting early signs of fatigue [46].Although extensive research has been conducted on eye detection systems, there are still some obstacles to be overcome. First, previous research has established that such methods rely on image processing technology and have strict requirements for environmental conditions; light interference and objects blocking the eyes will sharply reduce detection accuracy. Second, variations in the facial expressions and head movements of the drivers as well as in the surrounding lighting can make it difficult to accurately ascertain eye localisation [55], which affects the accuracy of experimental results.


### 3.2. Subjective Scores

Subjective scale scores have been instrumental in our understanding of driver fatigue; using self-report scales, drivers estimate their own status. In research involving driving automated vehicles, researchers have typically asked subjects to give subjective reports of their fatigue states after each takeover event. Although the Dundee Stress State Questionnaire is an excellent 5-point Likert scale for assessing mental wandering, it contains two subscales with 16 items in total [56] that drivers must complete after each event; it is difficult to complete the scale after each event when there are numerous takeover events in experiments. Thus, current researchers now mostly use the KSS (see Table 1). The KSS is a 9-point self-fatigue state assessment scale with only one item [57]; drivers can easily give oral estimates of their fatigue states without affecting the driving experiment. Some cross-sectional studies suggest an association between fatigue and KSS score [26, 27].Subjective evaluation results are reliable, low in cost, and obtained in real time, but they have some limitations. Drivers give subjective assessments of their fatigue which are subjective, so their observations can be affected by problems such as memory effects or insufficient self-introspection skills [58]. Additionally, data from several on-road and simulator studies indicate that self-assessment of fatigue might not even be accurate. For example, in one study, Moller et al. [59] found that according to EEG data, participants were experiencing microsleeps while they drove, but the drivers’ self-reported sleepiness scores never changed significantly. Overall, there is some evidence that drivers’ subjective ratings can fail to align with objective data. Second, drivers in experiments usually self-report fatigue orally, and they usually report retrospectively; therefore, their retrospective evaluations of their own fatigue can suffer from recall bias [22].Thus, for this study, we supplemented subjective self-report data on automatic driving fatigue with the results of collecting blink behaviour data. Future researchers could investigate different scales such as the fatigue subscale of the Profile of Mood States Short Form [60] or the Stanford Sleepiness Scale [33].


### 3.3. Combining Indicators

In an investigation of driving fatigue, May and Baldwin [30] reported that combining fatigue ratings with multiple indicators was more reliable and more effectively detected driving fatigue. Table 1 clearly indicates that most automated driving fatigue researchers supplement self-report scale scores with fatigue indicators such as pupil diameter or EEG amplitude, which supports May’s findings.In 2016, for instance, Takeda et al. [44] described a study of automated driving fatigue using three indicators: saccadic eye movements, the amplitude of the P1 component of eye-fixation-related brain potential (EFRP), and blink duration. Based on the assumption that the driver’s attention state in a Level 3 autonomous car is similar to that of the co-pilot passenger, when the automated vehicle was running, the co-pilot passenger mainly imitates the driver's monitoring task. First, the authors measured electrical potential with two electrodes placed near the eyes that could detect small saccadic eye movements with high temporal resolution. Then, the authors collected EEG signals to estimate the amplitude of the P1 component of EFRP. Finally, they measured eye blink duration to measure participants’ arousal levels. Several lines of evidence indicate that saccadic eye movements are an accurate measure of drivers’ attention behaviours [61] and that the amplitude of the P1 component of EFRP decreases with decreased attention [62]. These two indicators accurately captured the study participants’ attention changes during autonomous driving and that their attention changes were closely related to their fatigue states [63].Additionally, Table1 shows that some drivers indicate fatigue by swaying their heads, yawning, or rubbing their faces. In the studies we summarised, the researchers often used these body movement data to supplement blink behaviours in assessing fatigue. Taken together, these studies’ findings support that the accuracy of measurements of driver fatigue in automated driving increases substantially when multiple measurement methods and indicators are combined. Further research should be undertaken to investigate the influence of data collection methods on the drivers.


## 3.1. Blink Behaviour

Table1 shows that driver characteristics have been one of the most frequently studied potential indicators of fatigue in automated driving, especially blink behaviour. Although some physiological signals, such as electroencephalogram (EEG) [49], electrocardiogram [34], and electromyography [50], have good accuracy in fatigue detection, these methods are highly disruptive; they require direct contact between the sensor and the body for collecting data, which can interfere with normal driving operation. By contrast, researchers determined that data on blink behaviour can be collected with less interference to the driver [51]; eye blink data can be captured by a camera on the vehicle dashboard or by an eye detector worn by the driver.Blink behaviour is a highly valued driver fatigue evaluation index [52]. Fernández et al. [53] considered the human eye to be the easiest and most remarkable data source for facial analysis because the eyes reflect mental states and the focus of attention. Eye movements such as PERCLOS (percentage of eyelid closure time) and blink duration can reflect the physiological states of drivers [42, 54] and are the main means of detecting early signs of fatigue [46].Although extensive research has been conducted on eye detection systems, there are still some obstacles to be overcome. First, previous research has established that such methods rely on image processing technology and have strict requirements for environmental conditions; light interference and objects blocking the eyes will sharply reduce detection accuracy. Second, variations in the facial expressions and head movements of the drivers as well as in the surrounding lighting can make it difficult to accurately ascertain eye localisation [55], which affects the accuracy of experimental results.


## 3.2. Subjective Scores

Subjective scale scores have been instrumental in our understanding of driver fatigue; using self-report scales, drivers estimate their own status. In research involving driving automated vehicles, researchers have typically asked subjects to give subjective reports of their fatigue states after each takeover event. Although the Dundee Stress State Questionnaire is an excellent 5-point Likert scale for assessing mental wandering, it contains two subscales with 16 items in total [56] that drivers must complete after each event; it is difficult to complete the scale after each event when there are numerous takeover events in experiments. Thus, current researchers now mostly use the KSS (see Table 1). The KSS is a 9-point self-fatigue state assessment scale with only one item [57]; drivers can easily give oral estimates of their fatigue states without affecting the driving experiment. Some cross-sectional studies suggest an association between fatigue and KSS score [26, 27].Subjective evaluation results are reliable, low in cost, and obtained in real time, but they have some limitations. Drivers give subjective assessments of their fatigue which are subjective, so their observations can be affected by problems such as memory effects or insufficient self-introspection skills [58]. Additionally, data from several on-road and simulator studies indicate that self-assessment of fatigue might not even be accurate. For example, in one study, Moller et al. [59] found that according to EEG data, participants were experiencing microsleeps while they drove, but the drivers’ self-reported sleepiness scores never changed significantly. Overall, there is some evidence that drivers’ subjective ratings can fail to align with objective data. Second, drivers in experiments usually self-report fatigue orally, and they usually report retrospectively; therefore, their retrospective evaluations of their own fatigue can suffer from recall bias [22].Thus, for this study, we supplemented subjective self-report data on automatic driving fatigue with the results of collecting blink behaviour data. Future researchers could investigate different scales such as the fatigue subscale of the Profile of Mood States Short Form [60] or the Stanford Sleepiness Scale [33].


## 3.3. Combining Indicators

In an investigation of driving fatigue, May and Baldwin [30] reported that combining fatigue ratings with multiple indicators was more reliable and more effectively detected driving fatigue. Table 1 clearly indicates that most automated driving fatigue researchers supplement self-report scale scores with fatigue indicators such as pupil diameter or EEG amplitude, which supports May’s findings.In 2016, for instance, Takeda et al. [44] described a study of automated driving fatigue using three indicators: saccadic eye movements, the amplitude of the P1 component of eye-fixation-related brain potential (EFRP), and blink duration. Based on the assumption that the driver’s attention state in a Level 3 autonomous car is similar to that of the co-pilot passenger, when the automated vehicle was running, the co-pilot passenger mainly imitates the driver's monitoring task. First, the authors measured electrical potential with two electrodes placed near the eyes that could detect small saccadic eye movements with high temporal resolution. Then, the authors collected EEG signals to estimate the amplitude of the P1 component of EFRP. Finally, they measured eye blink duration to measure participants’ arousal levels. Several lines of evidence indicate that saccadic eye movements are an accurate measure of drivers’ attention behaviours [61] and that the amplitude of the P1 component of EFRP decreases with decreased attention [62]. These two indicators accurately captured the study participants’ attention changes during autonomous driving and that their attention changes were closely related to their fatigue states [63].Additionally, Table1 shows that some drivers indicate fatigue by swaying their heads, yawning, or rubbing their faces. In the studies we summarised, the researchers often used these body movement data to supplement blink behaviours in assessing fatigue. Taken together, these studies’ findings support that the accuracy of measurements of driver fatigue in automated driving increases substantially when multiple measurement methods and indicators are combined. Further research should be undertaken to investigate the influence of data collection methods on the drivers.


## 4. The Influence of Driver Fatigue on Takeover Efficiency

In previous studies of manual driving, some researchers have used vehicle motion data during driving to determine whether drivers are fatigued; measures include the number of microcorrections to the steering wheel and the standard deviation of lateral position [64, 65]. These features are among the several manual driving fatigue indicators that have long been studied. For instance, Feng [66] measured two typical steering behaviours for awake and drowsy drivers using an angle sensor installed on the steering column and found that driver fatigue level was accurately reflected by the number of microcorrections to the steering wheel. However, drivers do not need to manually manipulate a Level 3 automated vehicle under normal operation, so it is difficult to use this method to accurately assess fatigue in automated vehicle drivers.However, conditional automated vehicles have their own ways to characterize driver fatigue: when a Level 3 automated vehicle encounters obstacles, it issues a TOR to the driver, which requires the driver to switch to manual driving [67]. The driver must rapidly reposition the hands on the steering wheel and the feet on the brake pedals at the same time that they must determine the obstacle and whether steering or braking is the best response given surrounding traffic conditions. After an extended period of automated driving, it can be difficult for drivers to maintain optimum mental states, which can have a negative impact on takeover efficiency and response time to TORs [22]. Thus, it will be beneficial for improving the safety of autonomous driving to pay attention to the influence of fatigue on drivers’ takeover efficiency.


### 4.1. Three-Stage Takeover

As shown in Figure2, we divided the takeover process into three stages according to the characteristics of conditionally automated driving: the transition, the control, and the avoidance, in order to study the influence of fatigue on drivers’ takeover efficiency.Figure 2
Three stages of takeover.The transition (requesting to takeover to readying to takeover): the time between when the system needs to takeover and sends a TOR to the driver and when the driver is ready to operate the vehicle is the transition of takeover. As we noted above, at this stage, the driver must take certain actions to takeover the vehicle including adjusting the body [68] and regaining SA [69]. Thus, the primary transition indicator is takeover reaction time, that is, the time needed to complete the takeover transition.The control (readying to takeover to beginning to avoid): in this stage, the driver is ready to takeover the vehicle, putting hands on the steering wheel and feet on the brake pedal to slow the time to collision (TTC) and time to distance (TTD). Thus, the main indicators in the takeover control stage are the standard deviation of lane position, minimum TTC, minimum TTD, and maximum longitudinal acceleration.The avoidance (beginning to avoid to completed avoid): after the driver completely takes over the vehicle, the driver begins to steer the vehicle to avoid obstacles, that is, enters the avoidance stage. At this time, the driver can take measures by slowing down or moving to a safer lane or the road shoulder, and once the avoidance is complete, the driver can return the vehicle to automated driving mode. Indicators such as lateral acceleration, standard deviation of steering angle, and standard deviation of steering angle velocity reflect the takeover efficiency. It is worth mentioning that in some takeover scenarios, such as sensor failure and entering unknown areas [70], the driver does not need to turn the steering wheel for avoidance. In these scenarios, only the transition stage and control stage must be completed.


### 4.2. Quantifying Drivers’ Takeover Behaviour

In Table2, we summarise the takeover efficiency indicators used in extant studies to quantify takeover behaviour divided according to the three stages of the takeover process described above. We next discuss each of the main takeover efficiency index in more detail.Table 2
Takeover efficiency indicators.
StudiesYearTakeover efficiency indicatorsThe transitionThe controlThe avoidance[45]2017Takeover reaction timeStandard deviation of lane positionStandard deviation of steering angleMaximum longitudinal accelerationStandard deviation of steering angle velocity[71]2018Takeover reaction timeStandard deviation of lane positionStandard deviation of steering angleMinimum time to collisionMaximum lateral accelerationMinimum time to distanceMaximum longitudinal acceleration[47]2019Takeover reaction timeMinimum time to collisionStandard deviation of steering angle[23]2019Takeover reaction timeMinimum time to collisionMaximum lateral accelerationMaximum longitudinal acceleration[72]2019Takeover reaction timeMinimum time to collisionMaximum lateral accelerationMaximum longitudinal acceleration[48]2019Takeover reaction timeMaximum longitudinal accelerationMaximum lateral acceleration


#### 4.2.1. Transition

In the transition, when the system sends the TOR, the driver must rapidly respond to prepare to takeover the control of the vehicle. The time between the release of TOR and the time the driver is ready to takeover is called the takeover response time [70]. Drivers who drive conditionally automated vehicles for extended periods of time not only experience some of the fatigue described in Section 2 but often also experience reduced SA, which delays their response to TORs. This initial response time is one of the components of takeover response time along with time to reposition the hands and feet to the steering wheel and brake pedal. Therefore, longer response time to TORs extends overall takeover response time.Although there are many measures of takeover efficiency, takeover response time is the most commonly used. Among these, researchers tend to gauge whether a driver is ready to takeover using steering or breaking thresholds: when the driver receives the request to takeover and prepares to operate the vehicle again, the threshold for determining the start of the driver takeover manoeuvre is 2° steering wheel angle change and 10% free stroke of brake pedal [10, 70, 73–76]. Take-over response time is measured against this because no change in vehicle operating status occurs when the control range is below these thresholds [76].


#### 4.2.2. Control

Maximum longitudinal acceleration: researchers have identified a significant positive correlation between passive fatigue and maximum longitudinal acceleration during control that is now well established. For example, Feldhutter et al. conducted a longitudinal study and found that three-quarters of fatigued drivers had high longitudinal acceleration during takeover, whereas only one-quarter of non-fatigued drivers had this phenomenon [23]. The authors suggested that this occurred because fatigued drivers lack sufficient SA to respond quickly to TORs and therefore need to consciously brake quickly to gain more processing time. In turn, Robert and Hockey [77] identified a cognitive compensatory strategy in which fatigued drivers, aware of their poor mental state, compensated for their lack of SA by braking quickly when a TOR occurs.Minimum TTC: minimum collision time is the critical indicator to evaluate the safety of braking events [78]. For this study, we use Lu et al.‘s [79] definition of min TTC in the control stage: the time when the vehicle maintains its speed after the completion of the control stage until it collides with the obstacle; in essence, the min TTC is jointly determined by the driver’s takeover response time and the vehicle’s maximum longitudinal acceleration. Feldhutter et al. [23] defined collision time as the time gained by the tired driver through rapid braking.The min TTC reflects the driver’s fatigue state. In an investigation of the impact of automated driving duration on takeover efficiency, Bourrelly et al. reported that the TTC of fatigued drivers was shorter than that of normal drivers [71]. In brief, findings suggest that min TTC is an intuitive and effective indicator of the takeover efficiency because longer min TTC represents a higher quality of takeover that is thus more safe.


#### 4.2.3. Avoidance

After controlling the vehicle, the driver must turn the steering wheel to steer the vehicle to avoid obstacles, but Vogelpohl et al. [80] noted that tired drivers tended to brake directly rather than steer. However, Gonçalves et al. [37] found that fatigued drivers had higher maximum lateral acceleration after takeover. This indicates that fatigue affects driver takeover decisions, leading to incomplete longitudinal control of the vehicle in the takeover control stage. Fatigue can also extend takeover reaction times in the takeover transition stage such that there is insufficient time to enter the takeover control stage; in these circumstances, drivers make hasty steering choices to prevent collisions. Therefore, maximum lateral acceleration and the standard deviation of the steering angle can directly reflect the takeover quality at this stage.


## 4.1. Three-Stage Takeover

As shown in Figure2, we divided the takeover process into three stages according to the characteristics of conditionally automated driving: the transition, the control, and the avoidance, in order to study the influence of fatigue on drivers’ takeover efficiency.Figure 2
Three stages of takeover.The transition (requesting to takeover to readying to takeover): the time between when the system needs to takeover and sends a TOR to the driver and when the driver is ready to operate the vehicle is the transition of takeover. As we noted above, at this stage, the driver must take certain actions to takeover the vehicle including adjusting the body [68] and regaining SA [69]. Thus, the primary transition indicator is takeover reaction time, that is, the time needed to complete the takeover transition.The control (readying to takeover to beginning to avoid): in this stage, the driver is ready to takeover the vehicle, putting hands on the steering wheel and feet on the brake pedal to slow the time to collision (TTC) and time to distance (TTD). Thus, the main indicators in the takeover control stage are the standard deviation of lane position, minimum TTC, minimum TTD, and maximum longitudinal acceleration.The avoidance (beginning to avoid to completed avoid): after the driver completely takes over the vehicle, the driver begins to steer the vehicle to avoid obstacles, that is, enters the avoidance stage. At this time, the driver can take measures by slowing down or moving to a safer lane or the road shoulder, and once the avoidance is complete, the driver can return the vehicle to automated driving mode. Indicators such as lateral acceleration, standard deviation of steering angle, and standard deviation of steering angle velocity reflect the takeover efficiency. It is worth mentioning that in some takeover scenarios, such as sensor failure and entering unknown areas [70], the driver does not need to turn the steering wheel for avoidance. In these scenarios, only the transition stage and control stage must be completed.


## 4.2. Quantifying Drivers’ Takeover Behaviour

In Table2, we summarise the takeover efficiency indicators used in extant studies to quantify takeover behaviour divided according to the three stages of the takeover process described above. We next discuss each of the main takeover efficiency index in more detail.Table 2
Takeover efficiency indicators.
StudiesYearTakeover efficiency indicatorsThe transitionThe controlThe avoidance[45]2017Takeover reaction timeStandard deviation of lane positionStandard deviation of steering angleMaximum longitudinal accelerationStandard deviation of steering angle velocity[71]2018Takeover reaction timeStandard deviation of lane positionStandard deviation of steering angleMinimum time to collisionMaximum lateral accelerationMinimum time to distanceMaximum longitudinal acceleration[47]2019Takeover reaction timeMinimum time to collisionStandard deviation of steering angle[23]2019Takeover reaction timeMinimum time to collisionMaximum lateral accelerationMaximum longitudinal acceleration[72]2019Takeover reaction timeMinimum time to collisionMaximum lateral accelerationMaximum longitudinal acceleration[48]2019Takeover reaction timeMaximum longitudinal accelerationMaximum lateral acceleration


### 4.2.1. Transition

In the transition, when the system sends the TOR, the driver must rapidly respond to prepare to takeover the control of the vehicle. The time between the release of TOR and the time the driver is ready to takeover is called the takeover response time [70]. Drivers who drive conditionally automated vehicles for extended periods of time not only experience some of the fatigue described in Section 2 but often also experience reduced SA, which delays their response to TORs. This initial response time is one of the components of takeover response time along with time to reposition the hands and feet to the steering wheel and brake pedal. Therefore, longer response time to TORs extends overall takeover response time.Although there are many measures of takeover efficiency, takeover response time is the most commonly used. Among these, researchers tend to gauge whether a driver is ready to takeover using steering or breaking thresholds: when the driver receives the request to takeover and prepares to operate the vehicle again, the threshold for determining the start of the driver takeover manoeuvre is 2° steering wheel angle change and 10% free stroke of brake pedal [10, 70, 73–76]. Take-over response time is measured against this because no change in vehicle operating status occurs when the control range is below these thresholds [76].


### 4.2.2. Control

Maximum longitudinal acceleration: researchers have identified a significant positive correlation between passive fatigue and maximum longitudinal acceleration during control that is now well established. For example, Feldhutter et al. conducted a longitudinal study and found that three-quarters of fatigued drivers had high longitudinal acceleration during takeover, whereas only one-quarter of non-fatigued drivers had this phenomenon [23]. The authors suggested that this occurred because fatigued drivers lack sufficient SA to respond quickly to TORs and therefore need to consciously brake quickly to gain more processing time. In turn, Robert and Hockey [77] identified a cognitive compensatory strategy in which fatigued drivers, aware of their poor mental state, compensated for their lack of SA by braking quickly when a TOR occurs.Minimum TTC: minimum collision time is the critical indicator to evaluate the safety of braking events [78]. For this study, we use Lu et al.‘s [79] definition of min TTC in the control stage: the time when the vehicle maintains its speed after the completion of the control stage until it collides with the obstacle; in essence, the min TTC is jointly determined by the driver’s takeover response time and the vehicle’s maximum longitudinal acceleration. Feldhutter et al. [23] defined collision time as the time gained by the tired driver through rapid braking.The min TTC reflects the driver’s fatigue state. In an investigation of the impact of automated driving duration on takeover efficiency, Bourrelly et al. reported that the TTC of fatigued drivers was shorter than that of normal drivers [71]. In brief, findings suggest that min TTC is an intuitive and effective indicator of the takeover efficiency because longer min TTC represents a higher quality of takeover that is thus more safe.


### 4.2.3. Avoidance

After controlling the vehicle, the driver must turn the steering wheel to steer the vehicle to avoid obstacles, but Vogelpohl et al. [80] noted that tired drivers tended to brake directly rather than steer. However, Gonçalves et al. [37] found that fatigued drivers had higher maximum lateral acceleration after takeover. This indicates that fatigue affects driver takeover decisions, leading to incomplete longitudinal control of the vehicle in the takeover control stage. Fatigue can also extend takeover reaction times in the takeover transition stage such that there is insufficient time to enter the takeover control stage; in these circumstances, drivers make hasty steering choices to prevent collisions. Therefore, maximum lateral acceleration and the standard deviation of the steering angle can directly reflect the takeover quality at this stage.


## 4.2.1. Transition

In the transition, when the system sends the TOR, the driver must rapidly respond to prepare to takeover the control of the vehicle. The time between the release of TOR and the time the driver is ready to takeover is called the takeover response time [70]. Drivers who drive conditionally automated vehicles for extended periods of time not only experience some of the fatigue described in Section 2 but often also experience reduced SA, which delays their response to TORs. This initial response time is one of the components of takeover response time along with time to reposition the hands and feet to the steering wheel and brake pedal. Therefore, longer response time to TORs extends overall takeover response time.Although there are many measures of takeover efficiency, takeover response time is the most commonly used. Among these, researchers tend to gauge whether a driver is ready to takeover using steering or breaking thresholds: when the driver receives the request to takeover and prepares to operate the vehicle again, the threshold for determining the start of the driver takeover manoeuvre is 2° steering wheel angle change and 10% free stroke of brake pedal [10, 70, 73–76]. Take-over response time is measured against this because no change in vehicle operating status occurs when the control range is below these thresholds [76].


## 4.2.2. Control

Maximum longitudinal acceleration: researchers have identified a significant positive correlation between passive fatigue and maximum longitudinal acceleration during control that is now well established. For example, Feldhutter et al. conducted a longitudinal study and found that three-quarters of fatigued drivers had high longitudinal acceleration during takeover, whereas only one-quarter of non-fatigued drivers had this phenomenon [23]. The authors suggested that this occurred because fatigued drivers lack sufficient SA to respond quickly to TORs and therefore need to consciously brake quickly to gain more processing time. In turn, Robert and Hockey [77] identified a cognitive compensatory strategy in which fatigued drivers, aware of their poor mental state, compensated for their lack of SA by braking quickly when a TOR occurs.Minimum TTC: minimum collision time is the critical indicator to evaluate the safety of braking events [78]. For this study, we use Lu et al.‘s [79] definition of min TTC in the control stage: the time when the vehicle maintains its speed after the completion of the control stage until it collides with the obstacle; in essence, the min TTC is jointly determined by the driver’s takeover response time and the vehicle’s maximum longitudinal acceleration. Feldhutter et al. [23] defined collision time as the time gained by the tired driver through rapid braking.The min TTC reflects the driver’s fatigue state. In an investigation of the impact of automated driving duration on takeover efficiency, Bourrelly et al. reported that the TTC of fatigued drivers was shorter than that of normal drivers [71]. In brief, findings suggest that min TTC is an intuitive and effective indicator of the takeover efficiency because longer min TTC represents a higher quality of takeover that is thus more safe.


## 4.2.3. Avoidance

After controlling the vehicle, the driver must turn the steering wheel to steer the vehicle to avoid obstacles, but Vogelpohl et al. [80] noted that tired drivers tended to brake directly rather than steer. However, Gonçalves et al. [37] found that fatigued drivers had higher maximum lateral acceleration after takeover. This indicates that fatigue affects driver takeover decisions, leading to incomplete longitudinal control of the vehicle in the takeover control stage. Fatigue can also extend takeover reaction times in the takeover transition stage such that there is insufficient time to enter the takeover control stage; in these circumstances, drivers make hasty steering choices to prevent collisions. Therefore, maximum lateral acceleration and the standard deviation of the steering angle can directly reflect the takeover quality at this stage.


## 5. Improving Takeover Efficiency

Ensuring the safety of self-driving vehicles will require improving the takeover efficiency of fatigued drivers. Researchers have studied several ways to improve the efficiency of takeover such as non-driving-related tasks performed by the driver, takeover request modality, and time budgeted for TOR.


### 5.1. Non-Driving-Related Tasks

Because Level 3 autonomous driving allows the driver to disengage from the control of the vehicle in certain traffic conditions, the driver is permitted to perform non-driving-related tasks (NDRTs) [48] and a considerable body of literature has grown around the theme of whether NDRTs influence driving behaviour. In 2019, Jarosch et al. [48] asked drivers to perform two NDRTs, selecting the correct letter and answering a question, and they found that drivers performing NDRT reported less fatigue than drivers performing a single supervision task. In the same year, Feldhütter et al. [72] recruited 42 subjects and conducted experimental testing under two conditions, inducing fatigue and inducing alertness through engagement in voluntary NDRTs, in a 60 min conditionally automated drive. Drivers in the induced fatigue condition showed fatigue after 20–40 min, whereas the drivers who were allowed to perform any NDRT showed a better mental state.Although NDRTs significantly improve drivers’ fatigue states, some scholars have found that NDRTs had negative impacts on takeover performance. In 2017, Eriksson divided 26 subjects into two groups and asked them to read magazines or supervise the automation in Level 3, and one unanticipated finding was that the drivers assigned to the supervision task took 4.46 ± 1.63 s to complete the takeover after receiving the TOR, whereas the drivers assigned to read magazines took 6.06 ± 2.39 s to takeover the vehicle. In a follow-up study in 2018, drivers were asked to perform five different NDRTs, reading, typing, watching videos, playing games, and taking naps, while driving a Level 3 automated vehicle. The results showed significantly shorter response times in the supervision group than in the other groups, and the min TTC after takeover was also significantly higher than that of typing, watching a video, and napping. A possible explanation for these results might be that performing NDRTs distracts the driver, resulting in worse takeover performance. To sum up, performing non-driving-related tasks improves drivers’ fatigue states but distracts them and decreases their takeover efficiency, and whether NDRTs have positive or negative effects on drivers remains controversial.Because these inconsistent findings have been discouraging, further work is needed to determine the roles of NDRTs in driver fatigue and takeover efficiency. However, scholars should not measure NDRTs over long periods of driving time. Rather, NDRTs should be combined with fatigue detection technology and administered when drivers are tired and the surrounding traffic is uncomplicated with the objective of awakening sleepy drivers. However, we suggest that researchers at least consider NDRTs that are not likely to cause distraction that can not only alleviate drivers’ fatigue but also ensure their takeover efficiency, such as listening to specially designed background music [70] or radio with positive emotions [67].


### 5.2. TOR Modality

Among the factors that affect takeover efficiency, the alarm modality of TOR plays an important role in shortening driver response time. In the previous studies, researchers combined auditory, visual, and tactile alarm modalities to conduct their experiments [81–83]. For instance, some researchers have discussed TOR modality in some detail. Brandenburg and Chuang [84] abstracted and concretised TOR alarm modality and presented it to the driver before the takeover; when takeover was required, the abstract pattern only displayed an arrow on the screen to inform the driver that takeover was necessary, and the concrete pattern was a pattern of a hand holding a steering wheel, and the results showed that drivers responded more quickly to the materialised TOR modality. In 2020, Cohen-lazry et al. [76] found that audible TORs might have different effects on takeover efficiency; one of the auditory designs was that after each TOR was issued, the vehicle beeped until the takeover was complete and all threats had passed. The authors found that drivers maintained their attention to the driving threat under this continuous auditory feedback condition, increasing the frequency with which they looked around the driving environment during the takeover.Indeed, researchers have most often combined visual and auditory alarms in TOR alarm design. In 2014, Naujoks found that when a TOR incorporated both visual and auditory signals, the drivers had shorter takeover response times and better lateral control of the vehicle than when the TOR was solely visual. This finding aligned with findings by Politis et al. [85] that a single TOR alarm mode, whether visual, auditory, or vibration based, resulted in longer takeover response times for drivers than did multimodality alarms.Now that it has been firmly established that multimodality TOR designs were more effective at waking up fatigued drivers during takeover and shortened decision-making times, future researchers must determine the influence of multimodality TOR designs on the control and avoidance stages after takeover. For instance, continuous threat feedback based on hearing can be integrated to greatly improve the safety of automated driving takeover.


### 5.3. Time Budgeted for TOR

Time budgeted for TOR (TTB) is an important factor that affects the efficiency of takeover. In 2017, Eriksson and Stanton [86] defined TTB as the time between the release of a TOR and either the driver’s avoidance or the vehicle’s collision with an obstacle, which is the TTC at the time the TOR is sent. At that moment, the vehicle’s automation relies on radar [87] or cameras [88] to detect the surrounding road conditions or to predict the behaviour of other road users [89], and this means that TTB is largely limited by the detection capability of the device and the predictability of the road. In the current state of technology, these systems usually cannot provide sufficient TTB to the driver.Studies have shown that when the TTB is too short, the driver might not have enough time to react, resulting in reduced takeover efficiency [90], whereas sufficient TTB allows for more stable performance. Mok et al. [91] tested driver takeover efficiency at TTBs of 2, 5, and 8 s and found that few drivers could safely pass the road obstacle in 2 s; drivers had better control performance with the longer times and could safely pass the obstacle.For fatigued drivers, short TTB can lead to more hasty takeover behaviours such as the rapid braking behaviour described in Section4.2.2, and hasty takeovers are not necessarily safe ones. During hasty takeovers, drivers do not check the rear view mirror or perform other avoidance behaviours, which can lead to collisions [92]. Moreover, some researchers have found that shorter TTB worsened the driver’s vehicle control after takeover and resulted in shorter TTC, longer maximum lateral or longitudinal acceleration, and greater standard deviations in lane position and steering angle [93].Therefore, for safety concerns, the TTB should be extended for fatigued drivers to provide enough time for them to recover their SA. However, relying on radar or cameras to extend the TTB is insufficient; both of these are limited by the range of detection. Future researchers can use GPS, mobile signalling, and intelligent connected vehicle data to notify drivers earlier and shorten takeover response time.


## 5.1. Non-Driving-Related Tasks

Because Level 3 autonomous driving allows the driver to disengage from the control of the vehicle in certain traffic conditions, the driver is permitted to perform non-driving-related tasks (NDRTs) [48] and a considerable body of literature has grown around the theme of whether NDRTs influence driving behaviour. In 2019, Jarosch et al. [48] asked drivers to perform two NDRTs, selecting the correct letter and answering a question, and they found that drivers performing NDRT reported less fatigue than drivers performing a single supervision task. In the same year, Feldhütter et al. [72] recruited 42 subjects and conducted experimental testing under two conditions, inducing fatigue and inducing alertness through engagement in voluntary NDRTs, in a 60 min conditionally automated drive. Drivers in the induced fatigue condition showed fatigue after 20–40 min, whereas the drivers who were allowed to perform any NDRT showed a better mental state.Although NDRTs significantly improve drivers’ fatigue states, some scholars have found that NDRTs had negative impacts on takeover performance. In 2017, Eriksson divided 26 subjects into two groups and asked them to read magazines or supervise the automation in Level 3, and one unanticipated finding was that the drivers assigned to the supervision task took 4.46 ± 1.63 s to complete the takeover after receiving the TOR, whereas the drivers assigned to read magazines took 6.06 ± 2.39 s to takeover the vehicle. In a follow-up study in 2018, drivers were asked to perform five different NDRTs, reading, typing, watching videos, playing games, and taking naps, while driving a Level 3 automated vehicle. The results showed significantly shorter response times in the supervision group than in the other groups, and the min TTC after takeover was also significantly higher than that of typing, watching a video, and napping. A possible explanation for these results might be that performing NDRTs distracts the driver, resulting in worse takeover performance. To sum up, performing non-driving-related tasks improves drivers’ fatigue states but distracts them and decreases their takeover efficiency, and whether NDRTs have positive or negative effects on drivers remains controversial.Because these inconsistent findings have been discouraging, further work is needed to determine the roles of NDRTs in driver fatigue and takeover efficiency. However, scholars should not measure NDRTs over long periods of driving time. Rather, NDRTs should be combined with fatigue detection technology and administered when drivers are tired and the surrounding traffic is uncomplicated with the objective of awakening sleepy drivers. However, we suggest that researchers at least consider NDRTs that are not likely to cause distraction that can not only alleviate drivers’ fatigue but also ensure their takeover efficiency, such as listening to specially designed background music [70] or radio with positive emotions [67].


## 5.2. TOR Modality

Among the factors that affect takeover efficiency, the alarm modality of TOR plays an important role in shortening driver response time. In the previous studies, researchers combined auditory, visual, and tactile alarm modalities to conduct their experiments [81–83]. For instance, some researchers have discussed TOR modality in some detail. Brandenburg and Chuang [84] abstracted and concretised TOR alarm modality and presented it to the driver before the takeover; when takeover was required, the abstract pattern only displayed an arrow on the screen to inform the driver that takeover was necessary, and the concrete pattern was a pattern of a hand holding a steering wheel, and the results showed that drivers responded more quickly to the materialised TOR modality. In 2020, Cohen-lazry et al. [76] found that audible TORs might have different effects on takeover efficiency; one of the auditory designs was that after each TOR was issued, the vehicle beeped until the takeover was complete and all threats had passed. The authors found that drivers maintained their attention to the driving threat under this continuous auditory feedback condition, increasing the frequency with which they looked around the driving environment during the takeover.Indeed, researchers have most often combined visual and auditory alarms in TOR alarm design. In 2014, Naujoks found that when a TOR incorporated both visual and auditory signals, the drivers had shorter takeover response times and better lateral control of the vehicle than when the TOR was solely visual. This finding aligned with findings by Politis et al. [85] that a single TOR alarm mode, whether visual, auditory, or vibration based, resulted in longer takeover response times for drivers than did multimodality alarms.Now that it has been firmly established that multimodality TOR designs were more effective at waking up fatigued drivers during takeover and shortened decision-making times, future researchers must determine the influence of multimodality TOR designs on the control and avoidance stages after takeover. For instance, continuous threat feedback based on hearing can be integrated to greatly improve the safety of automated driving takeover.


## 5.3. Time Budgeted for TOR

Time budgeted for TOR (TTB) is an important factor that affects the efficiency of takeover. In 2017, Eriksson and Stanton [86] defined TTB as the time between the release of a TOR and either the driver’s avoidance or the vehicle’s collision with an obstacle, which is the TTC at the time the TOR is sent. At that moment, the vehicle’s automation relies on radar [87] or cameras [88] to detect the surrounding road conditions or to predict the behaviour of other road users [89], and this means that TTB is largely limited by the detection capability of the device and the predictability of the road. In the current state of technology, these systems usually cannot provide sufficient TTB to the driver.Studies have shown that when the TTB is too short, the driver might not have enough time to react, resulting in reduced takeover efficiency [90], whereas sufficient TTB allows for more stable performance. Mok et al. [91] tested driver takeover efficiency at TTBs of 2, 5, and 8 s and found that few drivers could safely pass the road obstacle in 2 s; drivers had better control performance with the longer times and could safely pass the obstacle.For fatigued drivers, short TTB can lead to more hasty takeover behaviours such as the rapid braking behaviour described in Section4.2.2, and hasty takeovers are not necessarily safe ones. During hasty takeovers, drivers do not check the rear view mirror or perform other avoidance behaviours, which can lead to collisions [92]. Moreover, some researchers have found that shorter TTB worsened the driver’s vehicle control after takeover and resulted in shorter TTC, longer maximum lateral or longitudinal acceleration, and greater standard deviations in lane position and steering angle [93].Therefore, for safety concerns, the TTB should be extended for fatigued drivers to provide enough time for them to recover their SA. However, relying on radar or cameras to extend the TTB is insufficient; both of these are limited by the range of detection. Future researchers can use GPS, mobile signalling, and intelligent connected vehicle data to notify drivers earlier and shorten takeover response time.


## 6. Discussion

Developments in science and technology have allowed for robust research results on driver fatigue in automated driving. For instance, previous researchers have established that driving Level 3 automated vehicles for long periods of time induces passive fatigue in drivers, and scholars have developed many methods to measure fatigue in automated vehicle drivers. For this study, we systematically reviewed some representative studies in this field and discussed the advantages, disadvantages, and feasibility of different assessment methods. We found that researchers prefer to use eye movement indicators to evaluate automated vehicle drivers’ fatigue, possibly because eye movement data are easier to collect and less intrusive. We think that another and more important reason is that eye movement devices usually capture drivers’ eye movement and fixation areas, which in their turn reflect a driver’s mind wandering, which is highly related to driver fatigue levels [38]. However, the effectiveness of eye movement indicators is greatly influenced by the environment, so some researchers combine eye movement with other indicators such as subjective fatigue scale scores and facial movements to improve the accuracy of fatigue assessment.The difference between conditionally and fully automatic driving is that the former requires the driver to be able to takeover the control of the vehicle in an emergency, so that takeover efficiency reflects the safety of conditionally automated vehicles. We divided the driver takeover process into three stages and quantified driver takeover behaviours; we then discussed the influences of fatigue on takeover stability. We finally summarised the indicators for evaluating takeover efficiency. We found that takeover reaction time, maximum longitudinal acceleration, and minimum collision time are the most commonly used indicators in the current research; we also discussed the relationships between these indicators and driver fatigue.Additionally, given that driver fatigue results in poor driver takeover, we proposed in this paper three potential methods for improving the takeover efficiency of tired drivers: NDRTs performed by the driver, takeover request modality, and time budgeted for TOR. However, there are still many unanswered questions about these methods. We thus suggest further study with more focus on these controversial areas and the gaps in the knowledge on improving takeover efficiency in fatigued drivers.A limitation of this study is that we did not discuss the influence of active fatigue on drivers in Level 3 automated driving. Complex traffic and mental overload cause active fatigue in drivers, which Gold et al. [74] established; they found that in complex traffic conditions, drivers had longer takeover response times and shorter minimum collision times. In China, where the traffic flow is mainly mixed, the traffic density is relatively high, the complicated traffic environment forces drivers to stay alert at all times, and drivers have more time to pay attention. In such a complex and changeable traffic environment, drivers must maintain high vigilance over long periods. This circumstance is likely to lead to active fatigue, which also reduces takeover quality.Another issue that we did not address was whether driver trust has an effect on driving fatigue, a crucial safety factor. Both lack of trust in and overreliance on automation can reduce driver takeover performance [94]. Some researchers have shown that drivers’ increased trust in the Level 3 automation increases takeover response time [95, 96] and decreases handling stability after takeover (represented by lower min TTC) [96]; it also increases the maximum standard deviation of lane position [97]. By contrast, Gold et al. [98] believed that driving safety increased with the greater subjective trust of drivers, and they found that drivers with high trust showed lower accident incidence. These results indicate that even following both subjective and objective experimental findings, some controversies remain about driver trust in automated vehicles and how trust affects driving safety; surprisingly few researchers have linked trust with driving fatigue. Trust can also affect other psychological factors during driving automated vehicles [99]. For example, drivers with little trust in automation might be more nervous and force themselves to stay awake while the vehicle is operating. This could be an important and interesting issue for future research.



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*Source: 8632685-2021-10-22.xml*