Takeover Performance Research Articles

Regular looks out the window do not maintain situation awareness in highly automated driving

The loss of situation awareness (SA) caused by the out-of-the-loop phenomenon is a major problem for successful take-overs after highly automated driving. Despite recent advances in driving automation, a human backup for the automated system is currently indispensable. For uneventful situations, the automation works and drivers can perform engaging non-driving-related tasks (NDRTs). However, they need to be ready to respond at all times in case of a take-over request (TOR). For smooth and safe transition from automated to manual control, SA has to be re-established within seconds. We investigated whether regular out-the-window looks can facilitate this process. This driving simulation study is the first to investigate whether such situation awareness maintenance can improve take-over performance after a TOR. In a repeated-measures design, 89 participants experienced 5-minutes of highly automated driving with a NDRT followed by a critical take-over situation. Every 30 s, participants were asked either to monitor the traffic scene for 3 s (monitoring condition) or to merely interrupt the NDRT for 3 s (control condition). We ensured that incidental traffic monitoring was impossible during the NDRT. In the control condition, participants saw a blank screen during the 3-seconds intervals. Contrary to common intuition, the results show neither improvements in objective take-over performance nor in perceived SA, driver availability, criticality, stress, risk assessment, or adequacy of the TOR. We conclude that brief interruptions of the NDRT to look at the ongoing traffic are unable to maintain the driver's SA during highly automated driving. Future studies should therefore focus on other measures to optimize the recovery of SA.

2단계 자율주행 중 시각 및 청각 이차 과제 수행이 제어권 전환 능력에 미치는 영향

2단계 자율주행 중 시각 및 청각 이차 과제 수행이 제어권 전환 능력에 미치는 영향

The Effect of AR-HUD Takeover Assistance Types on Driver Situation Awareness in Highly Automated Driving: A 360-Degree Panorama Experiment

Human-machine co-driving presents a significant hurdle in automated driving system. The takeover process in automated driving system involves complex human factors, failure to takeover the vehicle and control driving behavior during the takeover process may lead to severe traffic safety hazards. An augmented reality head-up display (AR-HUD) takeover assistance information can provide real-time assistance information to the driving environment, enhancing drivers’ situation awareness (SA) and takeover decisions in highly automated driving system. This study investigated the impact of different AR-HUD types of takeover assistance information display. Three AR-HUD types, corresponding to the three pre-takeover behavioral processes (perception, understanding, and prediction), were evaluated: PSR (assistance in perceiving the source of risk), AS (assistance in analyzing situations), and MD (assistance in making decisions). The baseline (without assistance information) was used as the control group. In a driving simulation experiment using 360° panoramic video, seventy-nine participants performed SA assessment and visual tracking tasks. Questionnaire and eye-tracking data indicated that the type of AR-HUD displayed positively influenced drivers’ SA and takeover decisions, with AS being the most effective in enhancing SA and improving takeover performance. Additionally, this study compared the differences between the three types of AR-HUD and the baseline under two takeover request lead times (TORlt) of 5 seconds and 7 seconds. It was found that drivers’ SA was lower when TORlt was shorter (with the corresponding AR-HUD display also being shorter). This study provides insight concerning the impact of various types of AR-HUD takeover assistance information display and TORlt on driving safety. The findings support the further optimization of AR-HUD takeover assistance information design.

How does drivers’ trust in vehicle automation affect non-driving-related task engagement, vigilance, and initiative takeover performance after experiencing system failure?

Vehicle automation is a potentially highly advantageous concept in terms of reducing traffic accidents, e.g., due to distractions by non-driving related tasks (NDRTs) or decreased vigilance. The driver needs to have a thorough understanding of the vehicle automation system and a moderate level of trust in it for the advantages to be realized. System failures inherently adversely affect trust, however, a completely reliable system is unrealistic. In the present study, we explore how drivers’ trust in vehicle automation affects their NDRT engagement, vigilance, and initiative takeover performance after experiencing a system failure. In the induction experiment, seventy-seven participants were divided into three groups by their level of trust (distrust, appropriate trust, excessive trust) in the highly automated vehicle. All experienced five “hazardous events” in the formal experiment, the second of which was a system failure. Relevant results indicated that the system failure affected drivers differently depending on their levels of trust. Drivers’ NDRT engagement decreased after the system failure due to a decline in trust but increased during subsequent driving as trust recovered. Vigilance was higher in drivers who were distrustful, but the post-failure decrease in vigilance was higher compared to others. Drivers who were distrustful were more likely to initiatively take over the vehicle as evidenced by higher resulting acceleration upon encountering the hazards. The maximum resulting acceleration decreased with the sequence of hazardous events but increased with the level of risks. The findings of this work may be useful in terms of trust calibration processes, technological improvements to vehicle automation systems, and the promotion of automated vehicle usage.

Unlocking Safer Driving: How Answering Questions Help Takeovers in Partially Automated Driving

As vehicle automation increases, drivers' roles shift from active operation to passive monitoring, making it challenging for drivers to effectively take over when automation fails. While a growing body of research focuses on providing assistance to improve driver monitoring behavior and takeover performance, these approaches have been mostly linked to the takeover event. The current study proposes to facilitate drivers' awareness during automated driving by adopting strategies from natural conversations, specifically, asking driver questions while monitoring the vehicle. In this simulated driving study, we investigated how different types of questions affect monitoring behavior and takeover performance with or without takeover notifications. Our preliminary results suggest that the driving-related questions could improve drivers’ takeover performance, especially when the takeover notification is absent.

Predicting Automated Vehicle Takeover Decision During the Nighttime

Conditionally automated vehicles require drivers to take over control occasionally. To date, takeover performance has been mostly evaluated using only re-engagement time and quality metrics. However, the appropriateness of takeover decisions, which has not been considered by previous research, should also be included as a performance indicator as it reflects one’s situation awareness of the takeover scenario. The goal of this study was to use eye-tracking, demographic factors, workload, and non-driving-related task (NDRT) conditions to predict takeover decisions. Forty-three participants drove a simulated conditionally automated vehicle while performing visual NDRTs and needed to decide the most appropriate maneuver around a roadway obstacle. Six classifiers were used to predict takeover decisions. The Random Forest model achieved the best performance, and driving experience and perceived workload were the most influential features. Findings may be used to assist in the design of adaptive algorithms that support drivers taking over from automated vehicles.

Effects of Traffic Conditions on Driver Takeover Performance and Situational Awareness

This study aimed to investigate the impacts of oncoming traffic density, surrounding vehicle configuration, and driving lane on drivers' takeover performance and situational awareness in conditionally automated driving. We conducted a driving simulator experiment with 28 participants. Our results showed that high oncoming traffic led to shorter takeover response time and lower situational awareness. Drivers tended to brake more aggressively and had lower collision risks when the traffic was on the front side, and drivers were more likely to ignore traffic from behind and behind side when taking over. Taking over in the right lane on a two-way road led to lower collision risks and smoother driving compared to the left lane. Our results have valuable implications for the design and development of driver monitoring and support systems to improve the safety of conditionally automated vehicles.

Tactile Displays: The Effects of Location and Intensity on Automated Vehicle Takeover Performance

This study investigated the effects of tactile display location and intensity on automated vehicle takeover performance. Twenty participants completed simulated driving tasks in a Level 3 automated vehicle and responded to takeover requests presented at different locations (seat back, seat pan, seat belt, and wrist) with different intensity levels. Results showed that higher intensity tactile displays were associated with faster takeover times, and placing the display on the wrist was correlated with shorter takeover times compared to the seat pan in lower intensity. However, there were no significant effects on maximum resulting jerk. These findings suggest that incorporating tactile displays in automated vehicles as takeover requests is promising, particularly when placed on the wrist and presented at higher intensities. This research contributes to the development of human-machine interfaces that can better assist drivers during takeovers and improve the safety of automated vehicles.

Is driving experience all that matters? Drivers’ takeover performance in conditionally automated driving

Introduction: In conditionally automated driving, drivers are allowed to engage in non-driving related tasks (NDRTs) and are occasionally requested to take over vehicle control in situations that the automation system cannot handle. Drivers may not be able to adequately perform such requests if they have limited driving experience. This study investigates the influence of driving experience on takeover performance in conditionally automated driving. Method: Nineteen subjects participated in this driving simulator study. The NDRTs consisted of three tasks: writing business emails (working condition), watching videos (entertaining condition), and taking a break with eyes closed (resting condition). These three NDRTs require drivers to invest high, moderate, and low levels of mental workload, respectively. The duration of engagement in each NDRT before a takeover request (TOR) was either 5 minutes (short interval) or 30 minutes (long interval). Results: Drivers’ driving experience and performance during the control period are highly correlated with their TOR performance. Furthermore, the type and duration of NDRT influence TOR performance, and inexperienced drivers exhibit poorer TOR performance than experienced drivers. Conclusions and Practical Applications: These findings have relevance for the types of NDRTs that ought to be permitted during automated driving, the design of automated driving systems, and the formulation of regulations regarding the responsible use of automated vehicles.

Measuring takeover performance in different driving scenarios: Considering the influence of non-driving-related tasks and takeover request lead time

Conditional automation systems (SAE Level 3) allow the driver to perform a secondary task, but the driver must regain control of the vehicle after the takeover request (ToR). Therefore, it is important to understand the factors that influence the driver’s takeover performance. Previous studies have analyzed the variables including ToR modalities, ToR lead time, non-driving-related (NDR) task, driving experience and age. The influence of driving scenario on driver takeover performance and whether this influence varies the effect of the ToR time and NDR task remained unanswered. This study designed a 2(ToR time) × 2(NDR task) × 4 (driving scenario) factorial measure to investigate the effect of these factors on automated vehicle takeover performance. Forty-two participants drove in a simulated SAE Level 3 vehicle and performed a series of takeover tasks. The results showed that the ToR time, NDR task, driving scenario and ToR time × driving scenario had a significant effect on takeover behavior. In particular, the secondary work state task was correlated with the increased takeover and control time. The complexity of the driving scenario indicated a marginally significant influence on the quality of the takeover. A shorter takeover time and a smaller lateral offset were associated with a lower urgency of the driving scenario. The ToR time for the fog-cluster scenario showed an opposite effect to the takeover performance compared to the other three scenarios. Findings from this study can provide implications on the effect of ToR time desired and enabled by different scenarios.

To Inform or to Instruct? An Evaluation of Meaningful Vibrotactile Patterns to Support Automated Vehicle Takeover Performance

Automated vehicles may occasionally require drivers to take over. The complexity of the takeover process warrants the design of effective human–machine interfaces that assist drivers in regaining control, especially when the visual and auditory sensory modalities are occupied. Vibrotactile displays, which can represent information about the status, direction, and position of driving environment elements, have been suggested as one promising approach, but their effectiveness to aid in takeover transitions has not been fully evaluated. This study investigated the effects of meaningful tactile signal patterns, used as takeover requests, on automated vehicle takeover performance. Forty participants rode in a simulated SAE Level 3 automated vehicle and completed a series of takeover tasks with two tactile pattern formats, i.e., informative (which displayed status information of surrounding vehicles) and instructional (that displayed the appropriate takeover maneuver), and three in-vehicle locations (seat back, seat pan, and a seat back and seat pan combination). Takeover response options included lane changes only or brake applications followed by changing lanes, depending on the locations of surrounding vehicles. Results indicate that only meaningful instructional tactile signals, in either the seat back or seat pan, were associated with worse takeover response time and maximum resulting acceleration compared to signals without any patterns. Additionally, tactile information presented on the seat back was perceived as the most useful and satisfying. Findings from this study can inform the development of next-generation human–machine interfaces that utilize tactile stimulation in a wide range of environments with automation.

The effect of two-stage warning system on human performance along with different takeover strategies

Warning stages and takeover strategies are two key factors that affect the takeover process when highly automated systems fail. However, the interaction between these two factors remains unclear. This study empirically investigated the effect of warning types (two-stage warning vs. single-stage warning) and takeover strategies (interruption vs. multitasking) on human performance. Thirty-eight participants performed three tasks of the Multi-Attribute Task Battery (MATB), in which the system monitoring task was automated and adopted as the takeover task, while tracking and resource management tasks were employed as the initial tasks simultaneously. The results showed that compared with the single-stage warning system, the two-stage warning system enhanced situation awareness, reduced mental workload and improved takeover performance. However, tracking task performance declined when the two-stage warning system was used. Moreover, participants using the two-stage warning system reacted to gauge malfunctions much more quickly than participants using the single-stage warning system when the interruption strategy was adopted. However, there was no significant difference in tracking task performance between the two warning systems when the multitasking strategy was adopted. This study concluded that the two-stage warning system can benefit the takeover process but at the cost of the performance of initial tasks, and the takeover strategy plays an important role in realizing the benefits of the two-stage warning system and alleviating its costs. These findings could provide useful insights for automated system design and personnel training.

The influence of a color themed HMI on trust and take-over performance in automated vehicles.

SAE Level 3 is known as conditional driving automation. As long as certain conditions are met, there is no need to supervise the technology and the driver can engage in non-driving related tasks (NDRTs). However, a human driver must be present and alert to take over when the automation is facing its system limits. When such an emergency takes place, the automation uses the human machine interface (HMI) to send a take-over request (TOR) to the driver. We investigated the influence of a color themed HMI on the trust and take-over performance in automated vehicles. Using a driving simulator, we tested 45 participants divided in three groups with a baseline auditory HMI and two advanced color themed HMIs consisting of a display and ambient lighting with the colors red and blue. Trust in automation was assessed using questionnaires while take-over performance was assessed through response time and success rate. Compared to the baseline HMI, the color themed HMI is more trustworthy, and participants understood their driving tasks better. Results show that the color themed HMI is perceived as more pleasant compared to the baseline HMI and leads to shorter reaction times. Red ambient lighting is seen as more urging than blue, but HMI color did not significantly affect the general HMI perception and TOR performance. Further research can explore the use of color and other modalities to express varying urgency levels and validate findings in complex on road driving conditions.

Playing Games Guiding Attention Improves Situation Awareness and Takeover Quality during Automated Driving

During SAE L3 automated driving, drivers engage in non-driving related tasks (NTRDs), but NTRDs impair situation awareness (SA) and takeover quality. This study aimed to find an NTRD that is interesting but has a minimal negative impact on SA. Given the strong impact of games on cognition, we studied how games affected SA and takeover performance. In the game survey, we investigated the impact of four type of games on driving SA. The results showed that there were some games positively affected SA (situational game). In the formal experiment, we used a realistic simulated driving environment to investigate the effect of games on SA and takeover performance. A 3 (task type: situational game/non-situational game/monitoring) × 4 (attention duration, time between monitoring requests and takeover requests: 4 s/6 s/8 s/10 s) mixed design was used. Results showed that drivers who engaged in the situational game had higher SA, better takeover performance, and required less attention duration than drivers who engaged in the non-situational game. Both groups showed faster takeover time than the monitoring group, indicating higher arousal. Our research results indicate that playing situational games can improve SA and takeover quality in automated driving.

Transitioning to multi-dimensional estimation of visual distraction and its safety effects under automated driving: A spatiotemporal and directional estimation approach

Traditional methodologies for measuring visual distraction have been limiting in their approaches, treating distraction as a one-dimensional variable. This has been accomplished either by categorizing distraction as a binary variable or using surrogate measurements, such as reaction time in response to non-driving related tasks, which follow the procedures of measuring distraction from psychology. Furthermore, as human-vehicle interaction (HVI) under automated driving has the potential to provide safety information and bring visual distraction simultaneously, there lacks an investigation on the quantitative relationship between distraction and safety due to the restrained methodologies in measuring visual distraction. As HVI-induced driver distraction plays a critical role in determining safe driving under automated driving, a methodology is highly needed to comprehensively measure the visual distraction under automated driving so that its impact on safety can be further investigated. Therefore, sticking to the definition of visual distraction, this research aims to (1) improve the existing methodology in measuring HVI-induced driver distraction under automated driving by focusing on visual distraction and mathematically describing it from spatiotemporal and directional dimensions; (2) theoretically investigate how the quantified visual distraction influences driving safety and (3) quantify the relationship between distraction and safety with introducing the “distraction-safety” ratio. Drivers’ fixation behaviors are used in quantifying visual distraction, which is measured by spatiotemporal and directional relationships between drivers’ visual attention and the attention that indicates “zero distraction”. Three newly added performance measures in quantifying HVI-induced visual distraction are real-time magnitudes, real-time directions, and intensities (cumulation of magnitudes over time). To validate the proposed methods, a verification study was conducted by recruiting drivers to test automated driving under Level 3 automation. Drivers are required to wear an eye-tracker and go through two scenarios interacting with jaywalkers where takeover actions are needed with two takeover warnings (“visual-only” and “visual & audible”). Per past studies, takeover time was measured in representing distraction level. As a result, this study confirms the validity of the proposed methods by revealing the significant and positive correlations between the measured distraction intensity and the takeover time. Discussions of the quantified visual distraction from the spatiotemporal and directional perspective further enhance the understanding of HVI-induced driver distraction under automated driving through multiple dimensions. Furthermore, this research reveals how distracted driving affects safety under automated driving through the takeover performance. Thresholds under visual distraction magnitudes and degrees that lead to traffic conflicts were identified. More importantly, a “distraction-safety” ratio that quantifies the relationship between visual distraction and safety benefits is proposed. The results suggest that the “visual & audible” is more effective in significantly enhancing safety while aggregating a significantly smaller amount of visual distraction. The contribution of this research is to re-define the methodological approach of measuring visual distraction by (1) measuring distraction multi-dimensionally and (2) establishing a “distraction-safety” system in quantitatively assessing the balance of safety benefits and the HVI-induced visual distraction magnitude under automated driving environment.

Where drivers are looking at during takeover: Implications for safe takeovers during conditionally automated driving

Objective Safety has become one of the primary concerns of level 3 automated driving, especially during the takeover process. Since most studies have focused on impacts of various factors on takeover performance of drivers, there seems to be a gap between the causes of crashes and the desired means to mitigate their occurrence and consequences. Hence, the main objective of this study is to extract from crash data during takeovers drivers’ patterns of gaze behaviors and maneuvers and then utilize them to extract some guidance on human-machine-interface design to enhance safety and acceptability of automated driving. Methods A study involving 27 subjects was conducted on a high-fidelity driving simulator with a Steward motion platform of six degrees of freedom. Each subject participated in 6 takeover scenarios with a lead time of 5 s and different duration of monitoring (DoM), with their maneuvers recorded by the system and eye gazes recorded by the Smart Eye Pro and Smart Recorder. Crash data collected during the takeover process were then utilized for the analysis. Results From 132 valid takeovers collected from 23 out of the 27 participants, 15 crashes were recorded. Based on which, five typical patterns of unsafe behaviors were recognized that may have caused the crashes, denoted as Type I to Type V, respectively. Besides, it appears that even if drivers were given more time to observe the surroundings, i.e., longer DoM, the number of crashes has not decreased as anticipated. Therefore, what is more important seems to be drivers’ gaze behaviors and maneuvers shortly after TOR. Conclusions For takeovers to be safe, good cooperations between drivers’ gaze behaviors and maneuvers are essential. Overall, it seems that in emergent situations that require takeovers, some drivers have difficulty in allocating attentions reasonably, which appears to have less to do with the time left for drivers to observe the surroundings. While designing HMIs, we may as well consider providing enough information to guide drivers according to drivers’ states and maneuvers at the time to improve safety of takeovers in emergent situations, and more importantly, to provide the information timely and effectively.

Investigating effects of multimodal explanations using multiple In-vehicle displays for takeover request in conditionally automated driving

In conditionally automated driving, a human takeover transition is necessary in response to vehicle malfunction or other limitation. To mitigate the negative experience of this technological limitation, researchers have suggested providing an explanation along with the takeover request to increase drivers’ understanding and trust in an automated driving system (ADS). However, previous studies on the explanation have been limited to investigations of unimodal explanations that use either text or speech, while neglecting other various in-vehicle display options. The present study aims to investigate the effects of multimodal explanation using three in-vehicle displays—ambient light, cluster, and sound. The explanation given by each in-vehicle display was systemically manipulated into two levels (ambient light: constant color vs. variable color, cluster: no-text and no-background vs. text with colored background, sound: alert-only sound vs. speech with alert sound). To examine the effects of the explanation using these displays, we conducted mixed method research including a scenario-based experiment, questionnaires, and a follow-up interview for thematic analysis. In the experiment, eighty-two participants drove in a driving simulator and performed takeover tasks in several types of urgent situations for measuring their takeover time and error. The participants then responded to questionnaires on takeover workload and overall trust in automated vehicle. Next, fifty-six of them participated in the follow-up interview for discovering significant themes related to their experiences with the multimodal explanation. As results, we found that multimodal explanations that use two types of displays were more effective than unimodal explanations or multimodal explanations using three types of displays. Regarding the effects of each type of display, the speech-based explanation showed the best takeover performance in general. The explanation using the cluster was next most useful in performance. The ambient light explanation showed less significant to no effect in the takeover situation. We expect that these findings can contribute to theoretical understanding of driver response to multimodal explanations and to practical design of trustworthy explanations in various takeover situations.

How Resource Demands of Nondriving-Related Tasks and Engagement Time Affect Drivers’ Physiological Response and Takeover Performance in Conditional Automated Driving

Drivers are allowed to perform a nondriving-related task (NDRT) in the Level-3 (L3) automated driving, which inevitably promotes a variety of complex NDRTs. Hence, investigating the effect of NDRTs on drivers’ behavior is necessary to ensure a safe transition from automated to manual driving. The main aim of this study is to examine the effect of resource demands of NDRTs and its engagement time on drivers’ physiological responses, takeover performance, and subjective ratings. A total of 42 participants were recruited to conduct a driving simulator study. Results showed that visual and physical resource demands significantly increased participants’ response time and deteriorated their takeover quality. Engagement time significantly affected participants’ drowsiness and response time to the takeover request. In addition, participants rated the physical–visual–cognitive task as the most challenging task and the cognitive task as the least demanding task, which matches the takeover performance. Moreover, pupil diameter responds significantly to visual resource demands rather than physical resource demands. Physical resource demands had a significant tendency to elevate heart rate. This study suggested that tasks with a certain level of cognitive resource demand may be more appropriate for L3 automated driving. The findings of this study make it possible to use physiological responses to identify NDRT types or even predict drivers’ takeover performance in automated driving. Based on this, the time budget could be dynamically adjusted to ensure a safe transition.

The impact of non-driving related tasks on the development of driver sleepiness and takeover performances in prolonged automated driving

Introduction: Vehicle automation is thought to improve road safety since numerous accidents are caused by human error. However, the lack of active involvement and monotonous driving environments due to automation may contribute to drivers' passive fatigue and sleepiness. Previous research indicated that non-driving related tasks (NDRTs) were beneficial in maintaining drivers’ arousal levels but detrimental to takeover performance. Method: A 3·2 mixed design (between subjects: driving condition; within subjects: takeover orders) simulator experiment was conducted to explore the development of driver sleepiness in prolonged automated driving context and the effect of NDRTs on driver sleepiness development, and to further evaluate the impact of driver sleepiness and NDRTs on takeover performance. Sixty-three participants were randomly assigned to three driving conditions, each lasting 60 min: automated driving while performing driving environment monitoring task; visual NDRTs task; and visual NDRTs with scheduled driving environment monitoring task. Two hazardous events occurring at about the 5th and 55th min needed to be handled during the respective driving. Results: Drivers performing monitoring tasks had a faster development of driver sleepiness than drivers in the other two conditions in terms of both subjective and objective indicators. Takeover performance of drivers performing monitoring task were undermined due to driver sleepiness in terms of braking and steering reaction times, the time between saccade latency and braking or steering reaction times, and so forth. Additionally, NDRTs impaired the drivers’ takeover ability in terms of saccade latency, max braking pedal input, max steering velocity, minimum time to collision, and so forth. This study shows that NDRTs with scheduled road environment monitoring task improve takeover performance during prolonged automated driving by helping to maintain driver alertness. Practical Applications: Findings from this work provide some technical assistance in the development of driver sleepiness monitoring systems for conditionally automated vehicles.

Study on the influence factors of takeover behavior in automated driving based on survival analysis

The driver’s takeover behavior is critical to safety during automated driving in fog zones. This paper aims to explore the influence mechanism of takeover behavior, this paper analyzes the influence of various factors on takeover behavior from the perspective of event duration, and the influence mechanism of takeover behavior can be promoted. A takeover behavior test platform of L3 automated driving technology was developed based on a driving simulator, and a high-fidelity driving simulation experiment was carried out with the fog zone selected as the takeover scenario. The takeover request time (TOR) was set as 5 s and 10 s, and the no-driving-related task (NDRT) was set as work tasks and entertainment tasks. 42 drivers were invited to participate in the experiment where takeover behavior data were obtained. Takeover response time and takeover correct time are representative indicators of takeover behavior, which are extracted from takeover response and takeover performance. The survival analysis (the Kaplan-Meier method and Cox proportional-hazards method) was used to explore the effects of factors (gender, age, driving age, TOR, NDRT) on survival time (takeover response time, takeover correct time). The KM model results show that driver attribute factors (gender, age, and driving age) had significant difference on takeover response time. Females were more cautious about the takeover, and their response time is shorter than that of males. The takeover response time increased with age and driving age, with statistical difference. Only TOR had a statistical difference in the takeover correct time, while other factors had no statistical significance. The takeover correct time of TOR-10 s (median = 8.50 s) is higher than the TOR-5 s (median = 3.65 s). However, the mean value of takeover correct time corresponding to 5 s is higher than 10 s. The Cox model results are consistent with the KM model, and the model results show that external variables (TOR, NDRT) have significant influence on the takeover correct time. The work task requires more immersion, and the longer time it takes for drivers to recover the situational awareness of driving, which leads to an increase in the takeover correct time. Based on the survival analysis method, this research studies the influence of factors on takeover behavior from the perspective of the duration of takeover events, which can lay a foundation for in-depth study on the influence mechanism about takeover behavior. The study can also provide references for the improvement of drivers’ takeover behavior, and the optimization design of the takeover warning system.