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OBJECTIVE: Vehicle automation technologies have the potential to address the mobility needs of older adults. However, age-related cognitive declines may pose new challenges for older drivers when they are required to take back or "takeover" control of their automated vehicle. This study aims to explore the impact of age on takeover performance under partially automated driving conditions and the interaction effect between age and voluntary non-driving-related tasks (NDRTs) on takeover performance. METHOD: A total of 42 older drivers (M = 65.5 years, SD = 4.4) and 40 younger drivers (M = 37.2 years, SD = 4.5) participated in this mixed-design driving simulation experiment (between subjects: age [older drivers vs. younger drivers] and NDRT engagement [road monitoring vs. voluntary NDRTs]; within subjects: hazardous event occurrence time [7.5th min vs. 38.5th min]). RESULTS: Older drivers exhibited poorer visual exploration performance (i.e., longer fixation point duration and smaller saccade amplitude), lower use of advanced driving assistance systems (ADAS; e.g., lower percentage of time adaptive cruise control activated [ACCA]) and poorer takeover performance (e.g., longer takeover time, larger maximum resulting acceleration, and larger standard deviation of lane position) compared to younger drivers. Furthermore, older drivers were less likely to experience driving drowsiness (e.g., lower percentage of time the eyes are fully closed and Karolinska Sleepiness Scale levels); however, this advantage did not compensate for the differences in takeover performance with younger drivers. Older drivers had lower NDRT engagement (i.e., lower percentage of fixation time on NDRTs), and NDRTs did not significantly affect their drowsiness but impaired takeover performance (e.g., higher collision rate, longer takeover time, and larger maximum resulting acceleration). CONCLUSIONS: These findings indicate the necessity of addressing the impaired takeover performance due to cognitive decline in older drivers and discourage them from engaging in inappropriate NDRTs, thereby reducing their crash risk during automated driving.
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Automatización , Conducción de Automóvil , Humanos , Conducción de Automóvil/psicología , Adulto , Masculino , Anciano , Femenino , Factores de Edad , Persona de Mediana Edad , Simulación por Computador , Desempeño Psicomotor , Adulto JovenRESUMEN
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.
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Conducción de Automóvil , Humanos , Automatización , Tiempo de ReacciónRESUMEN
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 60â¯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.
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Fatiga , Somnolencia , Humanos , Automatización , Tiempo de ReacciónRESUMEN
In conditionally automated driving, the driver is free to disengage from controlling the vehicle, but they are expected to resume driving in response to certain situations or events that the system is not equipped to respond to. As the level of vehicle automation increases, drivers often engage in non-driving-related tasks (NDRTs), defined as any secondary task unrelated to the primary task of driving. This engagement can have a detrimental effect on the driver's situation awareness and attentional resources. NDRTs with resource demands that overlap with the driving task, such as visual or manual tasks, may be particularly deleterious. Therefore, monitoring the driver's state is an important safety feature for conditionally automated vehicles, and physiological measures constitute a promising means of doing this. The present systematic review and meta-analysis synthesises findings from 32 studies concerning the effect of NDRTs on drivers' physiological responses, in addition to the effect of NDRTs with a visual or a manual modality. Evidence was found that NDRT engagement led to higher physiological arousal, indicated by increased heart rate, electrodermal activity and a decrease in heart rate variability. There was mixed evidence for an effect of both visual and manual NDRT modalities on all physiological measures. Understanding the relationship between task performance and arousal during automated driving is of critical importance to the development of driver monitoring systems and improving the safety of this technology.
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Accidentes de Tránsito , Análisis y Desempeño de Tareas , Humanos , Accidentes de Tránsito/prevención & control , Automatización , Vehículos Autónomos , ConcienciaciónRESUMEN
Vehicle automation promises to reduce the demands of the driving task, making driving less fatiguing, more convenient, and safer. Nevertheless, Level 3 automated vehicles rely on a human driver to be ready to resume control, requiring the driver to reconstruct situation awareness (SA) and resume the driving task. Understanding the interaction between non-driving-related task (NDRT) use, SA, and takeover capacity is important because an effective takeover is entirely dependent on, and scaffolds from, effectively reconstructed SA. While a number of studies have looked at the behavioural impact of being 'in- or on-the-loop', fewer consider the cognitive impact, particularly the consequences for SA. The present study exposed participants to an extended simulated automated drive involving two critical takeover scenarios (early- and late-drive). We compared automated vehicle (AV) operators who were required to passively monitor the vehicle to those engaging with self-selected NDRTs. Monitoring operators demonstrated lower total- and schema-specific SA count scores following a fatiguing drive compared to those engaging with self-selected NDRTs. NDRT engagement resulted in no significant difference in SA count scores early- and late-drive. Assessment of differences in the type and sensory modality of NDRTs indicated operators make fundamentally different selections about the NDRTs they engage with in an automated driving environment compared to a manual environment. The present study provides further evidence linking SA and AV operator behaviour and underscores the need to understand the role of SA in takeover capacity. Our findings suggest that although SA declines over time regardless of driving task requirements (Monitoring versus NDRT engagement), NDRT use may facilitate better SA construction, with implications for the regulation of NDRT use in AVs as the technology progresses.
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Conducción de Automóvil , Concienciación , Humanos , Vehículos Autónomos , Accidentes de Tránsito , Automatización , Fatiga , Tiempo de Reacción/fisiologíaRESUMEN
Autonomous vehicles are the near future of the automobile industry. However, until they reach Level 5, humans and cars will share this intermediate future. Therefore, studying the transition between autonomous and manual modes is a fascinating topic. Automated vehicles may still need to occasionally hand the control to drivers due to technology limitations and legal requirements. This paper presents a study of driver behaviour in the transition between autonomous and manual modes using a CARLA simulator. To our knowledge, this is the first take-over study with transitions conducted on this simulator. For this purpose, we obtain driver gaze focalization and fuse it with the road's semantic segmentation to track to where and when the user is paying attention, besides the actuators' reaction-time measurements provided in the literature. To track gaze focalization in a non-intrusive and inexpensive way, we use a method based on a camera developed in previous works. We devised it with the OpenFace 2.0 toolkit and a NARMAX calibration method. It transforms the face parameters extracted by the toolkit into the point where the user is looking on the simulator scene. The study was carried out by different users using our simulator, which is composed of three screens, a steering wheel and pedals. We distributed this proposal in two different computer systems due to the computational cost of the simulator based on the CARLA simulator. The robot operating system (ROS) framework is in charge of the communication of both systems to provide portability and flexibility to the proposal. Results of the transition analysis are provided using state-of-the-art metrics and a novel driver situation-awareness metric for 20 users in two different scenarios.
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Conducción de Automóvil , Humanos , Tiempo de Reacción , Automatización , Atención , Concienciación , Accidentes de Tránsito/prevención & controlRESUMEN
Many studies on effects of non-driving related tasks in the context of SAE Level3 automated driving have been conducted in driving simulator settings applying standardized tasks. Thereby internal validity is favored over external validity. To assess the influence of engagement in three natural non-driving related tasks on takeover behavior in the context of SAE Level3 automated driving, we conducted an experiment on a test track with a sample of naïve participants from the general public. We used a Wizard-of-Oz vehicle to simulate a SAE Level 3 traffic jam function in a real driving setting. To measure effects of compatibility between non-driving related tasks and driving task on subsequent takeover behavior and following manual driving behavior, participants played Tetris, watched a documentary film and read a text and typed a summary of it. After approx. 15 min, each non-driving related task was interrupted by a request to intervene. In the manual driving phase after the third takeover, participants encountered a balloon car positioned on their lane which they had to evade. Results show longer takeover times in the film and text condition compared to the Tetris condition. Implications on theory and practice are discussed.
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Accidentes de Tránsito , Conducción de Automóvil , Humanos , Automatización , Accidentes de Tránsito/prevención & control , Aeronaves , Medios de Comunicación de Masas , Tiempo de ReacciónRESUMEN
Road traffic accidents (RTAs) are an ever-existing threat to all road users. Automated vehicles (AVs; SAE Level 3-5) are developed in many countries. They are promoted with numerous benefits such as increased safety yielding less RTAs, less congestion, less greenhouse gas emissions, and the possibility of enabling non-driving related tasks (NDRTs). However, there has been no study which has investigated different NDRT conditions, while comparing participants who experienced a severe RTA in the past with those who experienced no RTA. Therefore, we conducted a driving simulator study (N = 53) and compared two NDRT conditions (i.e., auditory-speech (ASD) vs. heads-up display (HUD)) and an accident (26 participants) with a non-accident group (27; between-subjects design). Although our results did not reveal any interaction effect, and no group difference between the accident and the non-accident group on NDRT, take-over request (TOR), and driving performance, we uncovered for both groups better performances for the HUD condition, whereas a lower cognitive workload was reported for the ASD condition. Nevertheless, there was no difference for technology trust between the two conditions. Albeit we observed higher self-ratings of PTSD symptoms for the accident than for the non-accident group, there were no group differences on depression and psychological resilience self-ratings. We conclude that severe RTA experiences do not undermine NDRT, TOR, and driving performance in a SAE Level 3 driving simulator study, although PTSD symptoms after an RTA may affect the psychological wellbeing.
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Accidentes de Tránsito , Conducción de Automóvil , Humanos , ConfianzaRESUMEN
OBJECTIVE: At conditionally automated driving, the driver can temporarily engage in non-driving related tasks (NDRTs). However, they must safely take over control when the automated driving system reaches its operation limit. Thus, understanding the effects of the NDRTs on driver take-over performance is essential. The present work investigates the effects of various NDRTs on motor readiness in take-over scenarios during conditionally automated driving. METHODS: Three driving simulator studies were conducted. 48, 49, and 22 participants were recruited in three experiments, respectively. The participants were distracted by different NDRTs (everyday task in Experiment 1, arrow task in Experiment 2, and SuRT in Experiment 3) on a tablet mounted in the vehicle. The everyday task included reading the news and watching a video, and the arrow task included a set of arrow matrices presented to the participants in sequence. The time budgets in Experiment 1 included 3 s, 4 s, and 5 s, and the time budgets in Experiment 2 and 3 included 5 s and 7 s. A take-over request (TOR) warning was issued in the automated driving condition when the participants encountered a broken-down car in front. The participants must regain control of the vehicle with the given time budget. The hands-on time was evaluated, measuring the time from the TOR until the hands touch the steering wheel. RESULTS: The task (arrow task and SuRT), time budget (5 s and 7 s), and gender did not affect the hands-on time. However, the hands-on time for the drivers with the everyday task was significantly shorter than that for the drivers with the arrow task in the 5 s time budget. CONCLUSIONS: In conditionally automated driving, the arrow task and SuRT imposed a similar workload on readiness to take over control. Compared to the everyday task, the engagement in the arrow tasks consumed more workload on readiness to take over control.
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Accidentes de Tránsito , Conducción de Automóvil , Automatización , Humanos , Tiempo de ReacciónRESUMEN
Automated driving systems are becoming increasingly prevalent throughout society. In conditionally automated vehicles, drivers may engage in non-driving-related tasks (NDRTs), which can negatively affect their situation awareness (SA) and preparedness to resume control of the vehicle, when necessary. Previous work has investigated engagement in NDRTs, but questions remain unanswered regarding its effect on drivers' SA during a takeover event. The objective of the current study is to use eye-tracking to aid in understanding how visual engagement in NDRTs affects changes in SA of the driving environment after a takeover request (TOR) has been issued. Thirty participants rode in a simulated SAE Level 3 automated driving environment and engaged in three separate pre-TOR tasks (Surrogate Reference Task, Monitoring Task, and Peripheral Detection Task) until presented with a TOR. Situation Awareness Global Assessment Technique (SAGAT) scores and gaze behavior were recorded during the post-TOR segment. Overall, longer times spent viewing the driving scene, and more dispersed visual attention allocation, were observed to be associated with better overall SA. Also, location-based eye tracking metrics show most promise in differentiating between task conditions with significantly different SAGAT scores. Findings from this work can inform the development of real-time SA assessment techniques using eye movements and ultimately contribute to improved operator roadway awareness for next-generation automated transportation.
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Conducción de Automóvil , Concienciación , Accidentes de Tránsito , Automatización , Tecnología de Seguimiento Ocular , HumanosRESUMEN
OBJECTIVE: This study aimed at investigating the driver's takeover performance when switching from working on different non-driving related tasks (NDRTs) while driving with a conditionally automated driving function (SAE L3), which was simulated by a Wizard of Oz vehicle, to manual vehicle control under naturalistic driving conditions. BACKGROUND: Conditionally automated driving systems, which are currently close to market introduction, require the user to stay fallback ready. As users will be allowed to engage in more complex NDRTs during the automated drive than when driving manually, the time needed to regain full manual control could likely be increased. METHOD: Thirty-four users engaged in different everyday NDRTs while driving automatically with a Wizard of Oz vehicle. After approximately either 5 min or 15 min of automated driving, users were requested to take back vehicle control in noncritical situations. The test drive took place in everyday traffic on German freeways in the metropolitan area of Stuttgart. RESULTS: Particularly tasks that required users to turn away from the central road scene or hold an object in their hands led to increased takeover times. Accordingly, increased variance in the driver's lane position was found shortly after the switch to manual control. However, the drivers rated the takeover situations to be mostly "harmless." CONCLUSION: Drivers managed to regain control over the vehicle safely, but they needed more time to prepare for the manual takeover when the NDRTs caused motoric workload. APPLICATION: The timings found in the study can be used to design comfortable and safe takeover concepts for automated vehicles.
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Automatización , Conducción de Automóvil , Tiempo de Reacción , Adulto , Anciano , Femenino , Humanos , Masculino , Sistemas Hombre-Máquina , Persona de Mediana Edad , Análisis y Desempeño de TareasRESUMEN
OBJECTIVE: The aim of this study was to investigate the effects of task-induced fatigue in prolonged conditional automated driving on takeover performance. BACKGROUND: In conditional automated driving, the driver can engage in non-driving related tasks (NDRTs) and does not have to monitor the system and the driving environment. In the event that the system hits its limits, the human driver must regain control of the car. To ensure safety, adequate driver fallback performance is necessary. Effects of the drivers' state and the engagement in NDRTs need to be investigated. METHOD: Seventy-three participants experienced prolonged conditional automated rides and simultaneously had to engage in either an activating quiz or a fatiguing monitoring task (between subjects). After 50 minutes, a takeover situation occurred, and participants had to regain control of the car. RESULTS: Prolonged conditional automated driving and simultaneously engaging in NDRTs affected the driver's state and the takeover performance of the participants. Takeover performance was impaired when participants had to deal with monotonous NDRTs. CONCLUSION: An engagement in monotonous monitoring tasks in conditional automated driving affects the driver's state and takeover performance when it comes to takeover situations. Especially in prolonged automated driving, an adequate driver state seems to be necessary for safety reasons. APPLICATION: The results of this study demonstrate that engagement in monotonous NDRTs while driving conditionally automated may negatively affect takeover performance. A monitoring of the driver state and adapted assistance in a takeover situation seems to be a good opportunity to ensure safety.