Human Factors最新文献

Objective: Using brain haemodynamic responses to measure perceived risk from traffic complexity during automated driving.

Background: Although well-established during manual driving, the effects of driver risk perception during automated driving remain unknown. The use of fNIRS in this paper for assessing drivers' states posits it could become a novel method for measuring risk perception.

Methods: Twenty-three volunteers participated in an empirical driving simulator experiment with automated driving capability. Driving conditions involved suburban and urban scenarios with varying levels of traffic complexity, culminating in an unexpected hazardous event. Perceived risk was measured via fNIRS within the prefrontal cortical haemoglobin oxygenation and from self-reports.

Results: Prefrontal cortical haemoglobin oxygenation levels significantly increased, following self-reported perceived risk and traffic complexity, particularly during the hazardous scenario.

Conclusion: This paper has demonstrated that fNIRS is a valuable research tool for measuring variations in perceived risk from traffic complexity during highly automated driving. Even though the responsibility over the driving task is delegated to the automated system and dispositional trust is high, drivers perceive moderate risk when traffic complexity builds up gradually, reflected in a corresponding significant increase in blood oxygenation levels, with both subjective (self-reports) and objective (fNIRS) increasing further during the hazardous scenario.

Application: Little is known regarding the effects of drivers' risk perception with automated driving. Building upon our experimental findings, future work can use fNIRS to investigate the mental processes for risk assessment and the effects of perceived risk on driving behaviours to promote the safe adoption of automated driving technology.

Objective: Varying driver distraction algorithms were developed using vehicle kinematics and driver gaze data obtained from a camera-based driver monitoring system (DMS).

Background: Distracted driving characteristics can be difficult to accurately detect due to wide variation in driver behavior across driving environments. The growing availability of information about drivers and their involvement in the driving task increases the opportunity for accurately recognizing attention state.

Method: A baseline for driver distraction levels was developed using a video feed of 24 separate drivers in varying naturalistic driving conditions. This initial assessment was used to develop four buffer-based algorithms that aimed to determine a driver's real-time attentiveness, via a variety of metrics and combinations thereof.

Results: Of those tested, the optimal algorithm included ungrouped glance locations and speed. Notably, as an algorithm's performance of detecting very distracted drivers improved, its accuracy for correctly identifying attentive drivers decreased.

Conclusion: At a minimum, drivers' gaze position and vehicle speed should be included when designing driver distraction algorithms to delineate between glance patterns observed at high and low speeds. Distraction algorithms should be designed with an understanding of their limitations, including instances in which they may fail to detect distracted drivers, or falsely notify attentive drivers.

Application: This research adds to the body of knowledge related to driver distraction and contributes to available methods to potentially address and reduce occurrences. Machine learning algorithms can build on the data elements discussed to increase distraction detection accuracy using robust artificial intelligence.

Objective: This study aimed to investigate the impact of automated vehicle (AV) interaction mode on drivers' trust and preferred driving styles in response to pedestrian- and traffic-related road events.

Background: The rising popularity of AVs highlights the need for a deeper understanding of the factors that influence trust in AV. Trust is a crucial element, particularly because current AVs are only partially automated and may require manual takeover; miscalibrated trust could have an adverse effect on safe driver-vehicle interaction. However, before attempting to calibrate trust, it is vital to comprehend the factors that contribute to trust in automation.

Methods: Thirty-six individuals participated in the experiment. Driving scenarios incorporated adaptive SAE Level 2 AV algorithms, driven by participants' event-based trust in AVs and preferences for AV driving styles. The study measured participants' trust, preferences, and the number of takeover behaviors.

Results: Higher levels of trust and preference for more aggressive AV driving styles were found in response to pedestrian-related events compared to traffic-related events. Furthermore, drivers preferred the trust-based adaptive mode and had fewer takeover behaviors than the preference-based adaptive and fixed modes. Lastly, participants with higher trust in AVs favored more aggressive driving styles and made fewer takeover attempts.

Conclusion: Adaptive AV interaction modes that depend on real-time event-based trust and event types may represent a promising approach to human-automation interaction in vehicles.

Application: Findings from this study can support future driver- and situation-aware AVs that can adapt their behavior for improved driver-vehicle interaction.

Objective: examine the prevalence of driver distraction in naturalistic driving when implementing European New Car Assessment Program (Euro NCAP)-defined distraction behaviours.

Background: The 2023 introduction of Occupant Status monitoring (OSM) into Euro NCAP will accelerate uptake of Driver State Monitoring (DSM). Euro NCAP outlines distraction behaviours that DSM must detect to earn maximum safety points. Distraction behaviour prevalence and driver alerting and intervention frequency have yet to be examined in naturalistic driving.

Method: Twenty healthcare workers were provided with an instrumented vehicle for approximately two weeks. Data were continuously monitored with automotive grade DSM during daily work commutes, resulting in 168.8 hours of driver head, eye and gaze tracking.

Results: Single long distraction events were the most prevalent, with .89 events/hour. Implementing different thresholds for driving-related and driving-unrelated glance regions impacts alerting rates. Lizard glances (primarily gaze movement) occurred more frequently than owl glances (primarily head movement). Visual time-sharing events occurred at a rate of .21 events/hour.

Conclusion: Euro NCAP-described driver distraction occurs naturalistically. Lizard glances, requiring gaze tracking, occurred in high frequency relative to owl glances, which only require head tracking, indicating that less sophisticated DSM will miss a substantial amount of distraction events.

Application: This work informs OEMs, DSM manufacturers and regulators of the expected alerting rate of Euro NCAP defined distraction behaviours. Alerting rates will vary with protocol implementation, technology capability, and HMI strategies adopted by the OEMs, in turn impacting safety outcomes, user experience and acceptance of DSM technology.

Objective: To explore the scope of available research and to identify research gaps on in-vehicle interventions for drowsiness that utilize driver monitoring systems (DMS).

Background: DMS are gaining popularity as a countermeasure against drowsiness. However, how these systems can be best utilized to guide driver attention is unclear.

Methods: A scoping review was conducted in adherence to PRISMA guidelines. Five electronic databases (ACM Digital Library, Scopus, IEEE Xplore, TRID, and SAE Mobilus) were systematically searched in April 2022. Original studies examining in-vehicle drowsiness interventions that use DMS in a driving context (e.g., driving simulator and driver interviews) passed the screening. Data on study details, state detection methods, and interventions were extracted.

Results: Twenty studies qualified for inclusion. Majority of interventions involved warnings (n = 16) with an auditory component (n = 14). Feedback displays (n = 4) and automation takeover (n = 4) were also investigated. Multistage interventions (n = 12) first cautioned the driver, then urged them to take an action, or initiated an automation takeover. Overall, interventions had a positive impact on sleepiness levels, driving performance, and user evaluations. Whether interventions effective for one type of sleepiness (e.g., passive vs. active fatigue) will perform well for another type is unclear.

Conclusion: Literature mainly focused on developing sensors and improving the accuracy of DMS, but not on the driver interactions with these technologies. More intervention studies are needed in general and for investigating their long-term effects.

Application: We list gaps and limitations in the DMS literature to guide researchers and practitioners in designing and evaluating effective safety systems for drowsy driving.

Objective: This study investigated the effects of four takeover request (TOR) times and seven warning modalities on performance and trust in automated driving on a mildly congested urban road scenario, as well as the relationship between takeover performance and trust.

Background: Takeover is crucial in L3 automated driving, where human-machine codriving is employed. Establishing trust in takeover scenarios among drivers can enhance the acceptance of autonomous vehicles, thereby promoting their widespread adoption.

Method: Using a driving simulator, data from 28 participants, including collision counts, takeover time (ToT), electrodermal activity (EDA) data, and self-reported trust scores, were collected and analyzed primarily using Generalized Linear Mixed Models (GLMM).

Results: Collisions during the takeover undermined participants' trust in the autonomous driving system. As TOR time increased, participants' trust improved, and the longer TOR time did not lead to participant confusion. There was no significant relationship between warning modality and trust. Furthermore, the combination of three warning modalities did not exhibit a notable advantage over the combination of two modalities.

Conclusion: The study examined the effects of TOR time and warning modality on trust, as well as preliminarily explored the potential association between takeover performance, including collisions and ToT, and trust in autonomous driving takeovers.

Application: Researchers and designers of automotive interactions were given referenceable TOR time and warning modality by this study, which extended the autonomous driving takeover scenarios. These findings contributed to boosting drivers' confidence in transferring control to the automated system.

Objective: An up-to-date and thorough literature review is needed to identify factors that influence driver situation awareness (SA) during control transitions in conditionally automated vehicles (AV). This review also aims to ascertain SA components required for takeovers, aiding in the design and evaluation of human-vehicle interfaces (HVIs) and the selection of SA assessment methodologies.

Background: Conditionally AVs alleviate the need for continuous road monitoring by drivers yet necessitate their reengagement during control transitions. In these instances, driver SA is crucial for effective takeover decisions and subsequent actions. A comprehensive review of influential SA factors, SA components, and SA assessment methods will facilitate driving safety in conditionally AVs but is still lacking.

Method: A systematic literature review was conducted. Thirty-four empirical research articles were screened out to meet the criteria for inclusion and exclusion.

Results: A conceptual framework was developed, categorizing 23 influential SA factors into four clusters: task/system, situational, individual, and nondriving-related task factors. The analysis also encompasses an examination of pertinent SA components and corresponding HVI designs for specific takeover events, alongside an overview of SA assessment methods for conditionally AV takeovers.

Conclusion: The development of a conceptual framework outlining influential SA factors, the examination of SA components and their suitable design of presentation, and the review of SA assessment methods collectively contribute to enhancing driving safety in conditionally AVs.

Application: This review serves as a valuable resource, equipping researchers and practitioners with insights to guide their efforts in evaluating and enhancing driver SA during conditionally AV takeovers.

Objective: We investigated how different deceleration intentions (i.e. an automated vehicle either decelerated for leading traffic or yielded for pedestrians) and a novel (Slow Pulsing Light Band - SPLB) or familiar (Flashing Headlights - FH) external Human Machine Interface (eHMI) informed pedestrians' crossing behaviour.

Background: The introduction of SAE Level 4 Automated Vehicles (AVs) has recently fuelled interest in new forms of explicit communication via eHMIs, to improve the interaction between AVs and surrounding road users. Before implementing these eHMIs, it is necessary to understand how pedestrians use them to inform their crossing decisions.

Method: Thirty participants took part in the study using a Head-Mounted Display. The independent variables were deceleration intentions and eHMI design. The percentage of crossings, collision frequency and crossing initiation time across trials were measured.

Results: Pedestrians were able to identify the intentions of a decelerating vehicle, using implicit cues, with more crossings made when the approaching vehicles were yielding to them. They were also more likely to cross when a familiar eHMI was presented, compared to a novel one or no eHMI, regardless of the vehicle's intention. Finally, participants learned to take a more cautious approach as trials progressed, and not to base their decisions solely on the eHMI.

Conclusion: A familiar eHMI led to early crossings regardless of the vehicle's intention but also led to a higher collision frequency than a novel eHMI.

Application: To achieve safe and acceptable interactions with AVs, it is important to provide eHMIs that are congruent with road users' expectations.

Objective: To examine the impact of secondary task performance on contextual blindness arising from the suppression and masking of temporal and spatial sequence learning.

Background: Dual-task scenarios can lead to a diminished ability to use environmental cues to guide attention, a phenomenon that is related to multitasking-induced inattentional blindness. This research aims to extend the theoretical understanding of how secondary tasks can impair attention and memory processes in sequence learning and access.

Method: We conducted three experiments. In Experiment 1, we used a serial reaction time task to investigate the impact of a secondary tone counting task on temporal sequence learning. In Experiment 2, we used a contextual cueing task to examine the effects of dual-task performance on spatial cueing. In Experiment 3, we integrated and extended these concepts to a simulated driving task.

Results: Across the experiments, the performance of a secondary task consistently suppressed (all experiments) and masked task learning (experiments 1 and 3). In the serial response and spatial search tasks, dual-task conditions reduced the accrual of sequence knowledge and impaired knowledge expression. In the driving simulation, similar patterns of learning suppression from multitasking were also observed.

Conclusion: The findings suggest that secondary tasks can significantly suppress and mask sequence learning in complex tasks, leading to a form of contextual blindness characterized by impairments in the ability to use environmental cues to guide attention and anticipate future events.

Application: These findings have implications for both skill acquisition and skilled performance in complex domains such as driving, aviation, manufacturing, and human-computer interaction.