Is it Good or Bad to Provide Driver Fatigue Warning During Take-Over in Highly Automated Driving?

2021 ◽  
pp. 036119812110469
Author(s):  
Jianwei Niu ◽  
Chuang Ma
Keyword(s):  
Situation Awareness ◽  
Steering Wheel ◽  
Automated Driving ◽  
The Road ◽  
Hands On ◽  
Level 3 ◽  
Processing Techniques ◽  
Fatigue Detection ◽  
Different Levels ◽  
Tor Tor

When and how the driver should be intervened to relieve the fatigue status in Society of Automotive Engineers Level 3 automated driving has raised numerous debates. In this paper, we identify the driver’s fatigue level according to the driver’s facial features before the system issues the take-over request (TOR), and then perform the fatigue warning (FW) intervention to investigate whether the driver would be better prepared for taking over the vehicle safely and efficiently. In a simulator-based study, we compared the driver’s driving performance under the condition of whether a FW intervention was provided before a possible TOR, with the aim to gain insights into the ability of fatigued drivers to regain manual control and situation awareness after automated driving. A FW + TOR condition was compared with a TOR-only condition using a within-subject design with 30 participants. Under the effect of FW, participants exhibited better take-over performance; for example, they gazed at the road and placed their hands on the steering wheel earlier before taking over the vehicle. Moreover, in the FW + TOR state, they also exhibited better take-over performance, with a shorter average braking reaction time and a higher average speed, which reflects that the driver’s speed is more stable during the take-over of the vehicle and that the take-over task can be completed with a smaller deceleration. Furthermore, it is concluded that the FW (5 s before TOR) + TOR mode has greater potential to increase the safety and acceptance of automated driving, and could reduce the driver’s safety risk in a fatigue state when driving automatically compared with the FW (10 s before TOR) + TOR mode. It is concluded that FW (5 s before TOR) + TOR mode has the potential to increase safety and acceptance of automated driving as compared with systems that provide only TORs. In addition, we need to design take-over scenarios and non-driving-related tasks with different levels of complexity to satisfy subsequent research. The fatigue detection method based on image-processing techniques also needs further improvement.

scholarly journals Optimal Time Intervals in Two-Stage Takeover Warning Systems With Insight Into the Drivers’ Neuroticism Personality

2021 ◽  
Vol 12 ◽  
Author(s):  
Wei Zhang ◽  
Yilin Zeng ◽  
Zhen Yang ◽  
Chunyan Kang ◽  
Changxu Wu ◽  
...  
Keyword(s):  
Situation Awareness ◽  
Proper Time ◽  
Steering Wheel ◽  
Optimal Time ◽  
Time Interval ◽  
Warning Systems ◽  
Automated Driving ◽  
Time Intervals ◽  
Two Stage ◽  
Hands On

Conditional automated driving [level 3, Society of Automotive Engineers (SAE)] requires drivers to take over the vehicle when an automated system’s failure occurs or is about to leave its operational design domain. Two-stage warning systems, which warn drivers in two steps, can be a promising method to guide drivers in preparing for the takeover. However, the proper time intervals of two-stage warning systems that allow drivers with different personalities to prepare for the takeover remain unclear. This study explored the optimal time intervals of two-stage warning systems with insights into the drivers’ neuroticism personality. A total of 32 drivers were distributed into two groups according to their self-ratings in neuroticism (high vs. low). Each driver experienced takeover under the two-stage warning systems with four time intervals (i.e., 3, 5, 7, and 9 s). The takeover performance (i.e., hands-on-steering-wheel time, takeover time, and maximum resulting acceleration) and subjective opinions (i.e., appropriateness and usefulness) for time intervals and situation awareness (SA) were recorded. The results showed that drivers in the 5-s time interval had the best takeover preparation (fast hands-on steering wheel responses and sufficient SA). Furthermore, both the 5- and 7-s time intervals resulted in more rapid takeover reactions and were rated more appropriate and useful than the 3- and 9-s time intervals. In terms of personality, drivers with high neuroticism tended to take over immediately after receiving takeover messages, at the cost of SA deficiency. In contrast, drivers with low neuroticism responded safely by judging whether they gained enough SA. We concluded that the 5-s time interval was optimal for drivers in two-stage takeover warning systems. When considering personality, drivers with low neuroticism had no strict requirements for time intervals. However, the extended time intervals were favorable for drivers with high neuroticism in developing SA. The present findings have reference implications for designers and engineers to set the time intervals of two-stage warning systems according to the neuroticism personality of drivers.

scholarly journals The Effects of Vehicle Level Of Automation and Warning Type on Responses to Vehicle Hacking

2019 ◽  
Vol 63 (1) ◽  
pp. 1977-1977
Author(s):  
Wyatt McManus ◽  
Jing Chen
Keyword(s):  
Driving Simulator ◽  
Blind Spot ◽  
Steering Wheel ◽  
Automated Driving ◽  
Impact Surface ◽  
Level Of Automation ◽  
Surface Transportation ◽  
Levels Of Automation ◽  
Automation Level ◽  
Different Levels

Modern surface transportation vehicles often include different levels of automation. Higher automation levels have the potential to impact surface transportation in unforeseen ways. For example, connected vehicles with higher levels of automation are at a higher risk for hacking attempts, because automated driving assistance systems often rely on onboard sensors and internet connectivity (Amoozadeh et al., 2015). As the automation level of vehicle control rises, it is necessary to examine the effect different levels of automation have on the driver-vehicle interactions. While research into the effect of automation level on driver-vehicle interactions is growing, research into how automation level affects driver’s responses to vehicle hacking attempts is very limited. In addition, auditory warnings have been shown to effectively attract a driver’s attention while performing a driving task, which is often visually demanding (Baldwin, 2011; Petermeijer, Doubek, & de Winter, 2017). An auditory warning can be either speech-based containing sematic information (e.g., “car in blind spot”) or non-sematic (e.g., a tone, or an earcon), which can influence driver behaviors differently (Sabic, Mishler, Chen, & Hu, 2017). The purpose of the current study was to examine the effect of level of automation and warning type on driver responses to novel critical events, using vehicle hacking attempts as a concrete example, in a driving simulator. The current study compared how level of automation (manual vs. automated) and warning type (non-semantic vs. semantic) affected drivers’ responses to a vehicle hacking attempt using time to collision (TTC) values, maximum steering wheel angle, number of successful responses, and other measures of response. A full factorial between-subjects design with the two factors made four conditions (Manual Semantic, Manual Non-Semantic, Automated Semantic, and Automated Non-Semantic). Seventy-two participants recruited using SONA ( odupsychology.sona-systems.com ) completed two simulated drives to school in a driving simulator. The first drive ended with the participant safely arriving at school. A two-second warning was presented to the participants three quarters of the way through the second drive and was immediately followed by a simulated vehicle hacking attempt. The warning either stated “Danger, hacking attempt incoming” in the semantic conditions or was a 500 Hz sine tone in the non-semantic conditions. The hacking attempt lasted five seconds before simulating a crash into a vehicle and ending the simulation if no intervention by the driver occurred. Our results revealed no significant effect of level of automation or warning type on TTC or successful response rate. However, there was a significant effect of level of automation on maximum steering wheel angle. This is a measure of response quality (Shen & Neyens, 2017), such that manual drivers had safer responses to the hacking attempt with smaller maximum steering wheel angles. In addition, an effect of warning type that approached significance was also found for maximum steering wheel angle such that participants who received a semantic warning had more severe and dangerous responses to the hacking attempt. The TTC and successful response results from the current experiment do not match those in the previous literature. The null results were potentially due to the warning implementation time and the complexity of the vehicle hacking attempt. In contrast, the maximum steering wheel angle results indicated that level of automation and warning type affected the safety and severity of the participants’ responses to the vehicle hacking attempt. This suggests that both factors may influence responses to hacking attempts in some capacity. Further research will be required to determine if level of automation and warning type affect participants ability to safely respond to vehicle hacking attempts. Acknowledgments. We are grateful to Scott Mishler for his assistance with STISIM programming and Faye Wakefield, Hannah Smith, and Pettie Perkins for their assistance in data collection.

scholarly journals The Identification of Non-Driving Activities with Associated Implication on the Take-Over Process

Sensors ◽  
2021 ◽  
Vol 22 (1) ◽  
pp. 42
Author(s):  
Lichao Yang ◽  
Mahdi Babayi Semiromi ◽  
Yang Xing ◽  
Chen Lv ◽  
James Brighton ◽  
...  
Keyword(s):  
Situation Awareness ◽  
Haptic Feedback ◽  
Hand Movement ◽  
Transition Process ◽  
Recognition System ◽  
Steering Wheel ◽  
Automated Driving ◽  
Driver Behaviour ◽  
Active Mode ◽  
The Impact

In conditionally automated driving, the engagement of non-driving activities (NDAs) can be regarded as the main factor that affects the driver’s take-over performance, the investigation of which is of great importance to the design of an intelligent human–machine interface for a safe and smooth control transition. This paper introduces a 3D convolutional neural network-based system to recognize six types of driver behaviour (four types of NDAs and two types of driving activities) through two video feeds based on head and hand movement. Based on the interaction of driver and object, the selected NDAs are divided into active mode and passive mode. The proposed recognition system achieves 85.87% accuracy for the classification of six activities. The impact of NDAs on the perspective of the driver’s situation awareness and take-over quality in terms of both activity type and interaction mode is further investigated. The results show that at a similar level of achieved maximum lateral error, the engagement of NDAs demands more time for drivers to accomplish the control transition, especially for the active mode NDAs engagement, which is more mentally demanding and reduces drivers’ sensitiveness to the driving situation change. Moreover, the haptic feedback torque from the steering wheel could help to reduce the time of the transition process, which can be regarded as a productive assistance system for the take-over process.

scholarly journals Occupant Monitoring in Automated Vehicles: Classification of Situation Awareness Based on Head Movements While Cornering

2019 ◽  
Vol 63 (1) ◽  
pp. 2078-2082
Author(s):  
Frederik Schewe ◽  
Hao Cheng ◽  
Alexander Hafner ◽  
Monika Sester ◽  
Mark Vollrath
Keyword(s):  
Situation Awareness ◽  
Driving Simulator ◽  
Head Tilt ◽  
Head Movements ◽  
Automated Driving ◽  
The Road ◽  
Subject Design ◽  
Visual Reference ◽  
The Mean

We tested whether head-movements under automated driving can be used to classify a vehicle occupant as either situation-aware or unaware. While manually cornering, an active driver’s head tilt correlates with the road angle which serves as a visual reference, whereas an inactive passenger’s head follows the g-forces. Transferred to partial/conditional automation, the question arises whether aware occupant’s head-movements are comparable to drivers and if this can be used for classification. In a driving-simulator-study (n=43, within-subject design), four scenarios were used to generate or deteriorate situation awareness (manipulation checked). Recurrent neural networks were trained with the resulting head-movements. Inference statistics were used to extract the discriminating feature, ensuring explainability. A very accurate classification was achieved and the mean side rotation-rate was identified as the most differentiating factor. Aware occupants behave more like drivers. Therefore, head-movements can be used to classify situation awareness in experimental settings but also in real driving.

Analysis of Visual Scanning Patterns Comparing Drivers of Simulated L2 and L0 Systems

2019 ◽  
Vol 2673 (10) ◽  
pp. 755-761 ◽  
Author(s):  
Nathan Hatfield ◽  
Yusuke Yamani ◽  
Dakota B. Palmer ◽  
Sarah Yahoodik ◽  
Veronica Vasquez ◽  
...  
Keyword(s):  
Eye Movements ◽  
Driving Simulator ◽  
Steering Wheel ◽  
Automated Driving ◽  
Young Drivers ◽  
Hands On ◽  
Visual Sampling ◽  
The Mean ◽  
Lateral Positioning ◽  
Perception Training

Automated driving systems (ADS) partially or fully perform driving functions. Yet, the effects of ADS on drivers’ visual sampling patterns to the forward roadway remain underexplored. This study examined the eye movements of 24 young drivers during either manual (L0) or partially automated driving (L2) in a driving simulator. After completing a hazard anticipation training program, Road Awareness and Perception Training, drivers in both groups navigated a single simulated drive consisting of four environment types: highway, town, rural, and residential. Drivers of the simulated L2 system were instructed to keep their hands on the steering wheel and told that the system controls the speed and lateral positioning of the vehicle while avoiding potential threats on the forward roadway. The data indicate that the drivers produced fewer fixations during automated driving compared with manual driving. However, the breadth of horizontal and vertical eye movements and the mean fixation durations did not strongly support the null results between the two conditions. Existing hazard anticipation training programs may effectively protect drivers of partially automated systems from inattention to the forward roadway.

scholarly journals Influence of automated driving on driver’s own localization: a driving simulator study

2018 ◽  
Vol 1 (3) ◽  
pp. 99-106 ◽  
Author(s):  
Ryuichi Umeno ◽  
Makoto Itoh ◽  
Satoshi Kitazaki
Keyword(s):  
Situation Awareness ◽  
Driving Simulator ◽  
Automated Driving ◽  
Operation System ◽  
Content Type ◽  
Automatic Driving ◽  
Driver Drowsiness ◽  
Level 3 ◽  
Fundamental Research ◽  
Operation Group

Purpose Level 3 automated driving, which has been defined by the Society of Automotive Engineers, may cause driver drowsiness or lack of situation awareness, which can make it difficult for the driver to recognize where he/she is. Therefore, the purpose of this study was to conduct an experimental study with a driving simulator to investigate whether automated driving affects the driver’s own localization compared to manual driving. Design/methodology/approach Seventeen drivers were divided into the automated operation group and manual operation group. Drivers in each group were instructed to travel along the expressway and proceed to the specified destinations. The automated operation group was forced to select a course after receiving a Request to Intervene (RtI) from an automated driving system. Findings A driver who used the automated operation system tended to not take over the driving operation correctly when a lane change is immediately required after the RtI. Originality/value This is a fundamental research that examined how the automated driving operation affects the driver's own localization. The experimental results suggest that it is not enough to simply issue an RtI, and it is necessary to tell the driver what kind of circumstances he/she is in and what they should do next through the HMI. This conclusion can be taken into consideration for engineers who design automatic driving vehicles.

Peripheral Monitoring of Traffic in Conditionally Automated Driving

2018 ◽  
Vol 62 (1) ◽  
pp. 1828-1832 ◽  
Author(s):  
Jonas Radlmayr ◽  
Karin Brüch ◽  
Kathrin Schmidt ◽  
Christine Solbeck ◽  
Tristan Wehner
Keyword(s):  
Situation Awareness ◽  
Automated Vehicles ◽  
Automated Driving ◽  
Active System ◽  
Subject Design ◽  
Level 3 ◽  
Driving Task

Conditionally automated vehicles (level 3) allow drivers to engage in visual, non-driving related tasks (NDRTs) while the automation is active. System limits require drivers to reengage in the dynamic driving task in take-over situations. If the NDRT is visually engaging, situation awareness (SA) necessary for a successful take-over can decrease. This study analyzed, if the SA of drivers increases while monitoring the surrounding traffic peripherally. A semi-transparent balloon game in the head-up display operationalized the engagement into a visual NDRT with the possibility of peripheral monitoring. In addition, participants without the possibility of monitoring due to simulated heavy fog (second group) were tested along with a third group that could monitor surroundings self-determined without a NDRT. The between-subject design included 57 participants. Results showed that self-determined monitoring leads to higher situation awareness compared to peripheral monitoring and no monitoring. This did not result in better take-over performances.

scholarly journals Transitions Between Highly Automated and Longitudinally Assisted Driving: The Role of the Initiator in the Fight for Authority

2020 ◽  
pp. 001872082094618 ◽  
Author(s):  
Davide Maggi ◽  
Richard Romano ◽  
Oliver Carsten
Keyword(s):  
Road Safety ◽  
Driving Simulator ◽  
Cruise Control ◽  
Automated Driving ◽  
Safety Issues ◽  
The Road ◽  
Operational Design ◽  
Level 3 ◽  
Driving Task

Objective A driving simulator study explored how drivers behaved depending on their initial role during transitions between highly automated driving (HAD) and longitudinally assisted driving (via adaptive cruise control). Background During HAD, drivers might issue a take-over request (TOR), initiating a transition of control that was not planned. Understanding how drivers behave in this situation and, ultimately, the implications on road safety is of paramount importance. Method Sixteen participants were recruited for this study and performed transitions of control between HAD and longitudinally assisted driving in a driving simulator. While comparing how drivers behaved depending on whether or not they were the initiators, different handover strategies were presented to analyze how drivers adapted to variations in the authority level they were granted at various stages of the transitions. Results Whenever they initiated the transition, drivers were more engaged with the driving task and less prone to follow the guidance of the proposed strategies. Moreover, initiating a transition and having the highest authority share during the handover made the drivers more engaged with the driving task and attentive toward the road. Conclusion Handover strategies that retained a larger authority share were more effective whenever the automation initiated the transition. Under driver-initiated transitions, reducing drivers’ authority was detrimental for both performance and comfort. Application As the operational design domain of automated vehicles (Society of Automotive Engineers [SAE] Level 3/4) expands, the drivers might very well fight boredom by taking over spontaneously, introducing safety issues so far not considered but nevertheless very important.

scholarly journals Context-Adaptive Availability Notifications for an SAE Level 3 Automation

2021 ◽  
Vol 5 (4) ◽  
pp. 16
Author(s):  
Simon Danner ◽  
Alexander Feierle ◽  
Carina Manger ◽  
Klaus Bengler
Keyword(s):  
Driving Simulator ◽  
Human Machine Interface ◽  
Gaze Behavior ◽  
Activation Time ◽  
Automated Driving ◽  
Driving Function ◽  
The Road ◽  
Level 3 ◽  
Machine Interface ◽  
Simulator Study

Context-adaptive functions are not new in the driving context, but even so, investigations into these functions concerning the automation human–machine interface (aHMI) have yet to be carried out. This study presents research into context-adaptive availability notifications for an SAE Level 3 automation in scenarios where participants were surprised by either availability or non-availability. For this purpose, participants (N = 30) took part in a driving simulator study, experiencing a baseline HMI concept as a comparison, and a context-adaptive HMI concept that provided context-adaptive availability notifications with the aim of improving acceptance and usability, while decreasing frustration (due to unexpected non-availability) and gaze deviation from the road when driving manually. Furthermore, it was hypothesized that participants, when experiencing the context-adaptive HMI, would activate the automated driving function more quickly when facing unexpected availability. None of the hypotheses could be statistically confirmed; indeed, where gaze behavior was concerned, the opposite effects were found, indicating increased distraction induced by the context-adaptive HMI. However, the trend in respect to the activation time was towards shorter times with the context-adaptive notifications. These results led to the conclusion that context-adaptive availability notifications might not always be beneficial for users, while more salient availability notifications in the case of an unexpected availability could be advantageous.

Cognitive Workload Using Interactive Voice Messaging Systems

2016 ◽  
Vol 60 (1) ◽  
pp. 1894-1898 ◽  
Author(s):  
James R. Coleman ◽  
Jonna Turrill ◽  
Joel M. Cooper ◽  
David L. Strayer
Keyword(s):  
Motor Vehicle ◽  
Text Messages ◽  
Steering Wheel ◽  
The Road ◽  
Human Voice ◽  
Cognitive Distraction ◽  
Hands On ◽  
Voice Messaging ◽  
On The Road ◽  
E Mail

The current research sought to understand the sources of cognitive distraction stemming from voice-based in-vehicle infotainment systems (IVIS) to send and receive textual information. Three experiments each evaluated 1) a baseline single-task condition, 2) listening to e-mail/text messages read by a “natural” pre-recorded human voice, 3) listening to e-mail/text messages read by a “synthetic” computerized text-to-speech system, 4) listening and composing replies to e-mail/text messages read by a “natural” voice, and 5) listening and composing replies to e-mail/text messages read by a “synthetic” voice. Each task allowed the driver to keep their eyes on the road and their hands on the steering wheel, thus any impairment to driving was caused by the diversion of non-visual attention away from the task of operating the motor vehicle.

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