Influence of Driving Experience on Distraction Engagement in Automated Vehicles

2019 ◽  
Vol 2673 (9) ◽  
pp. 142-151 ◽  
Author(s):  
Dengbo He ◽  
Birsen Donmez
Keyword(s):  
Secondary Task ◽  
Driving Simulator ◽  
Phase 1 ◽  
Task Engagement ◽  
Automated Vehicles ◽  
Automated Driving ◽  
Novice Drivers ◽  
Driving Experience ◽  
Driving Skill ◽  
The Difference

State-of-the-art vehicle automation requires drivers to visually monitor the driving environment and the automation (through interfaces and vehicle’s actions) and intervene when necessary. However, as evidenced by recent automated vehicle crashes and laboratory studies, drivers are not always able to step in when the automation fails. Research points to the increase in distraction or secondary-task engagement in the presence of automation as a potential reason. However, previous research on secondary-task engagement in automated vehicles mainly focused on experienced drivers. This issue may be amplified for novice drivers with less driving skill. In this paper, we compared secondary-task engagement behaviors of novice and experienced drivers both in manual (non-automated) and automated driving settings in a driving simulator. A self-paced visual-manual secondary task presented on an in-vehicle display was utilized. Phase 1 of the study included 32 drivers (16 novice) who drove the simulator manually. In Phase 2, another set of 32 drivers (16 novice) drove with SAE-level-2 automation. In manual driving, there were no differences between novice and experienced drivers’ rate of manual interactions with the secondary task (i.e., taps on the display). However, with automation, novice drivers had a higher manual interaction rate with the task than experienced drivers. Further, experienced drivers had shorter average glance durations toward the task than novice drivers in general, but the difference was larger with automation compared with manual driving. It appears that with automation, experienced drivers are more conservative in their secondary-task engagement behaviors compared with novice drivers.

The Effects of Distraction on Anticipatory Driving

2018 ◽  
Vol 62 (1) ◽  
pp. 1960-1964 ◽  
Author(s):  
Dengbo He ◽  
Birsen Donmez
Keyword(s):  
Secondary Task ◽  
Driving Simulator ◽  
Response Times ◽  
Task Engagement ◽  
Negative Effects ◽  
Novice Drivers ◽  
Secondary Tasks ◽  
Future Events ◽  
Simulator Study ◽  
Recognition And Response

The anticipation of future events in traffic can allow potential gains in recognition and response times. Anticipatory actions (i.e., actions in preparation for a potential upcoming conflict) have been found to be more prevalent among experienced drivers in a driving simulator study where driving was the sole task. The influence of secondary tasks on anticipatory driving has not yet been investigated, despite the prevalence and negative effects of distraction widely documented in the literature. A driving simulator experiment was conducted with 16 experienced and 16 novice drivers to address this gap with half of the participants provided with a self-paced visual-manual secondary task. More anticipatory actions were observed among experienced drivers in general compared to novices; experienced drivers also exhibited more efficient visual scanning behaviors. Secondary task engagement reduced anticipatory actions for both experienced and novice drivers.

scholarly journals Anticipatory Driving in Automated Vehicles: The Effects of Driving Experience and Distraction

2021 ◽  
pp. 001872082110261
Author(s):  
Dengbo He ◽  
Chelsea A. DeGuzman ◽  
Birsen Donmez
Keyword(s):  
Secondary Task ◽  
Cruise Control ◽  
Automated Vehicles ◽  
Novice Drivers ◽  
Driving Experience ◽  
Driving Behaviors ◽  
Research Gap ◽  
Task Conditions ◽  
Lane Keeping ◽  
Level 2

Objective To understand the influence of driving experience and distraction on drivers’ anticipation of upcoming traffic events in automated vehicles. Background In nonautomated vehicles, experienced drivers spend more time looking at cues that indicate upcoming traffic events compared with novices, and distracted drivers spend less time looking at these cues compared with nondistracted drivers. Further, pre-event actions (i.e., proactive control actions prior to traffic events) are more prevalent among experienced drivers and nondistracted drivers. However, there is a research gap on the combined effects of experience and distraction on driver anticipation in automated vehicles. Methods A simulator experiment was conducted with 16 experienced and 16 novice drivers in a vehicle equipped with adaptive cruise control and lane-keeping assist systems (resulting in SAE Level 2 driving automation). Half of the participants in each experience group were provided with a self-paced primarily visual-manual secondary task. Results Drivers with the task spent less time looking at cues and were less likely to perform anticipatory driving behaviors (i.e., pre-event actions or preparation for pre-event actions such as hovering fingers over the automation disengage button). Experienced drivers exhibited more anticipatory driving behaviors, but their attention toward the cues was similar to novices for both task conditions. Conclusion In line with nonautomated vehicle research, in automated vehicles, secondary task engagement impedes anticipation while driving experience facilitates anticipation. Application Though Level 2 automation can relieve drivers of manually controlling the vehicle and allow engagement in distractions, visual-manual distraction engagement can impede anticipatory driving and should be restricted.

scholarly journals Changes over 12 months in eye glances during secondary task engagement among novice drivers

2016 ◽  
Vol 93 ◽  
pp. 48-54 ◽  
Author(s):  
Fearghal O’Brien ◽  
Sheila G. Klauer ◽  
Johnathon Ehsani ◽  
Bruce G. Simons-Morton
Keyword(s):  
Secondary Task ◽  
Task Engagement ◽  
Novice Drivers

Evaluation of a Training Intervention to Improve Novice Drivers’ Hazard Mitigation When Approaching Left Turn Scenarios

2019 ◽  
Vol 2673 (12) ◽  
pp. 474-484
Author(s):  
Jeffrey W. Muttart ◽  
Swaroop Dinakar ◽  
Donald L. Fisher ◽  
Teena M. Garrison ◽  
Siby Samuel
Keyword(s):  
Video Game ◽  
Driving Simulator ◽  
Phase 1 ◽  
Hazard Mitigation ◽  
Management Strategies ◽  
Crash Risk ◽  
Novice Drivers ◽  
Left Turn ◽  
Street Sign ◽  
Teenage Drivers

Crash statistics reveal that newly licensed teenage drivers experience a higher risk of crashing than more experienced drivers, particularly when turning left across the path of approaching traffic. Research has also demonstrated that novice drivers exhibit poor hazard mitigation skills. The current study assesses the effectiveness of a training program aimed at improving novice drivers’ hazard mitigation and speed selection behaviors as both the through driver and turning driver in left turn across path scenarios. In this study, novice drivers were randomly assigned to one of two training cohorts: anticipation-control-terminate (ACT) or placebo. Phase 1 of ACT is a video game where drivers must select where to look, where they would steer, and when they would slow when observing the approach to known fatal crash risk scenarios. Placebo training involved reaction time tests and street sign definitions. In phase 2 the ACT-trained participants were shown where their choices were similar to, or different than, those of drivers aged 26 through 61who had not had crashed in the previous 10 years. In phase 3, ACT-trained drivers were compared with placebo-trained drivers at left turn scenarios both as through driver and turning driver, using a driving simulator. ACT-trained drivers were more likely to exhibit anticipatory glances and slowing behaviors, and were significantly less likely to crash than were placebo-trained drivers. The results indicate that ACT was effective as a countermeasure for training novice drivers to select better speed management strategies in the simulated scenarios utilized in this research.

Do Autonomous Vehicle Driving Styles Affect User State?: A Preliminary Investigation

2019 ◽  
Author(s):  
Yuan Shi ◽  
Wenhui Huang ◽  
Federico Cheli ◽  
Monica Bordegoni ◽  
Giandomenico Caruso
Keyword(s):  
Driving Simulator ◽  
Autonomous Vehicle ◽  
Preliminary Investigation ◽  
Autonomous Systems ◽  
Autonomous Driving ◽  
Point Of View ◽  
Automated Driving ◽  
Perspective View ◽  
Self Assessment ◽  
Driving Experience

Abstract A bursting number of achievements in the autonomous vehicle industry have been obtained during the past decades. Various systems have been developed to make automated driving possible. Due to the algorithm used in the autonomous vehicle system, the performance of the vehicle differs from one to another. However, very few studies have given insight into the influence caused by implementing different algorithms from a human factors point of view. Two systems based on two algorithms with different characteristics are utilized to generate the two driving styles of the autonomous vehicle, which are implemented into a driving simulator in order to create the autonomous driving experience. User’s skin conductance (SC) data, which enables the evaluation of user’s cognitive workload and mental stress were recorded and analyzed. Subjective measures were applied by filling out Swedish occupational fatigue inventory (SOFI-20) to get a user self-reporting perspective view of their behavior changes along with the experiments. The results showed that human’s states were affected by the driving styles of different autonomous systems, especially in the period of speed variation. By analyzing users’ self-assessment data, a correlation was observed between the user “Sleepiness” and the driving style of the autonomous vehicle. These results would be meaningful for the future development of the autonomous vehicle systems, in terms of balancing the performance of the vehicle and user’s experience.

Attentive User Interfaces to Improve Multitasking and Take-Over Performance in Automated Driving

2019 ◽  
Vol 11 (3) ◽  
pp. 40-58 ◽  
Author(s):  
Philipp Wintersberger ◽  
Clemens Schartmüller ◽  
Andreas Riener
Keyword(s):  
User Interfaces ◽  
Lead Time ◽  
Driving Simulator ◽  
Handheld Devices ◽  
Automated Vehicles ◽  
Automated Driving ◽  
Level 3 ◽  
Traffic Systems ◽  
Consumer Devices ◽  
Positive Effect

Automated vehicles promise engagement in side activities, but demand drivers to resume vehicle control in Take-Over situations. This pattern of alternating tasks thus becomes an issue of sequential multitasking, and it is evident that random interruptions result in a performance drop and are further a source of stress/anxiety. To counteract such drawbacks, this article presents an attention-aware architecture for the integration of consumer devices in level-3/4 vehicles and traffic systems. The proposed solution can increase the lead time for transitions, which is useful to determine suitable timings (e.g., between tasks/subtasks) for interruptions in vehicles. Further, it allows responding to Take-Over-Requests directly on handheld devices in emergencies. Different aspects of the Attentive User Interface (AUI) concept were evaluated in two driving simulator studies. Results, mainly based on Take-Over performance and physiological measurements, confirm the positive effect of AUIs on safety and comfort. Consequently, AUIs should be implemented in future automated vehicles.

The Influence of Visual-Manual Distractions on Anticipatory Driving

2020 ◽  
pp. 001872082093889 ◽  
Author(s):  
Dengbo He ◽  
Birsen Donmez
Keyword(s):  
Road Safety ◽  
Secondary Task ◽  
Response Times ◽  
Negative Effects ◽  
Novice Drivers ◽  
Driving Experience ◽  
Vehicle Systems ◽  
Future Events ◽  
Recognition And Response

Objective The aim of this study is to investigate how anticipatory driving is influenced by distraction. Background The anticipation of future events in traffic can allow potential gains in recognition and response times. Anticipatory actions (i.e., control actions in preparation for potential traffic changes) have been found to be more prevalent among experienced drivers in simulator studies when driving was the sole task. Despite the prevalence of visual-manual distractions and their negative effects on road safety, their influence on anticipatory driving has not yet been investigated beyond hazard anticipation. Methods A simulator experiment was conducted with 16 experienced and 16 novice drivers. Half of the participants were provided with a self-paced visual-manual secondary task presented on a dashboard display. Results More anticipatory actions were observed among experienced drivers; experienced drivers also exhibited more efficient visual scanning behaviors as indicated by higher glance rates toward and percent times looking at cues that facilitate the anticipation of upcoming events. Regardless of experience, those with the secondary task displayed reduced anticipatory actions and paid less attention toward anticipatory cues. However, experienced drivers had lower odds of exhibiting long glances toward the secondary task compared to novices. Further, the inclusion of glance duration on anticipatory cues increased the accuracy of a model predicting anticipatory actions based on on-road glance durations. Conclusion The results provide additional evidence to existing literature supporting the role of driving experience and distraction engagement in anticipatory driving. Application These findings can guide the design of in-vehicle systems and guide training programs to support anticipatory driving.

The concept of driver takeover assessment in the vehicles with conditional automation

2018 ◽  
Vol 121 ◽  
pp. 319-327
Author(s):  
Paula Razin ◽  
Iwona Grabarek
Keyword(s):  
Information Transfer ◽  
Driving Simulator ◽  
Research Question ◽  
The Other ◽  
Automated Vehicles ◽  
Automated Driving ◽  
Preliminary Results ◽  
Other Hand ◽  
Significant Research ◽  
Research Stage

The article presents the concept of driver takeover assessment in the vehicles with conditional automation. The signals are used to inform the driver about the necessity to take over the control in automated vehicles when considering automated driving scenarios (e.g. highway chauffeur). The article presents preliminary results of the research concerning the efficiency of different modality signals. The research was carried out on multisensoric stand in driving simulator AS1200-6. Such research on one hand enabled the verification of the efficiency of multisensoric stand’s operation. On the other hand, it helped to answer a significant research question regarding the efficient way of communicating with a driver through the use of HMI interface, in order to minimize the time of taking over the control of the vehicle. Time necessary for taking over the control was one of the main analyzed parameters. Results of the test indicate that the effectiveness of information transfer depends on its form. The examinees achieved the best results when informed through visual and auditory interfaces (t = 3,84 s). Obtained results are going to serve to formulate the recommendations for automotive. The next research stage will be to analyze the maneuvers taken after taking over the control and the assessment of their correctness.

scholarly journals Investigating the Impacts of Road Traffic Conditions and Driver’s Characteristics on Automated Vehicle Takeover Time and Quality Using a Driving Simulator

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Jaehyun Jason So ◽  
Sungho Park ◽  
Jonghwa Kim ◽  
Jejin Park ◽  
Ilsoo Yun
Keyword(s):  
Driving Simulator ◽  
Road Traffic ◽  
Vehicle Control ◽  
Automated Vehicles ◽  
Automated Driving ◽  
Automated Vehicle ◽  
The Road ◽  
Traffic Conditions ◽  
Vehicle System ◽  
Driver’S Characteristics

This study investigates the impacts of road traffic conditions and driver’s characteristics on the takeover time in automated vehicles using a driving simulator. Automated vehicles are barely expected to maintain their fully automated driving capability at all times based on the current technologies, and the automated vehicle system transfers the vehicle control to a driver when the system can no longer be automatically operated. The takeover time is the duration from when the driver requested the vehicle control transition from the automated vehicle system to when the driver takes full control of the vehicle. This study assumes that the takeover time can vary according to the driver’s characteristics and the road traffic conditions; the assessment is undertaken with various participants having different characteristics in various traffic volume conditions and road geometry conditions. To this end, 25 km of the northbound road section between Osan Interchange and Dongtan Junction on Gyeongbu Expressway in Korea is modeled in the driving simulator; the experiment participants are asked to drive the vehicle and take a response following a certain triggering event in the virtual driving environment. The results showed that the level of service and road curvature do not affect the takeover time itself, but they significantly affect the stabilization time, that is, a duration for a driver to become stable and recover to a normal state. Furthermore, age affected the takeover time, indicating that aged drivers are likely to slowly respond to a certain takeover situation, compared to the younger drivers. With these findings, this study emphasizes the importance of having effective countermeasures and driver interface to monitor drivers in the automated vehicle system; therefore, an early and effective alarm system to alert drivers for the vehicle takeover can secure enough time for stable recovery to manual driving and ultimately to achieve safety during the takeover.

Trust in Automation as a Matter of Media Influence and Experi-ence of Automated Vehicles

2016 ◽  
Vol 60 (1) ◽  
pp. 2024-2028 ◽  
Author(s):  
Anna Feldhütter ◽  
Christian Gold ◽  
Adrian Hüger ◽  
Klaus Bengler
Keyword(s):  
Road Safety ◽  
Driving Simulator ◽  
Intention To Use ◽  
Automated Vehicles ◽  
Automated Driving ◽  
Trust In Automation ◽  
Different Types ◽  
The Media ◽  
Source Of Information ◽  
Highly Automated Driving

Highly automated vehicles (HAV), which could help to enhance road safety and efficiency, are very likely to enter the market within the next decades. To have an impact, these systems need to be purchased, which is a matter of trust and acceptance. These factors are dependent on the level of information that one has about such systems. One important source of information is various media, such as newspapers, magazines and videos, in which highly automated driving (HAD) is currently a frequent topic of discussion. To evaluate the influence of media on the perception of HAD, 31 participants were presented with three different types of media addressing HAD in a neutral manner. Afterwards, the participants experienced HAD in the driving simulator. In between these steps, the participants completed questionnaires assessing comfort, trust in automation, increase in safety, intention to use and other factors in order to analyze the effect of the media and the driving simulation experience. Results indicate that the perception of some aspects of HAD were affected by the media presented, while experiencing HAD in the driving simulator generally did not have an effect on the attitude of the participants. Other aspects, such as trust, were not affected by either media or experience. In addition, gender-related differences in the perception of HAD were found.

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