scholarly journals Improving Passenger Experience and Trust in Automated Vehicles Through User-Adaptive HMIs: “The More the Better” Does Not Apply to Everyone

2021 ◽  
Vol 3 ◽  
Author(s):  
Franziska Hartwich ◽  
Cornelia Hollander ◽  
Daniela Johannmeyer ◽  
Josef F. Krems
Keyword(s):  
User Experience ◽  
Driving Simulator ◽  
Individual Characteristics ◽  
User Requirements ◽  
Automated Vehicles ◽  
Automated Driving ◽  
Perceived Safety ◽  
Information Availability ◽  
Related Information ◽  
Expected Benefits

Automated vehicles promise transformational benefits for future mobility systems, but only if they will be used regularly. However, due to the associated loss of control and fundamental change of in-vehicle user experience (shifting from active driver to passive passenger experience), many humans have reservations toward driving automation, which question their sufficient usage and market penetration. These reservations vary based on individual characteristics such as initial attitudes. User-adaptive in-vehicle Human-Machine Interfaces (HMIs) meeting varying user requirements may represent an important component of higher-level automated vehicles providing a pleasant and trustworthy passenger experience despite these barriers. In a driving simulator study, we evaluated the effects of two HMI versions (with permanent vs. context-adaptive information availability) on the passenger experience (perceived safety, understanding of driving behavior, driving comfort, driving enjoyment) and trust in automated vehicles of 50 first-time users with varying initial trust (lower vs. higher trust group). Additionally, we compared the user experience of both HMIs. Presenting driving-related information via HMI during driving improved all assessed aspects of passenger experience and trust. The higher trust group experienced automated driving as safest, most understandable and most comfortable with the context-adaptive HMI, while the lower trust group tended to experience the highest safety, understanding and comfort with the permanent HMI. Both HMIs received positive user experience ratings. The context-adaptive HMI received generally more positive ratings, even though this preference was more pronounced for the higher trust group. The results demonstrate the potential of increasing the system transparency of higher-level automated vehicles through HMIs to enhance users’ passenger experience and trust. They also consolidate previous findings on varying user requirements based on individual characteristics. User group-specific HMI effects on passenger experience support the relevance of user-adaptive HMI concepts addressing varying needs of different users by customizing HMI features, such as information availability. Consequently, providing full information permanently cannot be recommended as a universal standard for HMIs in automated vehicles. These insights represent next steps toward a pleasant and trustworthy passenger experience in higher-level automated vehicles for everyone, and support their market acceptance and thus the realization of their expected benefits for future mobility and society.

First encounter effects in testing of highly automated vehicles during two experimental occasions – The need for recurrent testing

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Jonas Andersson ◽  
Azra Habibovic ◽  
Daban Rizgary
Keyword(s):  
Driver Behavior ◽  
Added Value ◽  
Future Research ◽  
Gaze Behavior ◽  
Repeated Testing ◽  
Automated Vehicles ◽  
Automated Driving ◽  
Perceived Safety ◽  
Repeated Interaction ◽  
Over Time

Abstract To explore driver behavior in highly automated vehicles (HAVs), independent researchers are mainly conducting short experiments. This limits the ability to explore drivers’ behavioral changes over time, which is crucial when research has the intention to reveal human behavior beyond the first-time use. The current paper shows the methodological importance of repeated testing in experience and behavior related studies of HAVs. The study combined quantitative and qualitative data to capture effects of repeated interaction between drivers and HAVs. Each driver ( n = 8 n=8 ) participated in the experiment on two different occasions (∼90 minutes) with one-week interval. On both occasions, the drivers traveled approximately 40 km on a rural road at AstaZero proving grounds in Sweden and encountered various traffic situations. The participants could use automated driving (SAE level 4) or choose to drive manually. Examples of data collected include gaze behavior, perceived safety, as well as interviews and questionnaires capturing general impressions, trust and acceptance. The analysis shows that habituation effects were attenuated over time. The drivers went from being exhilarated on the first occasion, to a more neutral behavior on the second occasion. Furthermore, there were smaller variations in drivers’ self-assessed perceived safety on the second occasion, and drivers were faster to engage in non-driving related activities and become relaxed (e. g., they spent more time glancing off road and could focus more on non-driving related activities such as reading). These findings suggest that exposing drivers to HAVs on two (or more) successive occasions may provide more informative and realistic insights into driver behavior and experience as compared to only one occasion. Repeating an experiment on several occasions is of course a balance between the cost and added value, and future research should investigate in more detail which studies need to be repeated on several occasions and to what extent.

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.

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 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.

scholarly journals Travel experience matters: Expected personal mobility impacts after simulated L3/L4 automated driving

2021 ◽  
Author(s):  
Esko Lehtonen ◽  
Johanna Wörle ◽  
Fanny Malin ◽  
Barbara Metz ◽  
Satu Innamaa
Keyword(s):  
Driving Simulator ◽  
Stated Preference ◽  
Travel Times ◽  
Automated Vehicles ◽  
Automated Driving ◽  
Personal Mobility ◽  
Level Of Automation ◽  
Travel Experience ◽  
Gradual Development ◽  
Final Drive

AbstractAutomated vehicles (AVs) are expected to change personal mobility in the near future. Most studies on the mobility impacts of AVs focus on fully automated (SAE L5) vehicles, but the gradual development of the technology will probably bring AVs with more limited capabilities to begin with. This stated-preference study focused on the potential mobility impacts of conditionally automated (L3) and highly automated cars (L4). We investigated personal mobility impacts among 59 participants who experienced automated driving repeatedly in a driving simulator. Half of them drove with an L3 and half with an L4 motorway function. After the first and final drive they answered questions on their travel experience and how automated vehicles could change their mobility. After the drives, participants in both groups were willing to accept 30–50% longer travel times for a 30 min trip if they did not need to drive the whole trip themselves. This translates into savings of around 30% for the perceived value of travel time on routes where automation is available. There were no statistically significant differences between L3 and L4 in the accepted travel times. Most participants did not expect to make more trips with automated cars, but around half of them anticipated making longer trips. The amount of car travel may increase more with L4 than with L3 automation, possibly due somewhat to changes in the experienced travel quality. The results suggest that the mobility impacts of automated driving may increase with a higher level of automation.

scholarly journals Mitigating Motion Sickness in Automated Vehicles with Vibration Cue System

2022 ◽  
Author(s):  
Daofei Li ◽  
Linhui Chen
Keyword(s):  
Motion Sickness ◽  
Driving Simulator ◽  
Ride Comfort ◽  
System Development ◽  
Road Transport ◽  
Automated Vehicles ◽  
Automated Driving ◽  
User Friendliness ◽  
Practical Applications ◽  
Vehicle Movement

<p>Motion sickness is very common in road transport. To guarantee ride comfort and user experience, there is an urgent need for effective solutions to motion sickness mitigation in semi- and fully-automated vehicles. Considering both effectiveness and user-friendliness, a vibration cue system is proposed to inform passengers of the upcoming vehicle movement through tactile stimulation. By integrating the motion planning results from automated driving algorithms, the vibration cueing timing and patterns are optimized with the theory of motion anticipation. Using a cushion-based prototype of vibration cue system, 20 participants were invited to evaluate this solution in two conditions of driving simulator experiments. Results show that with the proposed vibration cue system, it could also help participants to comprehend the cues and to generate motion anticipation. The participants’ motion sickness degrees were significantly lowered. This research may serve as one foundation for the detailed system development in practical applications.</p><p>(This article has been accepted for publication in <i>Ergonomics</i>, published by Taylor & Francis.)</p><br>

Novel Auditory Displays in Highly Automated Vehicles: Sonification Improves Driver Situation Awareness, Perceived Workload, and Overall Experience

2021 ◽  
Vol 65 (1) ◽  
pp. 586-590
Author(s):  
Chihab Nadri ◽  
Sangjin Ko ◽  
Colin Diggs ◽  
Michael Winters ◽  
V. K. Sreehari ◽  
...  
Keyword(s):  
Situation Awareness ◽  
Driving Simulator ◽  
Visual Display ◽  
Use Cases ◽  
Automated Vehicles ◽  
Automated Driving ◽  
Auditory Displays ◽  
User Perceptions ◽  
Young Drivers ◽  
Group Interviews

Highly automated driving systems are expected to require the design of new user-vehicle interactions. Sonification can be used to provide contextualized alarms and cues that can increase situation awareness and user experience. In this study, we examined user perceptions of potential use cases for level 4 automated vehicles in online focus group interviews (N=12). Also, in a driving simulator study, we evaluated (1) visual-only display; (2) non-speech with visual display; and (3) speech with visual display with 20 young drivers. Results indicated participants’ interest in the use cases and insight on desired functions in highly automated vehicles. Both audiovisual display conditions resulted in higher situation awareness for drivers than the visual-only condition. Some differences were found between the non-speech and speech conditions suggesting benefits of sonification for both driving and non-driving related auditory use cases. This study will provide guidance on sonification design for highly automated vehicles.

Assessing Drivers’ Trust of Automated Vehicle Driving Styles With a Two-Part Mixed Model of Intervention Tendency and Magnitude

2019 ◽  
pp. 001872081988036 ◽  
Author(s):  
John D. Lee ◽  
Shu-Yuan Liu ◽  
Joshua Domeyer ◽  
Azadeh DinparastDjadid
Keyword(s):  
Mixed Model ◽  
Driving Simulator ◽  
Statistical Technique ◽  
Brake Pedal ◽  
Automated Vehicles ◽  
Automated Driving ◽  
Accelerator Pedal ◽  
Driving Style ◽  
Automated Vehicle ◽  
Stop Sign

Objective: This study examines how driving styles of fully automated vehicles affect drivers’ trust using a statistical technique—the two-part mixed model—that considers the frequency and magnitude of drivers’ interventions. Background: Adoption of fully automated vehicles depends on how people accept and trust them, and the vehicle’s driving style might have an important influence. Method: A driving simulator experiment exposed participants to a fully automated vehicle with three driving styles (aggressive, moderate, and conservative) across four intersection types (with and without a stop sign and with and without crossing path traffic). Drivers indicated their dissatisfaction with the automation by depressing the brake or accelerator pedals. A two-part mixed model examined how automation style, intersection type, and the distance between the automation’s driving style and the person’s driving style affected the frequency and magnitude of their pedal depression. Results: The conservative automated driving style increased the frequency and magnitude of accelerator pedal inputs; conversely, the aggressive style increased the frequency and magnitude of brake pedal inputs. The two-part mixed model showed a similar pattern for the factors influencing driver response, but the distance between driving styles affected how often the brake pedal was pressed, but it had little effect on how much it was pressed. Conclusion: Eliciting brake and accelerator pedal responses provides a temporally precise indicator of drivers’ trust of automated driving styles, and the two-part model considers both the discrete and continuous characteristics of this indicator. Application: We offer a measure and method for assessing driving styles.

Sign in / Sign up

Export Citation Format

Share Document