Automated driving has the potential to improve the safety and efficiency of future traffic and to extend elderly peoples' driving life, provided it is perceived as comfortable and joyful and is ...accepted by drivers. Driving comfort could be enhanced by familiar automated driving styles based on drivers' manual driving styles. In a two-stage driving simulator study, effects of driving automation and driving style familiarity on driving comfort, enjoyment and system acceptance were examined. Twenty younger and 20 older drivers performed a manual and four automated drives of different driving style familiarity. Acceptance, comfort and enjoyment were assessed after driving with standardised questionnaires, discomfort during driving via handset control. Automation increased both age groups' comfort, but decreased younger drivers' enjoyment. Younger drivers showed higher comfort, enjoyment and acceptance with familiar automated driving styles, whereas older drivers preferred unfamiliar, automated driving styles tending to be faster than their age-affected manual driving styles.
Practitioner Summary: Automated driving needs to be comfortable and enjoyable to be accepted by drivers, which could be enhanced by driving style individualisation. This approach was evaluated in a two-stage driving simulator study for different age groups. Younger drivers preferred familiar driving styles, whereas older drivers preferred driving styles unaffected by age.
As technological advances lead to rapid progress in driving automation, human-machine interaction (HMI) issues such as comfort in automated driving gain increasing attention. The research project ...KomfoPilot at Chemnitz University of Technology aims to assess discomfort in automated driving using physiological parameters from commercially available smartbands, pupillometry and body motion. Detected discomfort should subsequently be used to adapt driving parameters as well as information presentation and prevent potentially safety-critical take-over situations. In an empirical driving simulator study, 40 participants from 25 years to 84 years old experienced two highly automated drives with three potentially critical and discomfort-inducing approaching situations in each trip. The ego car drove in a highly automated mode at 100 km/h and approached a truck driving ahead with a constant speed of 80 km/h. Automated braking started very late at a distance of 9 m, reaching a minimum of 4.2 m. Perceived discomfort was assessed continuously using a handset control. Physiological parameters were measured by the smartband Microsoft Band 2 and included heart rate (HR), heart rate variability (HRV) and skin conductance level (SCL). Eye tracking glasses recorded pupil diameter and eye blink frequency; body motion was captured by a motion tracking system and a seat pressure mat. Trends of all parameters were analyzed 10 s before, during and 10 s after reported discomfort to check for overall parameter relevance, direction and strength of effects; timings of increase/decrease; variability as well as filtering, standardization and artifact removal strategies to increase the signal-to-noise ratio. Results showed a reduced eye blink rate during discomfort as well as pupil dilation, also after correcting for ambient light influence. Contrary to expectations, HR decreased significantly during discomfort periods, whereas HRV diminished as expected. No effects could be observed for SCL. Body motion showed the expected pushback movement during the close approach situation. Overall, besides SCL, all other parameters showed changes associated with discomfort indicated by the handset control. The results serve as a basis for designing and configuring a real-time discomfort detection algorithm that will be implemented in the driving simulator and validated in subsequent studies.
•Younger and older drivers consider driving automation trustworthy and acceptable.•The initial system experience significantly increases trust and acceptance.•After the initial system experience, ...trust and acceptance remain on a stable level.•Especially older drivers show a positive attitude towards driving highly automated.•Age-specific acceptance barriers regarding automotive technologies are identified.
Highly automated driving (HAD) is expected to improve future road transport, especially for older adults, provided that it is trusted and accepted by drivers. Research on Advanced Driver Assistance Systems (ADAS) suggests that system experience can enhance drivers’ trust and acceptance. To evaluate the transferability of this result to HAD, we examined the development of drivers’ trust and acceptance regarding this technology at different stages of system experience in a driving simulator as well as on a test track. Age effects were additionally addressed by comparing the results of 20 younger (25–45 years) and 20 older (65–85 years) drivers in the driving simulator study. Trust and acceptance were assessed before the initial system experience as well as after the first and second automated drive. Both age groups showed slightly positive a priori trust and acceptance ratings, which significantly increased after the initial experience and remained stable afterwards. Older drivers reported a more positive attitude towards using HAD despite their lower self-assessed self-efficacy and environmental conditions facilitating HAD-usage (e.g. technical support) compared to younger drivers. In the subsequent test track study, trust and acceptance of the younger driver group were assessed before and after experiencing HAD in a test vehicle. Neither trust nor acceptance decreased despite the absence of further system experiences between both studies and the increased realism on the test track. These results underline the importance of the initial system experience for HAD-trust and –acceptance and emphasize the significance of automotive technologies for the preservation of older drivers’ mobility.
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.
To facilitate the usage and expected benefits of higher-level automated vehicles, passengers’ distrust and safety concerns should be reduced through increasing system transparency (ST) by providing ...driving-related information. We therefore examined the effects of ST on passengers’ gaze behavior during driving, trust in automated driving and evaluation of different human-machine interface (HMI) concepts. In a driving simulator, 50 participants experienced three identical highly automated drives under three HMI conditions: no HMI (only conventional speedometer), context-adaptive HMI (all system information only available in more complex situations) or permanent HMI (all system information permanently available). Compared to driving without HMI, the introduction of the two HMIs resulted in significantly higher usage of the center stack display (i.e. gazes towards the HMIs), which was accompanied by significantly higher trust ratings. The considerable differences in information availability provided by the context-adaptive versus permanent HMI did not reflect in similarly considerable differences regarding the passengers’ gaze behavior or accompanied trust ratings. Additionally, user experience evaluations expressed preferences for the context-adaptive HMI. Hence, the permanent HMI did not seem to create benefits over the context-adaptive HMI, supporting the usage of more economical, context-adaptive HMIs in higher-level automated vehicles.
The quickly rising development of autonomous vehicle technology and increase of (semi-) autonomous vehicles on the road leads to an increased demand for more sophisticated human–machine-cooperation ...approaches to improve trust and acceptance of these new systems. In this work, we investigate the feeling of discomfort of human passengers while driving autonomously and the automatic detection of this discomfort with several model approaches, using the combination of different data sources. Based on a driving simulator study, we analyzed the discomfort reports of 50 participants for autonomous inner city driving. We found that perceived discomfort depends on the driving scenario (with discomfort generally peaking in complex situations) and on the passenger (resulting in interindividual differences in reported discomfort extend and duration). Further, we describe three different model approaches on how to predict the passenger discomfort using data from the vehicle’s sensors as well as physiological and behavioral data from the passenger. The model’s precision varies greatly across the approaches, the best approach having a precision of up to 80%. All of our presented model approaches use combinations of linear models and are thus fast, transparent, and safe. Lastly, we analyzed these models using the SHAP method, which enables explaining the models’ discomfort predictions. These explanations are used to infer the importance of our collected features and to create a scenario-based discomfort analysis. Our work demonstrates a novel approach on passenger state modelling with simple, safe, and transparent models and with explainable model predictions, which can be used to adapt the vehicles’ actions to the needs of the passenger.
When it comes to climate change, automated vehicles (AV) are often presented as a key factor to reducing emissions related with the transport sector. While studies promise emissions savings of up to ...80%, it is often overlooked how AVs will be introduced and which transportation mode changes will arise from their implementation. Therefore, this online survey examined usage intentions regarding private and shared AV types, and underlying attitudes and mobility needs of 136 current users of different main modes of transport. Two main results counteract the general assumption of ecological sustainability benefits of AVs: First, current car drivers prefer private over shared AV types, even though notable sustainability gains can only be expected from shared AVs. Second, current users of more sustainable modes of transport (walking, bike, public transport) would replace theses modes by AVs for substantial shares of their trips, which represents a behavioural rebound effect, since AVs cannot be more sustainable than walking or biking. Group-specific mobility needs and knowledge gaps regarding the sustainability of different AV types are identified as reasons for these results and as starting points for deriving necessary measures accompanying the introduction of AVs into society through motivating ecologically sustainable transportation mode changes.
•Continuous assessment of discomfort in automated driving using a handset control.•Decrease of heart rate during uncomfortable situations.•Pupil dilation and reduction of eye blinks during ...uncomfortable situations.•No specific effects for electrodermal activity, may related to smartband.
Comfort in automated driving is considered a key issue for broad acceptance of automated vehicles. The research project KomfoPilot at Chemnitz University of Technology aimed to investigate factors that influence comfort in automated driving as well as to identify physiological indicators of discomfort. In an empirical driving simulator study, 40 participants from 25 to 84 years old experienced three highly automated trips including six potentially uncomfortable situations in each trip. Participants reported perceived discomfort continuously by a handset control. The physiological parameters Heart Rate (HR) and Electrodermal Activity (EDA) were assessed using the smartband Microsoft Band 2; pupil diameter and eye blinks were measured by the SMI Eye Tracking Glasses 2. Results showed specific physiological reactions in situations that provoked moderate to high discomfort. Longer lasting and slowly evolving situations with lower reported discomfort did not show associated changes in physiological parameters. HR decreased consistently during uncomfortable situations, which could be related to the phenomenon “preparation for action”. Pupil diameter increased and eye blink rate decreased in uncomfortable situations that were visually monitored. EDA did not show specific effects, which, however could be attributed to measurement procedures of the smartband. The results serve as a basis for developing a real-time discomfort detection algorithm and will additionally be validated on-road.
With highly automated vehicles (HAVs), the human role shifts from active driver to mostly passive passenger, which has an impact on the perceived comfort while being driven. A number of factors need ...to be taken into account when designing an automated driving style (ADS) with the aim to minimize human discomfort. In this paper, the authors build on seven empirical studies to determine first, if a generally comfortable ADS exists and second, whether such an ADS should be designed depending on situational driving dynamics or individual user characteristics such as users' manual driving styles. The results give an overview of the ADS- and user-related factors causing human discomfort in automated driving and provide potential strategies to overcome potential discomfort. Drawing on the data, upcoming challenges in the field of human discomfort in HAVs for both research and practice are presented.
Zusammenfassung
Die Veränderung der
Fahrerrolle im hochautomatisierten Fahren vom Akteur zum „Passagier am Fahrersitz“ wirft neue Fragen auf nach dem erlebten
Fahrkomfort in dieser veränderten ...Situation. In
einer kombinierten Fahrsimulator- und Realfahrstudie
wurde der Einfluss von Fahrermerkmalen auf den wahrgenommenen
Fahrkomfort, die Akzeptanz und den Fahrspaß im
hochautomatisierten Fahren untersucht.
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