Driver acceptance of partial automation after a brief exposure (original) (raw)
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2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC)
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Frontiers in Psychology
IntroductionThe potential safety benefits of advanced driver assistance systems (ADAS) highly rely on drivers’ appropriate mental models of and trust in ADAS. Current research mainly focused on drivers’ mental model of adaptive cruise control (ACC) and lane centering control (LCC), but rarely investigated drivers’ understanding of emerging driving automation functions beyond ACC and LCC.MethodsTo address this research gap, 287 valid responses from ADAS users in the Chinese market, were collected in a survey study targeted toward state-of-the-art ADAS (e.g., autopilot in Tesla). Through cluster analysis, drivers were clustered into four groups based on their knowledge of traditional ACC and LCC functions, knowledge of functions beyond ACC and LCC, and knowledge of ADAS limitations. Predictors of driver grouping were analyzed, and we further modeled drivers’ trust in ADAS.ResultsDrivers in general had weak knowledge of LCC functions and functions beyond ACC and LCC, and only 27 (9%) o...
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As vehicle driving evolves from human-controlled to autonomous, human–machine interaction ensures intuitive usage as well as the feedback from vehicle occupants to the machine for optimising controls. The feedback also improves understanding of the user satisfaction with the system behaviour, which is crucial for determining user trust and, hence, the acceptance of the new functionalities that aim to improve mobility solutions and increase road safety. Trust and acceptance are potentially the crucial parameters for determining the success of autonomous driving deployment in wider society. Hence, there is a need to define appropriate and measurable parameters to be able to quantify trust and acceptance in a physically safe environment using dependable methods. This study seeks to support technical developments and data gathering with psychology to determine the degree to which humans trust automated driving functionalities. The primary aim is to define if the usage of an advanced dri...
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If previous research studied acceptability of partially or highly automated driving, few of them focused on fully automated driving (FAD), including the ability to master longitudinal control, lateral control and maneuvers. The present study analyzes a priori acceptability, attitudes, personality traits and intention to use a fully automated vehicle. 421 French drivers (153 males, M= 40.2 years, age range 19-73) answered an online questionnaire. 68.1% of the sample a priori accepted FAD. Predictors of intention to use a fully automated car (R²= .671) were mainly attitudes, contextual acceptability and interest in impaired driving (i.e. the two components of FAD acceptability), followed by driving related sensation seeking, finally gender. FAD preferred use cases were on highways, in traffic congestion and for automatic parking. Furthermore, some drivers reported interest in impaired driving misuses, despite awareness of their responsibility for both the vehicle and the driving. These results are discussed regarding previous knowledge about acceptability of advanced driving assistance systems and consequences for the use of fully automated cars.
Technical Report IMS / Department of Industrial and Material Science ;, 2020
The automotive industry is rapidly developing driving automation systems (DAS) with the aim of supporting drivers through automation of longitudinal and lateral vehicle control. As vehicle complexity increases, drivers' understanding of their responsibility and their vehicles' capabilities and limitations becomes significantly more important. In order to motivate manufacturers to adopt a human-centric perspective for the development of driving automation systems, the factors influencing the driver's perception during usage of such systems have to be understood. Therefore, the aim of this thesis is to contribute to the understanding of factors influencing user perception and understanding of driving automation systems in order to guide future design decisions from a human-centric perspective. The research for this thesis is organised into three empirical studies, embedding a mixedmethods research design. Study 1 aimed at investigating usage of DAS during different driving situations by facilitating an online survey. Studies 2 and 3 aimed to explore how drivers motivate their usage of driving automation systems, and which factors affect their understanding. Study 2 adopted an Explanatory Sequential Mixed Methods approach, consisting of a Naturalistic Driving Study and in-depth interviews to elicit knowledge about how users understand the DAS, and which factors influence usage. In Study 3 observations and interviews during an on-road driving session with a Wizard-of-Oz vehicle were conducted to gain insights into how users build an understanding of a vehicle with multiple levels of automation. The results show that the users of such systems, independent of the level of automation, talked about the systems by referring to different elements: the Context, the Vehicle, and the Driver. In addition, eleven recurring aspects describing the drivers' understanding of an automated system were discerned. Furthermore, six factors were identified that influence how drivers perceive driving automation during usage. The six factors are Preconceptions, Perceived Usefulness, Previous Experiences, Trust, System Performance, and Driving Behaviour of the Vehicle. Collectively, the identified aspects and factors constitute the building blocks of a process describing how drivers perceive driving automation systems and how this shapes their consequent understanding. The process is presented as a descriptive unified model. The main contribution of this thesis is twofold: unification of aspects found to shape a driver's understanding of a driving automation system, and the presentation of a unified descriptive model of the process showing how this understanding is shaped through what the driver perceives at the moment of use.
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Appropriate user trust is critical in ensuring the acceptance and safe use of Advanced Driver Assistance Systems (ADAS). Despite the prevalence of ADAS on-road today, there is a limited understanding of how trust is affected by a user's first contact with the system on-road. Ten participants without prior experience were introduced to a level 2 system and completed an on-road test drive session. Utilizing a mixedmethods approach including the Trust in Automation (TiA) questionnaire, verbal trust scores, and Facial Emotion Recognition (FER), trust in the system was measured at key milestones. TiA scores increased in a majority of participants, and a significant shift in the factor Reliability/Competence (p<0.05) was observed post-drive. According to FER scores, participants with a gain in TiA post-drive and those with a loss in TiA post-drive, more frequently displayed the emotions happy and angry, respectively. Results indicate that trust increases after a user's first experience with ADAS and further that FER may be predictive of user trust in automation.
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Advanced driver assistance systems (ADAS) are often used in the automotive industry to highlight innovative improvements in vehicle safety. However, today it is unclear whether certain automation (e.g., adaptive cruise control, lane keeping, parking assist) increases safety of our roads. In this paper, we investigate driver awareness, use, perceived safety, knowledge, training, and attitudes toward ADAS with different automation systems/features. Results of our online survey (n=1018) reveal that there is a significant difference in frequency of use and perceived safety for different ADAS features. Furthermore, we find that at least 70% of drivers activate an ADAS feature “most or all of the time” when driving, yet we find that at least 40% of drivers report feeling that ADAS often compromises their safety when activated. We also find that most respondents learn how to use ADAS in their vehicles by trying it out on the road by themselves, rather than through any formal driver educati...
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Engaging With Highly Automated Driving: To Be Or Not To Be In The Loop?
This desktop driving simulator study investigated the effect of engagement in a reading task during vehicle automation on drivers' ability to resume manual control and successfully avoid an impending collision with a stationary vehicle. To avoid collision, drivers were required to regain control of the automated vehicle and change lane. The decision-making element of this lane change was manipulated by asking drivers to move into the lane they saw fit (left or right) or to use the colour of the stationary vehicle as a rule (blue – left, red – right). Drivers' reaction to the stationary vehicle in manual control was compared to two automation conditions: (i) when drivers were engaged and observing the road during automation, and (ii) when they were reading a piece of text on an iPad during automation. Overall, findings suggest that drivers experiencing automation were slower to identify the potential collision scenario, but once identified, the collision was evaded more errat...
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