2. Driving Comfort
Driving comfort is an essential component to fulfill customer expectations
[27][28][27,28]. However, until today, there is no unique definition of comfort
[29]. Whereas some studies see comfort and discomfort as two end poles on a single dimension
[30][31][30,31], others interpret both as independent constructs that can be experienced simultaneously
[32][33][32,33]. Although no universal definition of comfort exists, Ref.
[33] has summarized three aspects of comfort that are commonly shared among a variety of definitions: (1) comfort is subjective and individual; (2) both internal and external factors have an impact on individually experienced comfort; (3) comfort is experienced as a reaction to the environment.
In addition to the absence of a common definition, measuring experienced comfort poses another significant challenge in comfort-related research. In the past, different methods have been introduced to measure comfort. In
[34][35][34,35], comfort is measured via physiological measures such as heart rate, electrodermal activity, galvanic skin response, and others, whereas
[36][37][36,37] measures the discomfort via a hand controller. The usage of questionnaires is another approach that is often taken in various studies to measure comfort
[38][39][38,39].
3. Driving Style
In the context of conditional automated driving, the driving style and strategy will become increasingly important for experienced comfort and safety as the driver will have limited predictability of the AV’s trajectory and control over the vehicle guidance
[10][36][40][10,36,40]. This has also been described as a “loss of controllability”
[4]. So far, no universal definition of driving style exists
[41]. However, Ref.
[41] has summarized three aspects of a driving style that are commonly shared among various definitions: (1) driving styles are relatively stable and habitual; (2) driving styles differ across groups and individuals; (3) driving styles reflect conscious decisions made by the driver.
A common approach to describe a driving style is to use objective parameters such as vehicle kinematics and environment context. In the past, a variety of parameter representations have been introduced, such as time series data
[3][42][3,42], statistical characteristics
[7][43][44][7,43,44], or latent variables, representing a dimensionless combination of different parameter types
[9][45][46][9,45,46]. In order to differentiate between various driving styles, classification techniques such as manual or data-driven methods are commonly applied. These techniques typically involve analyzing value ranges or data patterns, which are then followed by subjective categorization using terms such as “defensive”, “moderate”, or “aggressive”
[41].
Driving styles can also be differentiated based on their strategies for direct vehicle control, interaction with other traffic participants, or behavior in specific environments, as discussed in various studies
[47]. For instance, different deceleration profiles for approaching a slower lead vehicle have been presented as examples of different strategies used for vehicle control
[10][36][10,36]. In another study, the behavior of an autonomous vehicle affected whether a cut-in vehicle changed lanes in front of or behind the autonomous vehicle
[7] as an example of different strategies for cooperation. In the case of specific environments, Ref.
[21] presented different approaches for passing through an intersection. One strategy involves cautious, human-like behavior, whereas the other involves machine-like behavior that conveys a sense of prior knowledge about the environment and other oncoming road users.
Many studies suggest that human-like driving behavior will increase user trust, acceptance, predictability, and ride comfort and safety
[18][22][48][18,22,48]. Human-like driving can be learned from large naturalistic data sets
[13][49][13,49], expert demonstration
[14][16][14,16], or random subject collectives
[50]. A slightly different approach to learning a driving style was taken by
[15], specifically asking participants to demonstrate their desired driving style for an automated vehicle through manual driving instead of recording their everyday driving style. The aim of human-like driving styles is to incorporate common driving conventions of human drivers, including in-lane positioning in curves
[51], distance regulation for car-following
[17], crossing behavior for intersections
[52], and others. The use of virtual agents in AVs with human-like features such as voice and appearance
[22][53][54][55][22,53,54,55] is an additional approach to create an even stronger impression of a human-like automated vehicle, which is also referred to as anthropomorphism in the literature.
Preferences for automated driving have been analyzed for different maneuvers such as lane changes
[8][10][11][36][43][8,10,11,36,43], car-following
[56][57][58][59][56,57,58,59], decelerating to a slower lead vehicle
[10][36][10,36], or across different maneuvers and scenarios
[6][7][25][26][6,7,25,26]. A common approach by previous studies in this context is to let users experience a predefined driving style in comparison with other driving styles or their own prerecorded driving style, using driving simulators. A Wizard-of-Oz setup was used by
[6][36][6,36]. The amount of studies investigating driving style preferences for automated driving is considered small. In addition, studies differ in terms of driving environments such as urban, rural, or motorways and whether preferences are evaluated in relation to
[5][6][7][8][9][5,6,7,8,9] or independently of participants’ personal driving style
[10][11][36][43][10,11,36,43], making it yet difficult to derive a clear picture of how users want to be driven in an automated vehicle.
With regard to age-related differences, Refs.
[3][60][61][3,60,61] have investigated how age affects the experience of different automated driving styles for various maneuvers on rural and motorway roads. All studies involved participants experiencing their own driving style and up to two predefined automated driving styles. Ref.
[3] found that both younger and older participants preferred the driving style of a younger driver, whereas younger drivers in
[60] preferred a defensive driving style, showing the greatest rejection of their own driving style in terms of safety and comfort. Results of
[61] show that older drivers prefer their own style when compared to a more aggressive automated driving style.
4. Trust
Trust has proven to be a critical factor for the acceptance of automated vehicles
[24][62][63][24,62,63]. Without appropriate levels of trust, users may not feel comfortable using and relying on driverless technology, preventing them from fully taking advantage of its potential benefits
[64][65][66][64,65,66]. Similar to the terms comfort and driving style, various definitions exist for trust as it is seen as a multidimensional concept. However, Ref.
[62] has summarized three aspects that are commonly shared among a variety of definitions: (1) trust is given by a truster to a trustee with something at stake; (2) in order for the trustee to perform the task, an incentive must exist. In the case of technology, the designer’s intended use of the system serves as the primary incentive for the trustee; (3) trust involves the possibility of the trustee failing to perform the task, leading to uncertainty and risk. Trust is commonly assessed using questionnaires
[7][10][21][7,10,21]. Ref.
[26], on the other hand, measured trust via acceleration and brake pedal inputs by the user, expressing their dissatisfaction with the driving style.
Various studies investigated which factors affect users’ trust in an automated vehicle and how it can be increased. References
[67][68][69][67,68,69] focused on how information displayed to the driver can help to increase trust, whereas
[7][10][21][26][70][7,10,21,26,70] analyzed the influence of driving styles on users’ trust, all coming to different conclusions. Whereas the simulator study results of
[7][26][7,26] suggest that automated driving styles that align with the user’s personal driving style increase trust, no influence of driving style on trust was found in
[10][21][10,21]. However, it has been observed that trust can increase over time with repeated experience of an AV
[21][62][71][72][21,62,71,72].