Rearward Seating Orientation Decreases Trust and Increases Motion Sickness in Autonomous Vehicles

Abstract

As the development of autonomous vehicles (AVs) progresses, new seating arrangements are emerging. Face-to-face seating is common in SAE L4 AV concepts and is intended to facilitate social interaction during autonomous driving, enabling previously unfeasible non-driving related tasks (NDRTs). However, this is countered by the unpopularity of rearward seating orientations, which is particularly pronounced in cars. In order to develop countermeasures to address this unpopularity, a deeper understanding of the underlying mechanisms is required. This study validates a model that predicts the acceptance of AVs and takes seating orientation into account. To this end, a study with N = 46 participants was conducted to investigate the influence of seating orientation on AV acceptance and related factors such as transparency, trust, and motion sickness. Additionally, internal human–machine interfaces (iHMIs) were evaluated in regard to their ability to compensate for the disadvantages of a rearward seating orientation. To achieve a realistic implementation of a fully functional SAE L4 AV, an experimental vehicle was equipped with a steering and pedal robot, performing self-driven journeys on a test track. The iHMIs provided information about upcoming maneuvers and detected road users. While engaged in a social NDRT, participants experienced a total of six journeys. Seating orientation and iHMI visualization were manipulated between journeys. Rearward-facing passengers showed lower levels of trust and higher levels of motion sickness than forward-facing passengers. However, the iHMIs had no effect on acceptance or related factors. Based on these findings, an updated version of the model is proposed, showing that rearward-facing passengers in autonomous vehicles pose a particular challenge for trust calibration and motion sickness mitigation. During NDRTs, iHMIs which depend on the attention of AV occupants for information transfer appear to be ineffective. Implications for future research and design of iHMIs to address this challenge are discussed.

1. Introduction

Autonomous vehicles are expected to bring various benefits, such as increased safety and comfort. In addition, one of the main benefits is the possibility of using driving time for NDRTs, for example, for work or leisure activities. In order to better exploit the potential of possible NDRTs, innovative seating arrangements are being developed, particularly for fully automated vehicles (SAE Level 4, SAE International, 2016). One such arrangement which has been incorporated into various concept vehicles is rotating front seats (Mercedes-Benz F 015, Volkswagen SEDRIC, Yanfeng Automotive Interiors XiM18, Zoox Boz, Volvo 360c, Renault Symbioz, Renault EZ-Ultimo, Nio EVE, VW ID.Code). This allows face-to-face interaction between occupants, supporting NDRTs such as business meetings or social games.

This seating layout has repeatedly proven to be the most popular alternative among various concepts [1,2,3]. However, the extent to which this kind of seating layout would actually be used is questionable, as the seating orientation against the direction of travel has been shown to be rather unpopular in trains [4]. This unpopularity is anticipated to be even more pronounced in AVs [5,6,7,8,9,10]. Motion sickness is one of the most frequently mentioned factors and has been shown to be more pronounced in rearward-facing passengers [11]. Another potential contributor to the rejection of rearward-facing seats in AVs is trust. Paddeu et al. [12] investigated trust in L4 shuttles with speeds of up to 16 km/h and found significantly higher trust levels when passengers sat in the direction of travel as opposed to against it.

A lack of willingness to use AVs while being seated rearward would inhibit the potential benefits of this kind of seating layout and should, therefore, be addressed when designing the AV’s interior. In order to develop appropriate countermeasures, a deeper understanding of the underlying mechanisms is critical. Willingness to use technology, in general, and autonomous vehicles, in particular, has been subject to numerous analyses, and the relationships between the factors involved have been mapped in various models [13,14,15,16,17,18,19,20,21,22,23]. Accordingly, the following factors could influence the willingness to be seated rearward. These factors have been examined within the general context of what could influence the acceptance of being driven by AVs, including seating orientation. The relationships between these individual factors are summarized in the proposed autonomous driving acceptance model (Figure 1).

AV acceptance is commonly operationalized as the behavioral intention to use AVs [13,15,16,17]. This intention is largely predicted by hedonic motivation, perceived usefulness, and trust [15,23,24,25,26,27,28,29,30,31].

Trust is a widely studied construct in the context of AVs. Lee and See [32] describe trust as “the attitude that an agent will help achieve an individual’s goals in a situation characterized by uncertainty and vulnerability”. Information about the system’s intentions and perception have been shown to positively influence trust in L2 and L3 AVs [33,34,35]. Moreover Paddeu et al. [12] found lower trust levels in rearward-facing passengers in an L4 AV with speeds of up to 16 km/h. At these higher automation levels, undertrust is a major concern as it can lead to disuse [36].

Trust in AVs has been shown to be directly associated with AV acceptance (e.g., [24,25,37]). Furthermore, trust has been found to influence perceived usefulness, which in turn influences acceptance [21,22,23]. This is plausible, as a lack of trust can lead to self-assuring behavior such as observing the vehicle’s operations. Such behavior in turn reduces engagement with the NDRT, diminishing the benefits of autonomous driving and, thus, the likelihood of its adoption.

Hedonic motivation, defined as “the fun or pleasure derived from using a technology”, has also repeatedly been shown to predict AV acceptance [14,18,28,38]. We hypothesize hedonic motivation to be negatively related to motion sickness. Motion sickness describes a state of symptoms such as nausea and dizziness resulting from a mismatch between anticipated and perceived acceleration [39]. These symptoms are likely to inhibit the pleasure experienced while using AVs.

We further expect motion sickness to predict perceived usefulness, as it has been shown to influence the choice of NDRT during autonomous rides [40]. This is likely due to differences in motion sickness severity elicited by different NDRTs [41]. Specifically, looking outside leads to significantly less motion sickness than conducting a visual NDRT [42,43,44]. Thus, motion sickness will decrease the willingness to pursue useful NDRTs. Even in passengers continuing their NDRT while experiencing motion sickness, NDRT performance will suffer [45,46].

System transparency, particularly on information about the vehicle’s current and planned maneuvers as well as detected traffic objects and other road users, has been shown to influence trust [22,47,48,49]. These two types of information are, henceforth, referred to as “intention transparency” and “perception transparency”. Perception transparency is defined as the extent to which passengers are able to identify traffic objects that are recognized by the AV as such. Intention transparency is defined as the extent to which passengers are able to correctly anticipate vehicle maneuvers. Besides trust, intention transparency is also hypothesized to predict motion sickness, as more accurate information on upcoming maneuvers reduces the probability of sensory mismatches between anticipated and sensed motion.

Intention and perception transparency are both expected to be negatively influenced by a rearward-facing seating orientation. Rearward-facing passengers cannot see the road ahead, making vehicle maneuvers less predictable. Similarly, they cannot see traffic objects or road users in front of the vehicle. Consequently, these passengers cannot classify maneuvers such as stopping for a crossing pedestrian or turning at an intersection as a reaction to the corresponding traffic objects, reducing perception transparency. While some authors argue that higher levels of autonomy cause lower levels of information needs, rearward-facing seats may challenge this assumption by reducing intention and perception transparency [49].

This raises the question on how intention and perception transparency can be increased in AV passengers, in general, and in rearward-facing passengers, in particular. Wintersberger et al. [50] manipulated the transparent system behavior of AVs in a simulator study using augmented reality (AR) displays. Highlighting traffic objects and other road users as well as presenting upcoming driving maneuvers to rearward-facing participants led to increased trust and acceptance. Flohr et al. [51] used a wizard-of-oz vehicle to investigate the effect of augmenting other road users and traffic objects. The augmentations were displayed as an overlay on a camera feed of the vehicle apron that was shown on a large display between the first and second row of seats. Fröhlich et al. [52] used a simulated ride in an autonomous bus to compare three modes of presentation for visual information on detected road users and upcoming maneuvers: textual, iconic, and AR (icons overlapping a camera feed). The AR variant was most favored by the participants. Wilbrink et al. [53] compared light-based intention and perception messages in a VR study and found higher trust and usability ratings for both types of messages compared to a baseline. Oliveira et al. [49] compared various technologies that visually augment the AV’s future trajectory and potential hazards in front of the vehicle. A camera feed on a display with graphical overlays presenting this information was found to increase system transparency and trust.

In summary, there is substantial evidence for the effectiveness of in-vehicle displays that visualize upcoming maneuvers using a camera feed and/or arrows for the manipulation of intention transparency in AVs. Concepts related to perception transparency have been successfully manipulated by highlighting relevant traffic objects, e.g., using bounding boxes or signaling colors. Thus, in the current study, intention transparency is manipulated using displays that are either turned off, that present a camera feed from the windshield of the vehicle, or that present a camera feed with an overlay consisting of arrows that indicate the next maneuver of the AV. Perception transparency is manipulated using the same displays, either highlighting relevant traffic objects and road users with bounding boxes or not. However, little is known about the suitability of this type of information display for rearward-facing passengers [50]. The forward view allows the occupants to verify the information presented and to relate them to the real traffic situation. This could have an impact on the influence of the HMIs on the various factors related to acceptance. Thus, seating orientation is manipulated by participants either facing in or against the direction of travel.

Additionally, the passengers in the studies mentioned were not distracted by an NDRT. While this is appropriate for evaluating the general effectiveness of the HMIs presupposing the passengers’ attention, this attention cannot be guaranteed in fully autonomous vehicles. The capacity to engage in a variety of NDRTs is one of the main advantages of L4 and L5 AVs, in general, and of rearward-facing seats, specifically. It is unclear whether passengers engaged in an NDRT require information regarding the AV’s intention and perception and whether display-based iHMIs are suitable for conveying this information.

The aim of this study is to determine the influences of informational displays and the seating orientation of passengers on intention and perception transparency. Furthermore, the relationships described in the model are examined in order to explore the connection between seating orientation and acceptance of fully autonomous vehicles.

Most of the existing AV acceptance models are generated through data from online questionnaires and samples of naive participants that have had little or no real-life interaction with AVs. The question arises on whether the proposed relationships hold true when participants obtain realistic mental models based on hands-on experience with the AV [14]. Especially when investigating factors like seating orientation and motion sickness, this first-hand experience is necessary to derive implications for the development of acceptance, promoting HMIs [54].

2. Materials and Methods

2.1. Design

A 2 × 2 × 3 mixed design with seating orientation (forward-facing vs. rearward-facing) and intention transparency (no image vs. camera feed vs. camera feed + arrows) as within-subjects variables and the perception transparency (bounding boxes vs. no bounding boxes) as a between-subjects variable was implemented. As no bounding boxes were displayed in the “no image” condition, this resulted in a nested design. A tabular overview of the experimental conditions is shown in Appendix A. The operationalization of intention and perception transparency is described in greater detail in the subsection “Transparency Displays”.

2.2. Dependent Variables

All data were pseudonymized and processed confidentially. Online questionnaires and questionnaires assessed between the journeys were administered using LimeSurvey Cloud Version 5.6.49 (Limesurvey GmbH, Hamburg, Germany).

Motion sickness was assessed during the rides using the Fast Motion Sickness Scale (FMS, Keshavarz and Hecht [55]). It is a single-item scale ranging from 0 (no sickness at all) to 20 (frank sickness). This single-item scale was chosen to minimize distraction from the NDRT and allow for continuous assessment of motion sickness over the course of the journeys.

Trust was assessed after each journey, using a modified version of the Situational Trust Scale for Automated Driving (STS-AD, Holthausen et al. [56]). STS-AD assesses trust on the basis of six statements about the topics of trust in the AV, performance, NDRT, risk, judgment, and reaction, and it is rated on a seven-point scale ranging from 1 (completely disagree) to 7 (completely agree). The term ‘in this situation’ was changed to ‘during the ride’ to enable trust ratings for the entire journey.

Driving involvement, intention transparency, and perception transparency were also assessed after each journey. For intention and perception transparency, German adaptations of the system transparency items used by Choi and Ji [22] were implemented, which consist of three statements for each construct. For driving involvement, a self-devised questionnaire consisting of six statements related to the respondents’ monitoring of the journeys was used. Participants rated their approval of the statements of each of the three questionnaires on a six-point scale from 1 (completely disagree) to 6 (completely agree).

Hedonic motivation was assessed after each journey with the scale used by Venkatesh et al. [18]. It measures the participants’ approval of three statements about the fun of using a device on a seven-point scale ranging from ‘1—Strongly disagree’ to ‘7—Strongly agree’. The term ‘mobile internet’ was replaced by ‘autonomous vehicles’.

Perceived usefulness was assessed after each journey using a modified version of the scale used by Zhang et al. [19], which is based on the scale used by Davis [57]. Participants rated their approval of five statements about the usefulness of AVs on a seven-point Likert scale ranging from ‘1—strongly disagree’ to ‘7—strongly agree’.

Lastly, acceptance was assessed after each journey. It was measured as the behavioral intention to use AVs [13,15,16,17]. Behavioral intention was assessed using the two items ‘Would you use such a vehicle when sitting against the direction of travel?’ and ‘Would you use such a vehicle when sitting in the direction of travel?’ and the response options ‘Yes’ and ‘No’.

Gaze behavior was assessed using eye-tracking glasses (Tobii Pro Glasses 3, Tobii AB 2023, Danderyd Municipality, Sweden). Eye-tracking data was collected for exploratory analyses.

2.3. Control Variables

Data for the following variables was collected through an online survey prior to the initial drive. The data obtained was then matched with data gathered during the study using unique codes generated by each participant. Susceptibility to motion sickness was evaluated using a shortened version of the Motion Sickness Susceptibility Questionnaire (MSSQ-Short; Golding [58]), which comprises 18 items. Nine of these items gauge the frequency of experiencing nausea in different motion-related situations before turning twelve, rated on a four-point scale from “never felt sick” to “frequently felt sick”, while the remaining nine measure similar frequencies over the past decade.

Affinity for technology was assessed using the Affinity for Technology Interaction scale (ATI; Franke et al. [59]). Driving experience was assessed through questions about annual mileage, usage frequency of various means of transportation, years of driver’s license ownership, and level of pleasure experienced while driving (as a driver and passenger). Additionally, prior knowledge of autonomous driving and experience with car automation systems were assessed. Propensity to trust was rated on a five-point scale ranging from 1 (strongly disagree) to 5 (strongly agree) using the Propensity to Trust Machines scale employed by [60].

Fixation duration on the displays was measured using eye-tracking data. This was performed to relate potential effects of the intention and perception transparency manipulations to the visual attention these manipulations from the participants.

2.4. Sample

The sample consisted of 51 participants, 30 males and 21 females. Participants were between 19 and 58 years of age (M = 32.46, SD = 10.16). Each participant had a German citizenship; 64% were employed, 22% were students. All participants were recruited via the mailing list of the DEKRA Automobil GmbH, having met the inclusion criteria of not having had any previous experience with autonomous vehicles or the pedal and steering robot and being 18 years of age or older. Participants were compensated individually, either in working hours, with guided tours over the DEKRA Lausitzring, or financially with 30 Euros.

Descriptive analyses were based on a sample of 45 participants, as 1 participant did not complete the pre-questionnaire. Of the initial 51 participants, 5 withdrew from the study before completing all six trials. All five cited severe symptoms of motion sickness as the reason for discontinuing. These dropouts were excluded from the analyses and only used as a descriptive comparison. The remaining participants had held their driver’s license for an average of 13.79 years (SD = 9.93, min = 0, max = 33). They drove an average of 18,389 km per year (SD = 11,558, min = 0, max = 40,000) and reported greater joy when driving themselves compared to being driven. The mean score for the driver was 4.36 (SD = 0.86, min = 1, max = 5), while the mean score for the co-driver was 3.18 (SD = 0.84, min = 1, max = 4). The most frequently used mode of transportation was the car. The average score for prior knowledge in the field of autonomous driving was 2.10 (SD = 0.69, min = 1, max = 3.67), which corresponds to little prior knowledge. The sample had mean ATI of 4.20 (SD = 0.91, min = 1.67, max = 6), which corresponds to relatively high ATI samples tested by [59]. The mean propensity to trust technology was 3.66 (SD = 0.74, min = 1.83, max = 5), which is relatively high when compared with the sample tested by [61]. The mean MSSQ-Short raw score was 8.35 (SD = 8.54, min = 0, max = 32.38). In comparison to the population tested by Golding (2006), the participants’ motion sickness susceptibility scores were between the 0th and 96.58th percentile, with an overall mean of 38.5th percentile against Golding’s (2006) population. When including dropout participants, the overall mean shifted to the 43.8th percentile, indicating a higher susceptibility to motion sickness in dropout participants.

2.5. Apparatus

A Volkswagen T6 Multivan (2-L Diesel engine, 200 HP, automatic dual clutch transmission) served as the test vehicle (Figure 2). It featured three rows of seats, with the seats in the middle row being rotated by 180 degrees, causing the seats of the second and third rows to face each other. Participants sat in the second and third row of the seats, which were mounted 60 cm apart, and the backrest was set at an 100° angle relative to the seating surface. LC-displays (Faytech n.d., Witzenhausen, Germany) measuring 13 inches with a resolution of 1920 × 1080 pixels were mounted between the seats of the second and third row, respectively. They were positioned at a height of 85.5 cm from the vehicle floor (lower edge) and 7 cm behind the seats. Each display was visible to participants sitting in the opposite seating row (Figure 2). Vomit bags were provided within easy reach. To enhance the impression of an AV, a magnet board stating ‘autonomous vehicle’ was placed on each side of the vehicle. Additionally, four dummy cameras were attached to the vehicle roof.

A Logitech BRIO 4K STREAM EDITION webcam (Logitech, Lausanne, Switzerland) was mounted on the windscreen next to the rear-view mirror, streaming a live video of the view from the windscreen to the displays at 1280 × 720 pixels and 60 frames per second. The camera had a 90-degree field of view and was equipped with a polarization filter to prevent overexposure. The display manipulations in the rear of the vehicle (visible to rearward-facing participants) showed a horizontally mirrored video. Preliminary tests showed that this reduced the discrepancy between expected and actual vehicle movements during turning maneuvers. One drawback, however, was the perception of left-hand driving. The display manipulations were processed by a Jetson AGX Orin Developer Kit (hereafter: Jetson; NVIDIA Corporation, Santa Clara, CA, USA, 2023). The experimenter sat in the passenger seat and controlled the Jetson and the display manipulations. The SFPhybrid automated driving system for cars (Stähle GmbH, Wimsheim, Germany) was installed in the test vehicle (Figure 2). It consists of a robot comprising various actuators and the internal measuring unit ADMA-SLIM (GeneSys Elektronik GmbH, Offenburg, Germany). The robot combines actuators from the AUTOPILOT HYBRID series, including acceleration and braking actuators, with the STEERING ACTUATOR SSP-FrontFree, both controlled by a mobile control unit. ADMA-SLIM is a GPS-based system mounted on the roof of the vehicle. It measures the vehicle’s motion in all three axes to provide precise position and motion determination.

To program the route, the course was first driven manually, with the lateral and longitudinal movements of the vehicle recorded by ADMA-SLIM. Synoptically, the systems did not use vehicle sensors to capture the environment; instead, all vehicle motion was hard-coded. Throughout the experiment, a safety driver sat in the driver’s seat and observed the robot’s performance. He was trained to shut down the technology and take back control of the vehicle if the robot was not performing as intended. He had no other influence on the driving performance.

The test was conducted at the DEKRA Lausitzring test track in Klettwitz, Germany. The track included sections simulating urban and rural roads and a motorway. Speeds reached up to 50 km/h in the urban sections, up to 70 km/h in the rural sections, and up to 100 km/h on the motorway. The route included four scenarios (two urban, one rural, one highway) to increase realism (Figure 3).

Scenario 1 (Urban): The AV enters a roundabout. As it exits the roundabout after half a rotation, a previously hidden cyclist played by a confederate crosses the exit lane of the roundabout on a bicycle priority lane. The AV performs an abrupt breaking maneuver and waits for the cyclist to cross.

Scenario 2 (Rural): The AV drives along a rural road at 70 km/h as a boar unexpectedly crosses the road. The AV performs an emergency break and comes to a halt just in front of the crossing boar. The boar was stuffed and attached to a rolling board, which was pulled along the road with a rope by a confederate who was not visible to the test subjects.

Scenario 3 (Highway): The AV drives along a highway at 100 km/h. It enters a construction site which has a speed restriction of 60 km/h and a chicane. Another car, driven by a DEKRA test driver overtakes the AV at 70 km/h inside the chicane.

Scenario 4 (Urban): The AV turns right at an intersection. A pedestrian played by a confederate, previously invisible to the AV’s passengers, crosses the road unexpectedly. The AV performs an abrupt breaking maneuver and waits for the pedestrian to cross.

Training of the confederates ensured a reliable timing of the scenarios. The route was driven anticlockwise (scenario order 1, 2, 3, 4) and clockwise (scenario order 4, 3, 2, 1) to minimize learning effects (Figure 4). The order of the route direction was counterbalanced between groups.

2.6. Transparency Displays

Depending on the intention transparency condition, the displays were either turned off, showed only the camera feed, or displayed the camera feed with the arrow overlay. Perception transparency was either enabled or disabled, depending on the group condition. This resulted in five experimental conditions: displays off, camera feed without bounding boxes, camera feed with bounding boxes, arrow overlay without bounding boxes, or arrow overlay with bounding boxes. Both arrows and bounding boxes were programmed to superimpose the live stream video using Python 3.11 (64-bit). The design of the arrow overlay was based on insights from previous studies investigating transparency in autonomous vehicles, the state of practice in navigation systems, German road signs, and Gestalt principles [33,62,63,64]. Additionally, an expert workshop with eight professionals in the fields of human factors and industrial design was conducted to evaluate several design proposals. This resulted in six different navigation symbols: navigation arrow, acceleration, braking, roundabout, turns, and a neutral state/standing (Figure 5). Adobe Illustrator and Adobe Photoshop (Adobe, 2023) were used for the design. DaVinci Resolve Version 18.6 (Blackmagic Design Pty. Ltd., 2023) was employed to create an animated video involving the navigation symbols appropriate for each course. This overlay video was exported, including the alpha channel color, allowing for a transparent background and enabling it to be overlaid onto the camera feed.

All symbols featured the same curve radius, perspective degree, color, and stroke weight to account for the Gestalt principle of similarity. Additionally, proximity and similarity were taken into account in the design of the acceleration and braking navigation symbols. The choice of presentation colors was guided by the figure–ground Gestalt principle, incorporating insights from Künzer [65] regarding the perception of danger or safety based on coloration. Consequently, colors such as green or red were omitted, as they could erroneously imply safe or unsafe maneuvers of the experimental vehicle, potentially contradicting the actual situation (e.g., in the event of an emergency brake). Moreover, these colors are commonly interpreted as ‘stop’ and ‘go’ within the automotive context [66]. Cyan, recommended for external AV communication as a neutral color [66,67], as well as purple were deemed unsuitable in the present context, as it would not sufficiently contrast with the tarmac. Instead, magenta was chosen as it showed a good figure–ground effect while still conveying a neutral vehicle state.

Previous research has frequently examined augmented reality representations for vehicle transparency, which emerged as a preferred representation by most participants [62,64]. To account for this preference, the perspective of the navigation symbols was adjusted by two 8-degree rotations to create the illusion of the symbols lying on the ground and the vehicle driving over them (Figure 5). The intention transparency manipulation was integrated into the Python code, allowing it to function independently and in conjunction with the perception transparency manipulation.

Bounding boxes for perception transparency were generated using Ultralytics’ object recognition software YOLOv8s (Ultralytics Inc., Frederick, MD, USA). YOLOv8 is a real-time object detection and image segmentation model based on deep learning and computer vision. As models with large packages were found to introduce increased display manipulation delays without significantly enhancing object detection performance, the model with a small package size was chosen for this study. YOLOv8 can detect objects belonging to different classes defined in the COCO dataset. For this study, only the categories of person, bicycle, and car were utilized to identify road users. Categories such as road signs were excluded as the corresponding identifications proved to be unreliable. Additionally, a variety of animals (cat, dog, horse, sheep, cow, elephant, bear) were employed to identify the boar, as there is no specific category for it in the dataset. Assigning a different color to each category resulted in a cluttered scene due to the software frequently switching categories based on confidence levels for individual objects. Therefore, the code was modified to assign a single color to all objects. Cyan was chosen, as it showed sufficient contrast against the general background of the camera feed.

2.7. NDRT

Participants engaged in a non-driving-related task (NDRT) during the automated journey to simulate realistic travel conditions and encourage social interaction. The selected task was Stadt–Land–Fluss, the German version of Categories, a well-known social game commonly played during long bus or train rides. Categories were distributed evenly across journeys to minimize variation in task difficulty. Participants were instructed to engage with the task to the extent they felt comfortable, reflecting natural behavior during AV journeys.

This NDRT was chosen for several reasons. It is representative of typical activities in the given seating arrangement and involves a combination of reading, writing, and occasional visual engagement with the environment (e.g., looking out the window while thinking of a word). As reading is one of the most motion sickness-inducing NDRTs, this activity is suitable for inducing motion sickness while preserving visual differences between seating orientations Metzulat et al. [68]. The cognitive load is moderate, ensuring sustained engagement without overwhelming participants. Additionally, the rules of Stadt–Land–Fluss are widely known among the target population, eliminating the need for extensive instructions.

2.8. Procedure

Participants completed the pre-trial questionnaire online prior to the in situ experiment. Upon arrival at the test track, participants were greeted by the experimenter, who verbally explained the purpose of the study, the test procedure, and participants’ rights. Participants could withdraw from the study at any point in time without consequence or need for justification. As soon as a participant reached an FMS score of over 16, their participation was terminated, which did not occur. Participants then gave informed consent to participate in the study. The eye trackers were introduced and set up. For spectacle wearers, corrective lenses were integrated into the eye-tracking goggles. The steering robot was presented to the participants to ensure that they were aware of the autonomy of the test vehicle. Questions about the steering robot’s capabilities were left until the end of the study to avoid influencing trust ratings by revealing that the robot was following a pre-programmed route. Questions about whether the steering robot would drive on its own were answered in the affirmative. Throughout the study, the experimenter emphasized ‘automated’ driving rather than ‘autonomous’ driving to avoid misinformation. Instructions for the questionnaires on a tablet, the NDRT, and the MISC were given. The functionality of the displays was then explained.

After calibration of the eye-tracking glasses, the first journey started. During the journeys, participants played ‘Categories’ and answered the FMS.Between each journey, participants remained in the vehicle to complete the scales for the remaining dependent variables and then changed seats. This took seven minutes on average, during which the vehicle remained stationary. The order of the seating orientation was randomized such that each participant experienced three journeys in forward-facing orientation and three journeys in rearward-facing seating orientation, one for each condition of intention transparency manipulation. Participants experienced the ride in forward-facing seating orientation on the co-driver´s seating side at least once as the robot was best visible from here. Overall, participants completed six journeys, each lasting 330 s. Additionally, groups were randomly assigned to the two bounding boxes conditions. The order of the display manipulation was counterbalanced between groups. Finally, the participants were thanked for their participation. A debriefing about the robot’s real capabilities was given to all participants via email after data collection was completed to prevent the spread of information to future participants.

3. Results

Statistical analysis was conducted using R version 4.3.1 (The R Foundation, Vienna, Austria). A mixed model analysis of variance was applied to assess the effects of seating orientation (forward, rearward), intention transparency (IT: no image vs. camera feed vs. camera feed + arrows), and perception transparency (PT: bounding boxes vs. no bounding boxes) on measured intention and perception transparency, motion sickness, trust, driving involvement, hedonic motivation, and perceived usefulness. Each outcome was analyzed separately with fixed effects for the factorial design (SeatingOrientation × IT × PT) and random intercepts for subjects to account for repeated measures [69]. Due to the nested data structure where PT “ON” combined with IT “OFF” effectively functioned as “OFF”, causing unequal group sizes, a mixed model was preferred over classical ANOVA, which assumes balanced data [70].

This mixed model serves as an extension of factorial ANOVA that explicitly handles complex nesting and repeated measures. Type III ANOVA F-tests were estimated within this mixed model framework using the lmerTest package. After fitting via restricted maximum likelihood estimation, the package applies Satterthwaite’s approximation to compute denominator degrees of freedom for each fixed effect.

Two ANOVAs serve as manipulation checks to verify whether the HMIs were capable of improving intention and perception transparency ratings in forward- and rearward-facing AV passengers occupied with an NDRT. The ANOVA with intention transparency ratings as the dependent variable showed a significant main effect of seating orientation. Passengers reported higher intention transparency when facing forward (M = 4.00, SD = 0.90) than when facing rearward (M = 3.48, SD = 1.03, F(1, 221.45) = 28.53, p < 0.001, ${\eta }_{\mathrm{g}}^2$ = 0.114). None of the other main effects or interactions showed significance. Means and standard deviations for each condition are shown in

Table 1. Type III ANOVA results from mixed-effects models.

Dependent Variable	Effect	F	dfeffect	dferror	p-Value	Partial ${\mathit{\eta }}^2$
Intention Transparency	SeatingOrientation	28.534	1	221.449	<0.001 ***	0.114
	IT	0.456	2	234.877	0.634	0.004
	PT	0.005	1	251.253	0.942	0.000
	SeatingOrientation:IT	0.284	2	221.449	0.753	0.003
	SeatingOrientation:PT	0.012	1	221.449	0.914	0.000
	IT:PT	0.773	1	221.449	0.380	0.003
	SeatingOrientation:IT:PT	0.377	1	221.449	0.540	0.002
Perception Transparency	SeatingOrientation	2.777	1	221.401	0.097	0.012
	IT	0.635	2	232.370	0.531	0.005
	PT	0.437	1	263.859	0.509	0.002
	SeatingOrientation:IT	0.146	2	221.401	0.864	0.001
	SeatingOrientation:PT	2.088	1	221.401	0.150	0.009
	IT:PT	0.004	1	221.401	0.947	0.000
	SeatingOrientation:IT:PT	0.128	1	221.401	0.720	0.001
Motion Sickness	SeatingOrientation	12.859	1	221.052	<0.001 ***	0.055
	IT	0.935	2	224.687	0.394	0.008
	PT	0.017	1	245.661	0.896	0.000
	SeatingOrientation:IT	0.002	2	221.052	0.998	0.000
	SeatingOrientation:PT	2.586	1	221.052	0.109	0.012
	IT:PT	2.632	1	221.052	0.106	0.012
	SeatingOrientation:IT:PT	0.002	1	221.052	0.968	0.000
Trust	SeatingOrientation	7.845	1	221.000	0.006 **	0.034
	IT	0.431	2	236.300	0.650	0.004
	PT	1.220	1	265.992	0.270	0.005
	SeatingOrientation:IT	0.199	2	221.000	0.820	0.002
	SeatingOrientation:PT	2.350	1	221.000	0.127	0.011
	IT:PT	0.008	1	221.000	0.927	0.000
	SeatingOrientation:IT:PT	0.005	1	221.000	0.945	0.000
Driving Involvement	SeatingOrientation	0.790	1	220.869	0.375	0.004
	IT	0.803	2	0	1.000	1.000
	PT	0.102	1	0	1.000	1.000
	SeatingOrientation:IT	0.222	2	220.869	0.801	0.002
	SeatingOrientation:PT	2.663	1	220.869	0.104	0.012
	IT:PT	0.002	1	220.869	0.960	0.000
	SeatingOrientation:IT:PT	0.023	1	220.869	0.879	0.000
Hedonic Motivation	SeatingOrientation	10.492	1	220.858	0.001 **	0.045
	IT	0.874	2	0	1.000	1.000
	PT	0.008	1	0	1.000	0.996
	SeatingOrientation:IT	0.377	2	220.858	0.687	0.003
	SeatingOrientation:PT	3.067	1	220.858	0.081	0.014
	IT:PT	0.601	1	220.858	0.439	0.003
	SeatingOrientation:IT:PT	0.612	1	220.858	0.435	0.003
Perceived Usefulness	SeatingOrientation	4.597	1	220.877	0.033 *	0.020
	IT	0.378	2	226.044	0.686	0.003
	PT	0.134	1	223.885	0.715	0.001
	SeatingOrientation:IT	0.591	2	220.877	0.554	0.005
	SeatingOrientation:PT	1.465	1	220.877	0.227	0.007
	IT:PT	0.882	1	220.877	0.349	0.004
	SeatingOrientation:IT:PT	0.496	1	220.877	0.482	0.002
Fixation Duration Display	SeatingOrientation	3.393	1	217.137	0.067	0.015
	IT	1.394	2	227.322	0.250	0.012
	PT	1.521	1	127.743	0.220	0.012
	SeatingOrientation:IT	2.789	2	217.230	0.064.	0.025
	SeatingOrientation:PT	1.016	1	216.950	0.315	0.005
	IT:PT	0.016	1	216.950	0.900	0.000
	SeatingOrientation:IT:PT	0.906	1	216.950	0.342	0.004

Note. Significance indicators: *** p < 0.001, ** p < 0.01, * p < 0.05, p < 0.10. Partial ${\mathit{\eta }}^2$ = partial eta squared effect sizes. F = F statistic; df_effect = numerator degrees of freedom; df_error = denominator degrees of freedom.

.

Perception transparency rating were descriptively higher when the bounding boxes were visible (M = 4.53, SD = 0.77) than when they were not (M = 4.39, SD = 0.72). However, neither this nor any other main effect or interaction regarding perception transparency ratings reached significance.

3.1. Motion Sickness

Passengers experienced significantly stronger motion sickness when facing rearward (M = 2.02, SD = 2.78) than when facing forward (M = 1.51, SD = 2.30, F(1, 221.05) = 12.86, p < 0.001, ${\eta }_{\mathrm{p}}^2$ = 0.055). Again, there were no other significant main effects or interactions. Figure 6 provides an overview of the means and standard deviations for each condition.

3.2. Trust

The ANOVA with trust as the independent variable also revealed a significant effect of seating orientation. Passengers reported higher trust when facing forward (M = 4.88, SD = 0.90) than when facing rearward (M = 4.71, SD = 0.89, F(1, 221) = 7.85, p = 0.006, ${\eta }_{\mathrm{g}}^2$ = 0.034). No other main effects or interactions reached significance. Means and standard deviations for each experimental condition are shown in Figure 6.

3.3. Driving Involvement, Hedonic Motivation, and Perceived Usefulness

The ANOVA with driving involvement as the dependent variable showed no significant main effects or interactions.

Hedonic motivation scores were significantly higher when facing forward (M = 4.98, SD = 1.31) than when facing rearward (M = 4.76, SD = 1.42, F(1, 220.89) = 10.49, p = 0.001, ${\eta }_{\mathrm{p}}^2$ = 0.045).

Passengers reported significantly higher perceived usefulness when facing forward (M = 5.16, SD = 1.30) than when facing rearward (M = 5.06, SD = 1.26, F(1, 220.88) = 4.59, p = 0.033, ${\eta }_{\mathrm{p}}^2$ = 0.020). No other main effects or interactions reached significance.

3.4. Gaze Behavior

The same mixed model ANOVA using mean fixation time on the transparency displays as the dependent variable showed no significant effects. On average, participants spent 3.6% of the journeys duration looking at the displays (M = 11.9 s, SD = 16.1 s).

3.5. Behavioral Intention

To test whether behavioral intention to use AVs facing rearward is lower than facing forward, McNemar’s Chi-squared test with continuity correction was conducted. Behavioral intention to use AVs facing forward was significantly greater than facing rearward (${\chi }^2=30.782$, p < 0.001). The odds of a “Yes” response were 4.5 times higher for forward-facing than for rearward-facing (OR = 0.22), with a large effect size (Cohen’s g = −0.64). The proportion difference was −0.18, meaning forward-facing had a 18% higher “Yes” rate.

3.6. Indirect Effects

To assess the indirect effects of intention and perception transparency and seating orientation on AV acceptance through the other factors of the model, a structural equation model was specified. As multilevel structural equation models with dichotomous outcomes rely on estimation techniques that are especially sensitive to sample size and would, thus, substantially increase the risk of non-convergence and unreliable parameter estimates [71,72,73], behavioral intention was analyzed in a separate logistic regression. First, the model proposed in Figure 1 was estimated using robust maximum likelihood with 1000 bootstrap samples, taking into account clustering by subject ID. Non-significant paths were then iteratively removed, with a model comparison to the previous best-fitting model after each iteration. Model comparisons were based on CFI, TLI, RMSEA, SRMR, AIC, and BIC. Having identified the best-fitting model after path omission, modification indices were examined to identify previously unaccounted paths that could improve model fit. Only paths that were theoretically justified and had modification indices >3.84 were considered for inclusion in the model [74]. These paths were added to the model iteratively, again retaining the best-fitting model at each iteration:

Intention transparency–perception transparency, as relevant traffic objects might act as explanations for certain driving maneuvers [75]; trust–driving involvement, as low-trust passengers might reassure themselves of the system’s capabilities by monitoring the driving activity [76]; trust–motion sickness, as severe motion sickness might lead to a negative perception of the AV’s driving style and, thus, to a decrease in trust [77]; driving involvement–intention transparency, as passengers that do not receive information about driving maneuvers from the AV might be inclined to actively look for this information themselves [78]; and hedonic motivation–trust, as low trust can lead to aversive emotions [79], diminishing pleasure during automated journeys [80].

The SEM summary is shown in

Table 2. Summary of the structural equation model.

Outcome	Predictor	Coefficient	SE	z	p	95% CI	Beta
Intention Transparency	Seating Orientation	−0.50	0.12	−4.02	<0.001 ***	[−0.74; −0.26]	−0.25
Intention Transparency	Perception Transparency	0.19	0.12	1.58	0.114	[−0.05; 0.42]	0.14
Motion Sickness	Seating Orientation	0.51	0.17	3.08	0.002 **	[0.19; 0.84]	0.10
Trust	Intention Transparency	0.15	0.08	1.76	0.079	[−0.02; 0.31]	0.16
Trust	Perception Transparency	0.35	0.11	3.19	0.001 **	[0.14; 0.57]	0.29
Trust	Driving Involvement	−0.20	0.06	−3.15	0.002 **	[−0.33; −0.08]	−0.23
Trust	Motion Sickness	−0.09	0.02	−3.84	<0.001 ***	[−0.14; −0.05]	−0.27
Driving Involvement	Perception Transparency	−0.26	0.14	−1.91	0.056	[−0.53; 0.01]	−0.19
Driving Involvement	Motion Sickness	0.14	0.03	5.23	<0.001 ***	[0.09; 0.20]	0.36
Driving Involvement	Intention Transparency	0.28	0.06	4.63	<0.001 ***	[0.16; 0.39]	0.27
Perceived Usefulness	Trust	0.60	0.15	4.09	<0.001 ***	[0.31; 0.88]	0.42
Perceived Usefulness	Motion Sickness	−0.07	0.07	−1.06	0.289	[−0.20; 0.06]	−0.14
Perceived Usefulness	Driving Involvement	−0.04	0.08	−0.43	0.665	[−0.20; 0.13]	−0.03
Hedonic Motivation	Motion Sickness	−0.14	0.06	−2.20	0.028 *	[−0.26; −0.02]	−0.26
Hedonic Motivation	Trust	0.45	0.15	2.93	0.003 **	[0.15; 0.75]	0.29

Note. *** p < 0.001, ** p < 0.01, * p < 0.05, p < 0.10. Values in Beta represent standardized coefficients and, thus, effect sizes.

. The model showed a good fit (CFI = 0.969, TLI = 0.916, RMSEA = 0.06, SRMR = 0.05, ${\chi }^2$(11) = 18.11, p = 0.079, all scaled and robust versions calculated under the MLR estimator). As expected, seating orientation predicted intention transparency to a significant degree. However, the same was not true for perception transparency. Intention transparency predicted neither trust nor motion sickness significantly; however, it did predict driving involvement. Perception transparency predicted trust but not driving involvement. Motion sickness predicted driving involvement, hedonic motivation, and trust but not perceived usefulness. Trust predicted perceived usefulness, hedonic motivation, and driving involvement.

To assess the influence of trust, perceived usefulness, and hedonic motivation on behavioral intention, a logistic regression was conducted. Specifically, we used a cluster–robust Firth’s penalized logistic regression with intercept correction and bootstrap inference [81,82]. Coefficients were bootstrapped with 1000 replicates using case resampling at the participant level. Odds ratios with 95% confidence intervals were calculated and extracted. A one-point increase on the STS-AD scale increased the odds of intention to use an AV while being seated rearward by a factor of OR = 2.22, 95% CI [1.20, 4.38]. A one-point increase in hedonic motivation increased the odds by a factor of OR = 1.73, 95% CI [1.20, 4.38]. The odds ratio for perceived usefulness was OR = 1.54, 95% CI [0.88, 2.99]. However, since the confidence interval includes 1, no decisive conclusions can be drawn about its relationship with behavioral intention. The final model is shown in (Figure 7).

4. Discussion

A subject study on a test track was implemented to validate a model that predicts the acceptance of AVs based on the seating orientation of their passengers. Using a test vehicle equipped with a steering and pedal robot, seating orientation and vehicle transparency were manipulated. Intention transparency was manipulated using in-vehicle displays that either showed no image, a camera feed, or a camera feed and arrows. Perception transparency was manipulated by either displaying bounding boxes around relevant traffic objects as an overlay on the camera feed or not displaying the bounding boxes. Data was gathered on factors that are theorized to influence AV acceptance and to be influenced by seating orientation.

The influences of seating orientation as well as intention and perception transparency manipulation on motion sickness, trust, driving involvement, perceived usefulness, and hedonic motivation were examined using mixed repeated measures ANOVAs. As expected, subjects reported lower levels of intention transparency, trust, hedonic motivation, perceived usefulness and higher levels of motion sickness when facing rearward. Subjects also reported a higher likelihood of using AVs facing forward than rearward. However, seating orientation did not affect perception transparency. Importantly, the manipulations of intention and perception transparency had no significant effect on any of the model variables.

The structural equation model revealed that intention transparency was found to predict trust, while perception transparency did not. It is interesting to note, however, that motion sickness was not found to be predicted by intention transparency but by seating orientation directly. Motion sickness was found to predict hedonic motivation and perceived usefulness. While motion sickness was not found to predict behavioral intention directly, it unexpectedly was found to predict trust. Trust and hedonic motivation were found to predict behavioral intention, while no conclusive statement can be made about perceived usefulness. However, these results should be interpreted cautiously due to the small sample size, which falls below recommended thresholds [83,84]. While robust estimators (MLR) and bootstrapping were used to mitigate bias, the model’s complexity and limited power increase uncertainty in parameter estimates and fit indices. Future studies should validate these findings using larger samples.

One central finding is the failure of the intention and perception transparency manipulations. This is surprising, as similar manipulations showed significant effects in previous studies [49,50,51,52,53].

Analysis of the gaze behavior showed that the participants paid very little attention to the displays in general. Mean fixation duration on the displays was only 3.6% of the journey duration. While we did not explicitly ask the participants why they spent such little time monitoring the displays, several participants reported that they were too preoccupied with the NDRT to pay sufficient attention to the displays. As the majority of previous studies that reported evidence in favor of the effectiveness of similar manipulations did not involve NDRTs, this is a plausible explanation. The possibility of conducting NDRTs is considered one of the most impactful advantages of AVs in general and the living room seating layout in particular, and passengers are expected to spend a significant proportion of their traveling time with NDRTs [85,86]. Thus, having participants engaged in an NDRT poses a realistic use case that should not be neglected when investigating AV acceptance.

This suggests that displays requiring the passengers’ attention might not be suitable for information transmission in fully autonomous vehicles [87]. Especially when information is provided continuously, such as when visualizing the vehicle’s maneuvers, passengers either need to split their attention between NDRT and iHMI or pay attention to one of the two. Even though participants in this study were instructed to only engage in the NDRT to the amount they felt comfortable with, this resulted in a lack of effectiveness of the iHMI.

Rearward-facing passengers experienced lower levels of intention transparency than forward-facing passengers. This supports the notion that eliminating the visibility of the vehicle apron limits the passengers’ ability to predict upcoming maneuvers. Perception transparency, on the other hand, did not depend on seating orientation. The road users in three of the four scenarios were first visible from the windshield and only visible from the rear windshield after the scenario was finished (pedestrian, cyclist, boar), while the overtaking vehicle in the highway construction site was first visible from the rear windshield. A possible explanation for this could be that participants learned the positions of the scenarios despite the randomization of driving direction.

While rearward-facing passengers reported lower levels of intention transparency and higher levels of motion sickness, we did not find substantial evidence for or against a relationship between intention transparency and motion sickness. This raises the question on what aspect of the rearward-facing seating orientation caused the increase in motion sickness. It is still not entirely clear to what degree a mismatch between anticipated and sensed motion as opposed to a mismatch between different simultaneous sensory inputs, e.g., for the visual and the vestibular organs, contributes to the development of motion sickness. Susceptibility to motion sickness has been shown in blind people [88] but less so than in sighted people [89], suggesting that visual input is not necessary for the development of motion sickness, but it mediates it. Thus, a conflict between visual and vestibular information regarding self-motion cannot be the sole cause of motion sickness. De Winkel et al. [5] found no difference between visual cues indicating the maneuvers of an AV either 500ms in advance or in real time in terms of motion sickness mitigation. This indicates that a conflict between anticipated and sensed self-motion cannot be the sole cause of motion sickness either. Comparing the influences of both conflicts directly in future studies could yield more insights into the development and mitigation of motion sickness.

The sample had a relatively low motion sickness susceptibility. This raises the question on whether a potential effect of intention transparency manipulation may be found in more susceptible subjects Hainich et al. [90]. Only 13 participants in our sample exhibited above-average motion sickness susceptibility. Due to the limited size of this subsample, we refrained from conducting separate inferential statistical analyses. Instead, motion sickness means across experimental conditions were examined descriptively. These means were approximately twice as high compared to the full sample. However, this increase was relatively uniform across all conditions. Consequently, we do not anticipate substantial changes in the overall statistical outcomes, even if a larger subsample of highly susceptible individuals were available.

The direct relationship between motion sickness and behavioral intention found previously was not confirmed in this study [91]. The consideration of trust and hedonic motivation, both found to be predicted by motion sickness, might explain this discrepancy. However, there was a link between motion sickness and actual system usage, as all five dropouts in this study stated motion sickness as the reason for discontinuing the experiment. This highlights the importance of real system experience when modeling the acceptance of AVs, as actual AV usage seems to be influenced by immediate physical experiences. It is also possible that the exclusion of these participants from the final analysis prevented the detection of a direct relationship between motion sickness and behavioral intention to use AVs.

The revised model gives insight into the relationship between AV acceptance and seating orientation. Rearward-facing passengers have shown an impaired ability to anticipate the vehicle’s maneuvers. They in turn showed less trust in the automation and higher levels of motion sickness. This resulted in reduced hedonic motivation and perceived usefulness of using the AV. Ultimately, this led to less behavioral intention to use AVs. It needs to be addressed that these results are based upon the specific experiences that the subjects made during the study. Therefore, the model does not claim to be generally valid for any type of AV and driving scenario. Some differences in the results compared to previous studies could be explained by the specifics of this test setting. For example, the non-significant relationship between perceived usefulness and behavioral intention could be due to the recreational nature of the pre-defined NDRT. However, the added value in this experimental setting lies in obtaining data from subjects with consistent mental models of AVs based on realistic experiences. In particular, first-hand experience of the factors newly added to this model, such as seating orientation and motion sickness, can differ substantially from the expectations of the participants. Thus, the model provides a basis for design decisions about iHMIs in AVs with living room seating layouts.

Factors such as social influence or personality traits, which are part of various previous models, were not investigated in this study (e.g., [13,14,15,18,20,28,38]). As the aim of this study was to support the development of iHMIs that foster the acceptance of rearward-facing seating in AVs by gaining a deeper understanding of the relationship between seating orientation and AV acceptance, we focused on factors that could be influenced by iHMIs. Nevertheless, potential interactions between, for example, social influence and motion sickness cannot be discounted and pose an intriguing subject for future research.

5. Conclusions

The objective of the present study was to gain a more profound understanding of the relationship between seating orientation in AVs and their acceptance. The utilization of steering robotics on a test track enabled the implementation of autonomous journeys, thus allowing for the measurement of factors such as motion sickness and trust under realistic conditions. A reduction in intention transparency and an increase in motion sickness were observed when participants were seated against the direction of travel. The reduced intention transparency was accompanied by reduced trust, which, like the increased motion sickness, was associated with reduced perceived usefulness and, thus, a reduced behavioral intention to use AVs. These findings illustrate the pivotal roles of seating orientation and motion sickness when modeling AV acceptance, and highlight the necessity of motion sickness-mitigating measures to foster this acceptance. Furthermore, displays for the visualization of vehicle maneuvers and detected road users to increase intention and perception transparency were investigated for the first time in a realistic L4 setting including an NDRT. Here, the displays had no effect which emphasizes the need for novel iHMIs to transmit information during visual NDRTs. In addition to the use of other sensory modalities such as haptics and acoustics, the utilization of the AV occupants’ peripheral vision represents a promising approach in this regard. Future research should focus on the design of these iHMIs.