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Objective We observe the driving performance effects of gesture-based interaction (GBI) versus touch-based interaction (TBI) for in-vehicle information systems (IVISs). Background As a contributing factor to a number of traffic accidents, driver distraction is a significant problem for traffic safety. More specifically, visual distraction has a strong negative impact on driving performance and risk perception. Thus, the implementation of new interaction systems that use midair gestures to encourage glance-free interactions could reduce visual distraction among drivers. Methods In this experiment, participants drove a projection-based Vehicle-in-the-Loop. The projection-based technology combines a visual simulation with kinesthetic, vestibular, and auditory feedback from a car on a test track. While driving, participants used GBI or TBI to perform IVIS tasks. To investigate driving behavior related to critical driving situations and car-following maneuvers, vehicle data based upon longitudinal and lateral driving were collected. Results Participants reacted faster to critical driving situations when using GBI compared to TBI. For drivers using TBI, steering performance decreased and time headway to a preceding vehicle was higher. Conclusion Gestures provide a safe alternative to in-vehicle interactions. Moreover, GBI has fewer effects on driver distraction than TBI. Application Potential applications of this research include all in-vehicle interaction systems used by drivers.

Objective: We observe the effects of in-vehicle system gesture-based interaction versus touch-based interaction on driver distraction and user experience. Background: Driver distraction is a major problem for traffic safety, as it is a contributing factor to a number of accidents. Visual distraction in particular has a highly negative impact on the driver. One possibility for reducing visual driver distraction is to use new forms of interaction in the vehicle, such as gesture-based interaction. Method: In this experiment, participants drove on a motorway or in a city scenario while using touch-based interaction or gesture-based interaction. Subjective data, such as acceptance and workload, and objective data, including glance behavior, were gathered. Results: As a result, participants rated their subjective impressions of safe driving as higher when using gesture-based interaction. More specifically, acceptance and attractiveness were higher, and workload was lower. The participants performed significantly fewer glances to the display and the glances were much shorter. Conclusion: Gestures are a positive alternative for in-vehicle interaction since effects on driver distraction are less significant when compared to touch-based interaction. Application: Potential application of this research includes interaction design of typical in-vehicle information and entertainment functions.

Objective: The objective for this study was to investigate the effects of prior familiarization with takeover requests (TORs) during conditional automated driving on drivers' initial takeover performance and automation trust. Background: System-initiated TORs are one of the biggest concerns for conditional automated driving and have been studied extensively in the past. Most, but not all, of these studies have included training sessions to familiarize participants with TORs. This makes them hard to compare and might obscure first-failure-like effects on takeover performance and automation trust formation. Method: A driving simulator study compared drivers' takeover performance in two takeover situations across four prior familiarization groups (no familiarization, description, experience, description and experience) and automation trust before and after experiencing the system. Results: As hypothesized, prior familiarization with TORs had a more positive effect on takeover performance in the first than in a subsequent takeover situation. In all groups, automation trust increased after participants experienced the system. Participants who were given no prior familiarization with TORs reported highest automation trust both before and after experiencing the system. Conclusion: The current results extend earlier findings suggesting that prior familiarization with TORs during conditional automated driving will be most relevant for takeover performance in the first takeover situation and that it lowers drivers' automation trust. Application: Potential applications of this research include different approaches to familiarize users with automated driving systems, better integration of earlier findings, and sophistication of experimental designs.

Objective: The objective of the present research was to increase understanding of the phenomenon of range anxiety and to determine the degree to which practical experience with battery electric vehicles (BEVs) reduces different levels of range anxiety. Background: Limited range is a challenge for BEV users. A frequently discussed phenomenon in this context is range anxiety. There is some evidence suggesting that range anxiety might be a problem only for inexperienced BEV drivers and, therefore, might decrease with practical experience. Method: We compared 12 motorists with high BEV driving experience ( M = 60,500 km) with 12 motorists who had never driven a BEV before. The test drive was designed to lead to a critical range situation (remaining range < trip length). We examined range appraisal and range stress (i.e., range anxiety) on different levels (cognitive, emotional, and behavioral). Results: Experienced BEV drivers exhibited less negative range appraisal and range anxiety than inexperienced BEV drivers, revealing significant, strong effects for all but one variable. Conclusion: Hence, BEV driving experience (defined as absolute kilometers driven with a BEV) seems to be one important variable that predicts less range anxiety. Application: In order to reduce range anxiety in BEV drivers even when there is a critical range situation, it is important to increase efficiency and effectiveness of the learning process.

Objective: We report on four experiments that investigated the critical tracking task's (CTT) potential as a tool to measure distraction. Background: Assessment of the potential of new in-vehicle information systems to be distracting has become an important issue. An easy-to-use method, which might be a candidate to assess this distraction, is the CTT. The CTT requires an operator to stabilize a bar, which is displayed on a computer screen, such that it does not depart from a predefined target position. As the CTT reflects various basic aspects of the operational level of the driving task, we used it as a simple surrogate for driving to assess the CTT's capabilities. Method: We employed secondary tasks of varying demand, artificial tasks as well as tasks representative of secondary tasks while driving, and asked participants to perform them together with the CTT in parallel. CTT performance, secondary task performance, and subjective ratings of load were recorded and analyzed. Results: Overall, the CTT was able to differentiate between different levels of demand elicited by the secondary tasks. The results obtained corresponded with our a priori assumptions about the respective secondary tasks' potential to distract. Conclusion: It appears that the CTT can be used to assess in-vehicle information systems with regard to their potential to distract drivers. Additional experiments are necessary to further clarify the relationship between driving and CTT performance. Application: The CTT can provide a cost-effective solution as part of a battery of tests for early testing of new in-vehicle devices.

Objective: This study tested whether the ease of learning to use human—machine interfaces of in-vehicle information systems (IVIS) can be assessed at standstill. Background: Assessing the attentional demand of IVIS should include an evaluation of ease of learning, because the use of IVIS at low skill levels may create safety-relevant distractions. Method: Skill acquisition in operating IVIS was quantified by fitting the power law of practice to training data sets collected in a driving study and at standstill. Participants practiced manual destination entry with two route guidance systems differing in cognitive demand. In Experiment 1, a sample of middle-aged participants was trained while steering routes of varying driving demands. In Experiment 2, another sample of middle-aged participants was trained at standstill. Results: In Experiment 1, display glance times were less affected by driving demands than by total task times and decreased at slightly higher speed-up rates (0.02 higher on average) than task times collected at standstill in Experiment 2. The system interface that minimized cognitive demand was operated more quickly and was easier to learn. Its system delays increased static task times, which still predicted 58% of variance in display glance times compared with even 76% for the second system. Conclusion: The ease of learning to use an IVIS interface and the decrease in attentional demand with training can be assessed at standstill. Application: Fitting the power law of practice to static task times yields parameters that predict display glance times while driving, which makes it possible to compare interfaces with regard to ease of learning.

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.

Objective: The feasibility of measuring drivers' automation trust via gaze behavior during highly automated driving was assessed with eye tracking and validated with self-reported automation trust in a driving simulator study. Background: Earlier research from other domains indicates that drivers' automation trust might be inferred from gaze behavior, such as monitoring frequency. Method: The gaze behavior and self-reported automation trust of 35 participants attending to a visually demanding non-driving-related task (NDRT) during highly automated driving was evaluated. The relationship between dispositional, situational, and learned automation trust with gaze behavior was compared. Results: Overall, there was a consistent relationship between drivers' automation trust and gaze behavior. Participants reporting higher automation trust tended to monitor the automation less frequently. Further analyses revealed that higher automation trust was associated with lower monitoring frequency of the automation during NDRTs, and an increase in trust over the experimental session was connected with a decrease in monitoring frequency. Conclusion: We suggest that (a) the current results indicate a negative relationship between drivers' self-reported automation trust and monitoring frequency, (b) gaze behavior provides a more direct measure of automation trust than other behavioral measures, and (c) with further refinement, drivers' automation trust during highly automated driving might be inferred from gaze behavior. Application: Potential applications of this research include the estimation of drivers' automation trust and reliance during highly automated driving.

Objective: The objective of the present research was to advance understanding of factors that can protect against range anxiety, specifically range stress in everyday usage of battery electric vehicles (BEVs). Background: Range anxiety is a major barrier to the broad adoption of sustainable electric mobility systems. To develop strategies aimed at overcoming range anxiety, a clear understanding of this phenomenon and influencing factors is needed. Method: We examined range anxiety in the form of everyday range stress (ERS) in a field study setting. Seventy-two customers leased a BEV for 3 months. The field study was specifically designed to enable examination of factors that can contribute to lower ERS. In particular, study design and sample recruitment were targeted at generating vehicle usage profiles that would lead to relatively frequent experience of situations requiring active management of range resources and thereby potentially leading to experienced range stress. Results: Less frequent encounter with critical range situations, higher practical experience, subjective range competence, tolerance of low range, and experienced trustworthiness of the range estimation system were related to lower ERS. Moreover, range stress was found to be related to range satisfaction and BEV acceptance. Conclusion: The results underline the importance of the human factors perspective to overcome range anxiety and enhance sustainability of electric mobility systems. Application: Trustworthiness should be employed as a key benchmark variable in the design of range estimation systems, and assistance systems should target increasing drivers' adaptive capacity (i.e., resilience) to cope with critical range situations.

Objective: An evaluation study was conducted to answer the question of which system properties of night vision enhancement systems (NVESs) provide a benefit for drivers without increasing their workload. Background: Different infrared sensor, image processing, and display technologies can be integrated into an NVES to support nighttime driving. Because each of these components has its specific strengths and weaknesses, careful testing is required to determine their best combination. Method: Six prototypical systems were assessed in two steps. First, a heuristic evaluation with experts from ergonomics, perception, and traffic psychology was conducted. It produced a broad overview of possible effects of system properties on driving. Based on these results, an experimental field study with 15 experienced drivers was performed. Criteria used to evaluate the development potential of the six prototypes were the usability dimensions of effectiveness, efficiency, and user satisfaction (International Organization for Standardization, 1998). Results: Results showed that the intelligibility of information, the easiness with which obstacles could be located in the environment, and the position of the display presenting the output of the system were of crucial importance for the usability of the NVES and its acceptance. Conclusion: All relevant requirements are met best by NVESs that are positioned at an unobtrusive location and are equipped with functions for the automatic identification of objects and for event-based warnings. Application: These design recommendations and the presented approach to evaluate the systems can be directly incorporated into the development process of future NVESs.

Objective: To lay the basis of studying autonomous driving comfort using driving simulators, we assessed the behavioral validity of two moving-base simulator configurations by contrasting them with a test-track setting. Background: With increasing level of automation, driving comfort becomes increasingly important. Simulators provide a safe environment to study perceived comfort in autonomous driving. To date, however, no studies were conducted in relation to comfort in autonomous driving to determine the extent to which results from simulator studies can be transferred to on-road driving conditions. Method: Participants ( N = 72) experienced six differently parameterized lane-change and deceleration maneuvers and subsequently rated the comfort of each scenario. One group of participants experienced the maneuvers on a test-track setting, whereas two other groups experienced them in one of two moving-base simulator configurations. Results: We could demonstrate relative and absolute validity for one of the two simulator configurations. Subsequent analyses revealed that the validity of the simulator highly depends on the parameterization of the motion system. Conclusion: Moving-base simulation can be a useful research tool to study driving comfort in autonomous vehicles. However, our results point at a preference for subunity scaling factors for both lateral and longitudinal motion cues, which might be explained by an underestimation of speed in virtual environments. Application: In line with previous studies, we recommend lateral- and longitudinal-motion scaling factors of approximately 50% to 60% in order to obtain valid results for both active and passive driving tasks.