Automated driving has the potential to improve the safety and efficiency of future traffic and to extend elderly peoples' driving life, provided it is perceived as comfortable and joyful and is ...accepted by drivers. Driving comfort could be enhanced by familiar automated driving styles based on drivers' manual driving styles. In a two-stage driving simulator study, effects of driving automation and driving style familiarity on driving comfort, enjoyment and system acceptance were examined. Twenty younger and 20 older drivers performed a manual and four automated drives of different driving style familiarity. Acceptance, comfort and enjoyment were assessed after driving with standardised questionnaires, discomfort during driving via handset control. Automation increased both age groups' comfort, but decreased younger drivers' enjoyment. Younger drivers showed higher comfort, enjoyment and acceptance with familiar automated driving styles, whereas older drivers preferred unfamiliar, automated driving styles tending to be faster than their age-affected manual driving styles.
Practitioner Summary: Automated driving needs to be comfortable and enjoyable to be accepted by drivers, which could be enhanced by driving style individualisation. This approach was evaluated in a two-stage driving simulator study for different age groups. Younger drivers preferred familiar driving styles, whereas older drivers preferred driving styles unaffected by age.
► Three groups received (1) a correct, (2) an incomplete, and (3) an incorrect ACC description. ► Users’ mental model of ACC converged towards the profile of the correct group. ► ACC limitations that ...did not occur tended to drop out of the mental model. ► Trust and acceptance grew steadily for the correct condition. ► Omitted problems led to a constant decrease in trust and acceptance without recovery.
Adaptive cruise control (ACC) automates vehicle speed and distance control. Due to sensor limitations, not every situation can be handled by the system and, therefore, driver intervention is required. Trust, acceptance and mental model of system functionality are considered key variables for appropriate system use. This study systematically investigates the effect of divergent initial mental models of ACC (i.e., varying according to correctness) on trust, acceptance and mental model evolvement. A longitudinal driving simulator study was conducted, using a two-way (3×3) repeated measures mixed design with a matched sample of 51 subjects. Three experimental groups received (1) a correct ACC description, (2) an incomplete and idealised account omitting potential problems, and (3) an incorrect description including non-occurring problems. All subjects drove a 56-km track of highway with an identical ACC system, three times, and within a period of 6weeks. After using the system, participants’ mental model of ACC converged towards the profile of the correct group. Non-experienced problems tended to disappear from the mental model network when they were not activated by experience. Trust and acceptance grew steadily for the correct condition. The same trend was observed for the group with non-occurring problems, starting from a lower initial level. Omitted problems in the incomplete group led to a constant decrease in trust and acceptance without recovery. This indicates that automation failures do not negatively affect trust and acceptance if they are known beforehand. A strategy reliant upon trial-and-error alone is considered insufficient for developing an appropriate trust, acceptance and mental model. Implications on information and learning strategies are discussed.
As technological advances lead to rapid progress in driving automation, human-machine interaction (HMI) issues such as comfort in automated driving gain increasing attention. The research project ...KomfoPilot at Chemnitz University of Technology aims to assess discomfort in automated driving using physiological parameters from commercially available smartbands, pupillometry and body motion. Detected discomfort should subsequently be used to adapt driving parameters as well as information presentation and prevent potentially safety-critical take-over situations. In an empirical driving simulator study, 40 participants from 25 years to 84 years old experienced two highly automated drives with three potentially critical and discomfort-inducing approaching situations in each trip. The ego car drove in a highly automated mode at 100 km/h and approached a truck driving ahead with a constant speed of 80 km/h. Automated braking started very late at a distance of 9 m, reaching a minimum of 4.2 m. Perceived discomfort was assessed continuously using a handset control. Physiological parameters were measured by the smartband Microsoft Band 2 and included heart rate (HR), heart rate variability (HRV) and skin conductance level (SCL). Eye tracking glasses recorded pupil diameter and eye blink frequency; body motion was captured by a motion tracking system and a seat pressure mat. Trends of all parameters were analyzed 10 s before, during and 10 s after reported discomfort to check for overall parameter relevance, direction and strength of effects; timings of increase/decrease; variability as well as filtering, standardization and artifact removal strategies to increase the signal-to-noise ratio. Results showed a reduced eye blink rate during discomfort as well as pupil dilation, also after correcting for ambient light influence. Contrary to expectations, HR decreased significantly during discomfort periods, whereas HRV diminished as expected. No effects could be observed for SCL. Body motion showed the expected pushback movement during the close approach situation. Overall, besides SCL, all other parameters showed changes associated with discomfort indicated by the handset control. The results serve as a basis for designing and configuring a real-time discomfort detection algorithm that will be implemented in the driving simulator and validated in subsequent studies.
•Longitudinal on-road study with ten consecutive trips on a standardised route.•Power law of learning applies in case of prior information on system limitations.•Stabilization of trust, acceptance ...and learning at a high level after five trips.•System limitations that were not experienced tend to drop out of the mental model.•Periodic reminder of system’s limitations recommended.
To harness the potential of advanced driver assistance systems, drivers must learn how to use them in a safe and appropriate manner. The present study investigates the learning process, as well as the development of trust, acceptance and the mental model for interacting with adaptive cruise control (ACC). Research questions aim to model the learning process in mathematical/statistical terms, examine moments and conditions when these processes stabilize, and assess how experience changes the mental model of the system. A sample of fifteen drivers without ACC experience drove a test vehicle with ACC ten consecutive times on the same route within a 2-month period. All participants were fully trained in ACC functionality by reading the owner’s manual in the beginning. Results show that learning, as well as the development of acceptance and trust in ACC follows the power law of learning. All processes stabilize at a relatively high level after the fifth session, which corresponds to 185km or 3.5h of driving. No decline is observable with ongoing system experience. However, limitations that are not experienced tend to disappear from the mental model if they are not activated by experience. Therefore, it is recommended that users be periodically reminded of system limitations (e.g. by intelligent tutoring systems) to make sure that corresponding knowledge nodes are activated.
•Younger and older drivers consider driving automation trustworthy and acceptable.•The initial system experience significantly increases trust and acceptance.•After the initial system experience, ...trust and acceptance remain on a stable level.•Especially older drivers show a positive attitude towards driving highly automated.•Age-specific acceptance barriers regarding automotive technologies are identified.
Highly automated driving (HAD) is expected to improve future road transport, especially for older adults, provided that it is trusted and accepted by drivers. Research on Advanced Driver Assistance Systems (ADAS) suggests that system experience can enhance drivers’ trust and acceptance. To evaluate the transferability of this result to HAD, we examined the development of drivers’ trust and acceptance regarding this technology at different stages of system experience in a driving simulator as well as on a test track. Age effects were additionally addressed by comparing the results of 20 younger (25–45 years) and 20 older (65–85 years) drivers in the driving simulator study. Trust and acceptance were assessed before the initial system experience as well as after the first and second automated drive. Both age groups showed slightly positive a priori trust and acceptance ratings, which significantly increased after the initial experience and remained stable afterwards. Older drivers reported a more positive attitude towards using HAD despite their lower self-assessed self-efficacy and environmental conditions facilitating HAD-usage (e.g. technical support) compared to younger drivers. In the subsequent test track study, trust and acceptance of the younger driver group were assessed before and after experiencing HAD in a test vehicle. Neither trust nor acceptance decreased despite the absence of further system experiences between both studies and the increased realism on the test track. These results underline the importance of the initial system experience for HAD-trust and –acceptance and emphasize the significance of automotive technologies for the preservation of older drivers’ mobility.
In this paper we introduce a new fuzzy system using adaptive fuzzy pattern classification (AFPC) for data-based online evolvement. The fuzzy pattern concept represents an efficient tool for handling ...uncertainty in multi-dimensional data streams and combines powerful performance, flexibility and meaningful interpretability within one consistent framework. We outline AFPC for non-linear, multi-dimensional transition processes, namely, for the identification of lane change intention in car driving. While lane changes are rare, they are highly safety-relevant transition processes, showing high fuzziness and large individual and inter-individual variations (e.g., in lane change duration). The method employs a combined knowledge- and data-based approach, and the underlying fuzzy potential membership function concept models expert knowledge, closely mirroring human cognition. The design of AFPC comprises (I) an initial training phase (off-line and supervised), which generates a meaningful start-classifier, (II) an online application phase, and finally (III) an evolvement phase (online and unsupervised). Here we consider parametric and structural adaptations and discuss prospects and future challenges. Furthermore, we present specific modeling results for such online data from a real driving study. Next-generation advanced driver assistance systems, as well as autonomously driven vehicles need to evolve, in terms of parameters and structure, based on online real-time data. AFPC presents an efficient tool for application in this area and others (e.g., medicine).
To ensure traffic flow and road safety in automated driving, external human-machine interfaces (eHMIs) could prospectively support the interaction between automated vehicles (AVs; SAE Level 3 or ...higher) and pedestrians if implicit communication is insufficient. Particularly elderly pedestrians (≥65 years) who are notably vulnerable in terms of traffic safety might benefit of the advantages of additional signals provided by eHMIs. Previous research showed that eHMIs were assessed as useful means of communication in AVs and were preferred over exclusively implicit communication signals. However, the attitudes of elderly users regarding technology usage and acceptance are ambiguous (i.e., less intention to use technology vs. a tendency toward overreliance on technology compared to younger users). Considering potential eHMI malfunctions, an appropriate level of trust in eHMIs is required to ensure traffic safety. So far, little research respected the impact of multiple eHMI malfunctions on participants' assessment of the system. Moreover, age effects were rarely investigated in eHMIs. In the current monitor-based study,
= 36 participants (19 younger, 17 elderly) repeatedly assessed an eHMI: During an initial measurement, when encountering a valid system and after experiencing eHMI malfunctions. Participants indicated their trust and acceptance in the eHMI, feeling of safety during the interaction and vigilance toward the eHMI. The results showed a positive effect of interacting with a valid system that acted consistently to the vehicle's movements compared to an initial assessment of the system. After experiencing eHMI malfunctions, participants' assessment of the system declined significantly. Moreover, elderly participants assessed the eHMI more positive across all conditions than younger participants did. The findings imply that participants considered the vehicle's movements as implicit communication cues in addition to the provided eHMI signals during the encounters. To support traffic safety and smooth interactions, eHMI signals are required to be in line with vehicle's movements as implicit communication cues. Moreover, the results underline the importance of calibrating an appropriate level of trust in eHMI signals. An adequate understanding of eHMI signals needs to be developed. Thereby, the requirements of different user groups should be specifically considered.
The study aimed at investigating how drivers use Adaptive Cruise Control and its functions in distinct road environments and to verify if changes occur over time. Fifteen participants were invited to ...drive a vehicle equipped with a Stop & Go Adaptive Cruise Control system on nine occasions. The course remained the same for each test run and included roads on urban and motorway environments. Results showed significant effect of experience for ACC usage percentage, and selection of the shortest time headway value in the urban road environment. This indicates that getting to know a system is not a homogenous process, as mastering the use of all the system's functions can take differing lengths of time in distinct road environments. Results can be used not only for the development of the new generation of systems that integrate ACC functionalities but also for determining the length of training required to operate an ACC system.
•Mastering each ACC system function was a process completed in a few trials.•Changes registered over time were dependent on the road environment.•Usage rate discrepancy between road environments faded away significantly with time.•Shortest time headway significantly more used over time on the urban road.•Existence of distinct overriding strategy pattern for each road environment.
Automated vehicles will soon be integrated into our current traffic system. This development will lead to a novel mixed-traffic environment where connected and automated vehicles (CAVs) will have to ...interact with other road users (ORU). To enable this interaction, external human–machine interfaces (eHMIs) have been shown to have major benefits regarding the trust and acceptance of CAVs in multiple studies. However, a harmonization of eHMI signals seems to be necessary since the developed signals are extremely varied and sometimes even contradict each other. Therefore, the present paper proposes guidelines for designing eHMI signals, taking into account important factors such as how and in which situations a CAV needs to communicate with ORU. The authors propose 17 heuristics, the so-called eHMI-principles, as requirements for the safe and efficient use of eHMIs in a systematic and application-oriented manner.
The driving style of an automated vehicle (AV) needs to be comfortable to encourage the broad acceptance and use of this newly emerging transport mode. However, current research provides limited ...knowledge about what influences comfort, how this concept is described, and how it is measured. This knowledge is especially lacking when comfort is linked to the AV’s driving styles. This paper presents results from an online workshop with nine experts, all with hands-on experience of AVs and a long track record of research in this context. Using online tools, experts were invited to introduce concepts they considered relevant to comfort/discomfort in currently available modes of transport which offer a ride (taxi/bus/train) to users and compare these to the concepts used to define comfort and discomfort in AVs. Results showed that a wide range of terms were used to describe user comfort and discomfort for both modes. Although all terms used for existing vehicles were found to apply to AVs, additional terms were proposed for determining comfort/discomfort of AVs. For example, to enhance comfort in AVs, designers should consider good communication channels, as well as ensuring that the AV’s capabilities match users’ expectations. Results also revealed that more terms were used, overall, to define discomfort, and that a comfortable ride in AVs is not just about mitigating discomfort. New concepts specific to AVs were also revealed when considering what increases their discomfort, such as whether riders’ safety and privacy are affected, or if they feel in control. Experts’ input from the workshop was used to enhance and expand a simple conceptual framework, explaining how AV driving styles, as well as other, non-driving-related factors, affect user comfort. It is hoped that this framework provides a more comprehensive list of the concepts affecting user comfort, also allowing more accurate measurement of the concept. As well as allowing for a more accurate comparison between empirical studies measuring comfort in AVs, this study will facilitate the design of more comfortable and acceptable automated driving for future vehicles.
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