HCI in Mobility, Transport, and Automotive Systems

Urban mobility is changing due to the emergence of new technologies like autonomously navigating robots. In the future, various transport operators and micro mobility services will be integrated in an increasingly complex mobility system, potentially realizing benefits such as a reduction of congestion, travel costs, and emissions. The field of personal robotic transport agents is projected to increasingly play a role in urban mobility, hence in this study, prospective target groups and corresponding user needs concerning human-following robots for smart urban mobility applications are investigated. Building on an extensive literature review, three focus groups with a total of 19 participants are conducted, utilizing scenario-based design and personas. Results show clearly definable user needs and potential technological requirements for mobile robots deployed in urban road environments. The two most mentioned potential applications were found in the fields of leisure applications and in healthcare for elderly people. Based on these focus group results, two personal automated driving robots which differ in function, operation and interaction were designed. The focus group-based results and derived requirements shed light on the importance of context-sensitivity of robot design.

This paper outlines our process of researching, developing, and implementing a data analysis and visualization framework for Electric Bus (eBus) implementation – battery and hydrogen – in Canada, described as Zero Energy Buses (ZEB) using Human Computer Interaction strategies. Our research team works in collaboration with the Canadian Urban Transit & Innovation Research Consortium (CUTRIC), the coordinating agency for research and eBus trials across Canada. Our paper reviews factors considered to provide a meaningful systems analysis of eBus implementation, relevant data resources, and user-centric design approaches that will mitigate risks to create a successful eBus system analytics tool able to support diverse users. We are developing innovative techniques able to manage and represent the large volume of multisource spatial and temporal data in this new field of transit electrification including 2D/3D space-time and 4D visualization; real-time and predictive visualizations resulting from generative algorithms. Visual analytics will allow the careful tracking of multiple data sources, to measure the process of adoption, providing tools for monitoring of services, consumer attitudes, user experience, and prediction of impacts. Visual analytics provides a comprehensive overview of implementation nationally, enabling opportunities to share data, assess progress, identify challenges, and to explore challenges in this new field. Based on user consultations we are developing a Sustainable Transit Planning Index which considers multiple environmental, economic and equity/access factors in supporting ZEB implementation in Canada and applicable to other jurisdictions. It will extract and link local ZEB implementation data into a national framework.

The continuous development in technology allows to have fully autonomous vehicles (AVs) on the market. Travelers are interested in maximizing their utility onboard by involving themselves in multitasking. Does choosing a particular type of multitasking determine what type of transport mode to be used? Several studies are conducted on conventional transport modes (CTMs); however, scarcely can be found on AVs. A stated preference (SP) survey including sociodemographic and trip characteristics as well as discrete choice experiments (DCEs) is used. The impact of multitasking on travel behavior is assessed, where multitasking is divided into six types of activities based on the characteristics of an activity (i.e., active, or passive activity). The random utility theory approach including the discrete choice modeling is applied, where transport choice models for the shared autonomous vehicle (SAV) and public transport (PT) are developed considering time, cost, and multitasking availability. The results demonstrate that each onboard activity has a different impact on the transport mode choice, and the social media activity has the largest positive, while the only writing activity shows a negative impact on the transport mode choice. Moreover, the impact of travel time on multitasking is higher than that of the travel cost. Additionally, the changes in the travel time and the travel cost do not show strong and high differences between onboard activities. SAV is more affected by a change in the onboard activities, the travel time, and the travel cost than PT. In conclusion, the results show that inside urban areas, PT is more likely to be used than SAV concerning the multitasking possibility, the travel time, and the travel cost.

We present a proposal for a generic vehicle anti-theft system. The system has two parts – a platform located in the protected object and a user interface on a user’s phone or computer. A specific system is described in an XML fie, which is made by an anti-theft system designer using an interface described in this paper. High-level code to run on the protected object portion of the system is automatically generated from the XML description. After a system is deployed to a protected object, the user (owner, police, etc.) interacts with the protected device through a user interface described in this paper and can read status of the protected object as well as control devices on the object. The user interface is also generated automatically from the XML description. The system is generic and could be used to describe anti-theft systems for a variety of types of objects such as cars, trucks, boats, ATM machines, shipping containers, and others.

Urban areas encounter a number of particular challenges due to increasing numbers of residents and thus strongly growing traffic with motorized private transport. Along with this, pollution degrades air quality, traffic noise affects residents and parking spaces take up a large part of urban public space. We need intelligent solutions that offer an attractive alternative to the use of private cars. In our view, technology has the potential to solve some of our current mobility challenges, but it is not a solution per se. We claim that as a first step we must define what kind of cities we do consider worth living in, what kind of mobility enhances our quality of life. Right from the start of the development process, we further advocate a continuous analysis of the psychological and sociological effects of the planned changes. We therefore, would like to give an overview of the topics that research and development projects are currently dealing with in the area of human-technology interaction in Germany and show some exemplary projects, which started with the analysis of user needs and work on socio-technological innovations with user-centered processes.

Nowadays, the society is highly intelligent, and smart city has become the common expectation of people. Smart city is a large concept, which includes smart home, Smart Transportation, Smart public, Service and Social management, smart Urban Management and other aspects. This paper mainly discusses the problem of the mobility in cities. The mobility in cities can be divided into two categories in practice, namely, the movement of humans and the movement of goods. In this paper, we enumerate the typical cases in the mobility of humans and goods, and analyze the operation principle of these cases one by one. Some of these examples are implemented, some are not. In order to make urban mobility “smart”, we propose a framework of assistive system, which has a four-tier structure and uses big data, cloud computing, AI, deep learning and other technologies. It is aimed at a variety of different scenes, to provide users with the most thoughtful advice and the most humanized service, to solve the problem of urban mobility.

As part of the OPTIMOS 2.0 funding project of the German Federal Ministry for Economic Affairs and Energy (BMWi), a partner develops the TicketIssuance app for secure hardware-based storage of high-priced tickets. The app has implemented a previously unknown technology using the Secure Element and the NFC interface. It is therefore imperative to investigate the usability of the app for a successful market launch. For this purpose, user tests of a prototype of the app were conducted using the think-aloud method. This study analyses the results of five tasks. Test subjects rate the expected and perceived difficulty level for each task. That forms the basis for identifying improvement strategies. The test subjects’ performance, the frequency of errors and problems encountered, and the need for moderator’s support form the basis for prioritizing usability items within the tasks. The developed structure to determine the test tasks’ prioritization and usability items, layout, navigation, handling, wording, system, and data economy offers improvements to increase usability.Furthermore, the study investigates the determination of a suitable sample size for usability testing.

The use of augmented reality is becoming more and more commonplace. Whether via smartphone apps, on television or while driving a car, almost everyone has already encountered this technology more or less knowingly. In this paper, we investigate to what extent Augmented Reality (AR) can be used profitably in public transport, especially on semi-transparent display screens. These semi-transparent and interactive public displays are built-in as windows in public transport vehicles. In our research project SmartMMI these mobile public displays are called “SmartWindows”. We implemented an iterative evaluation concept containing of four phases to assess different types of AR content to be displayed on such a SmartWindow as well as the special interest of individual types of users. Based on the results of two conducted online studies, specific user scenarios were created and evaluated in a lab-based user study. Finally, we designed an online survey to determine which AR concepts are most accepted by users. In this video-based survey, we evaluated the running speed of AR object inserts, the colors, the fade in’s and the types of information displayed on a semi-transparent display. In this paper, we introduce all four stages of our iterative evaluation process, and focus further on study methodology, set-up and the results of the video-based online survey.

Currently, the problem of urban parking is getting serious. Limited by laws and regulation, cannot go through improving the parking fee to reduce the demands. This research tries to use the internet of things (IoT) and blockchain to provide a carpooling system to reducing the needs of parking and urban traffic congestion. The research design to build a platform which is through social internet of the vehicle to make a connection between urban of vehicle and need of customers. Making passengers who have the same destination can ride the car together at any time and reducing the needs of urban parking. The passenger needs to watching advertising and provide the basic information itself to exchange the service of riding. Through the simulation could find that the design mechanism could achieve the expecting, reducing the needs of parking problem during the urban peak hours. If the design mechanism and service pattern could attract most users and make a profit from user data, it is possible to let the system operation continued. Meanwhile, integrate the autopilot system could make the service system to become an important application of traffic service in the smart city.

There are more and more application scenarios for unmanned delivery vehicles, and traditional human-computer interaction methods can no longer meet different task scenarios and user needs. The primary purpose of this paper is to apply natural human-computer interaction technology to the field of unmanned delivery, changing the traditional mode that users can only operate unmanned delivery vehicles through the touch screen to complete tasks. The task scenarios of unmanned delivery vehicles are classified through the concept of context. Participatory design and heuristic research are used to allow users to define interactive gestures. Two groups of gesture interaction set that meet different task scenarios and can be accepted and understood by general users are designed. Based on Kinect’s deep imaging and bone tracking technology, large number of preset gesture samples are collected, and the Adaboost algorithm is used for machine training to realize gesture interaction. Through recognition and detection, it is proved that the gesture recognition achieved by this method has a high recognition rate and responding speed. Finally, based on Unity3D, the task scene of the real unmanned delivery vehicle is simulated in the virtual scene. Through the usability test of the human-computer interaction system, it is concluded that this interaction mode guarantees the task efficiency to a certain extent and improves the user experience.

The fifth-generation mobile communication technology (5G) is the latest generation. Under 5G environment, the automatic driving technology will be upgraded, and the automobile networking will be developed, rendering the cars smarter and more closely connected with each other. However, the existing researches all focus on technologies such as autonomous driving, car networking, etc., and lack the researches concentrated on the evolution of car functions under 5G environment. In this paper, the innovative car function research method based on Kano model is constructed, and the promotion of 5G technology to car networking, auto-driving, VR/AR, mobile office and so on is deeply studied. The functional requirements of users are obtained through interviews and expert evaluation methods, and Kano questionnaire survey is carried out as well. By sorting and analyzing the data, the real functional requirements of intelligent network connected car are derived. The core functions are screened through Better-Worse coefficient analysis, and the innovative function model of intelligent network connected cars is thereafter formed. This study makes up for the lack of researches in this field, and constructs the Kano model research method for automobile functions, and simultaneously digs out the users’ needs, which has immense theoretical and practical significance. By redesigning the functions of intelligent network connected car under 5G environment, the potential of 5G technology can be activated and the market’s satisfaction of intelligent network connected cars can be improved. It also provides a model for the function design of intelligent network connected cars in the future.

On-street Vehicle-to-Grid (V2G) allows battery electric vehicles (BEVs) to park on the street, charge from the electricity grid, and give electricity back to the grid. It could encourage people without off-street parking at home to adopt electric vehicles. On-street V2G is technically feasible, but there is a lack of understanding of consumer perceptions of On-street V2G features and services. This study aims to quantitatively and empirically explore people’s attitudes towards On-street V2G with a focus on preference and willingness-to-pay. An online survey was carried out and 495 successful responses were collected. In the survey, a video clip that explained On-street V2G was included to better sensitise participants to the futuristic scenario of On-street V2G. This study found that ‘required plug-in hours per month’ was viewed as more important regarding preference for On-street V2G, while ‘minimum level of battery guaranteed’ was more important regarding willingness-to-pay. People hold opposite views towards these two features concerning preference and willingness-to-pay. The theoretical contribution, practical implications, and future research directions are discussed.

The development towards autonomous driving is about to change our future mobility. Though there is public consent on perceived benefits of autonomous driving, potential drawbacks are also considered. In this survey, we focused on the user-centered assessment of autonomous driving in comparison of two traffic scenarios (“city” vs “highway”) using an online questionnaire. Participants were generally more affirmative in regard to using autonomous vehicles on the highway than in the urban area. Attributions measured on a semantic differential were more positive with respect to highway use. Motives regarding the use of autonomous vehicles differed between the scenarios, mainly in terms of the simplicity and complexity of the driving situation, with autonomous vehicles being considered feasible for highways but not for cities. However, some evaluation patterns were independent of the context, revealing controversial attitudes and perceived trade-offs in both scenarios. Participants indicated positive expectations regarding the novel transport experience, but also fears of use, mainly described in terms of control issues. Findings of this survey can be used in further research in the field of human-automation interaction and build the basis for education and communication concepts to increase public awareness.

Recent studies illustrate that people have negative attitudes towards utilizing autonomous systems due to lack of trust. Moreover, research shows a human-centered approach in autonomy is perceived as more trustworthy by users. In this paper, we scrutinize whether passengers expect self-driving cars (SDC) to mimic their personal driving behaviors or if they hold different expectations of how a SDC should drive. We developed a survey with 46 questions that asked 352 participants about their personal driving behaviors such as speed, lane changing, distance from a car in front, acceleration and deceleration, passing vehicles, etc. We further asked the same questions about their expectations of a SDC performing these tasks. Interestingly, we observed that most people prefer a SDC that drives like a less aggressive version of their own driving behaviors. Participants who reported they trust or somewhat trust AI, autonomous technologies, and SDCs expected a car with behaviors similar to their personal driving behaviors. We also found that the expectation of a SDC’s level of attenuated aggressiveness witnessed among all other participants was relative to their personal driving behavior aggressiveness. For instance, male drivers showed to be more aggressive drivers than female drivers, and therefore, their expectations for a SDC was slightly more aggressive. These findings can be useful in developing certain profiles or settings for SDCs, and overall they can help in designing a SDC that is perceived as trustworthy by passengers.

Understanding public attitudes, sentiments, and perceptions is a significant first step to the widespread acceptance of autonomous vehicles (AVs). In recent years, many studies been conducted in recent years examining the general perception of AVs. The implementation of AVs has potential challenges involving pedestrians and bicyclists that need special attention. The current study analyzes survey data obtained from BikePGH, a non-profit organization in Pittsburgh, Pennsylvania . This study applied multiple correspondence analysis (MCA) to explore response patterns among the participants. The findings of the survey reveal that certain groups of people are much more receptive to AV technology than others who are vehemently opposed to the introduction of AVs to the roadways. The findings indicate that participants who have real world experience with human-AV interactions have more positive expectations and a higher level of interest than participants with no previous experience. The authors expect that these findings will contribute greatly to the development of safety policies related AV and pedestrian interactions.

Motion sickness has gained renewed interested in the context of the developments in vehicle automation in which we are witnessing a transition from a driver-centric to passenger-centric design philosophy. As a corollary, motion sickness can be expected to become considerably more prevalent which creates a hurdle towards the successful introduction of vehicle automation and its ultimate socio-economic and environmental benefits. We here review early proof-of-concept studies into the beneficial effects of providing passenger with predictive motion cues as an elegant and effective method to reduce motion sickness in future vehicles. Future design parameters are discussed to finetune such cues not only for optimum effectiveness but, importantly, also for acceptance including sensory modality, timing, information detailing, and personalization.

In manual driving, implicit cues play an important role in the communication of intention and anticipation of upcoming driving situations. Considering mixed traffic situations, all interaction partners need to be able to detect and interpret implicit cues, as they are central to design smooth, efficient, and safe driving styles. However, most current automated driving functions do not incorporate the communication and anticipation of implicit cues. The lack of anticipation of upcoming events in automated driving increases the probability of inadequate actions (e.g., sudden breaking maneuver). This concerns especially freeway situations as the driving speeds are considerable high, requiring more anticipation. To show the importance of implicit cues, a study on German freeway was conducted where over 1000 km of 360° video material was recorded. The video material was then annotated with the focus on the identification of situations where implicit cues can be observed. Beside the situations, implicit and explicit cues as well as contextual information were annotated, too. The results show i) that specific situations can be categorized where ii) implicit and explicit as well as contextual information can be identified. Beside the findings of the study, the article provides an outlook on a naturalistic driving study design to examine implicit cues during the drive.

With the introduction of automation, vehicles have become increasingly complicated and difficult for users to understand. Users’ understanding of Automated Vehicles (AVs) is a key aspect for safe and successful implementation of AVs. However, more research is needed into how users understand AVs based on actual use experience. In this paper, users’ understanding of AVs is explored by investigating how they refer to and describe an AV, during and after initial usage. 18 participants experienced a seemingly fully automated vehicle, being driven with two distinctly different driving styles on a test course. The findings show that the participants had specific preconceptions of what they regarded as machine-like versus human-like driving characteristics of the AV. The participants also referred to the AV with gender pronouns and used human similes to describe the different driving styles. The different driving styles evoked different associations that influenced the participants’ perceptions of AV behaviour as a result of individual preconceptions and previous experiences. The results imply that the participants initially used a human-like mental model of the AV. However, further investigations are necessary into users’ initial comprehension of AVs, to better understand how they will experience and interact with future AVs.

Vehicle’s ADAS technologies are expected to provide benefits such as reducing accidents caused by human error while driving and improving the driving experience by supporting the driver in some driving tasks. This study summarizes how the haptic feedback support from the joystick affects the driver's visual actions, mental workload and user feeling when using autonomous driving. 24 elderly drivers (M = 67/SD = 4.3) took part in a driving simulation experiment. The result showed that there was a significant reduce in driver's visual actions with the haptic support, as the haptic feedback from the joystick provided a sense of reassurance and reduced the visual burden on the monitoring task when using ADAS. Also, the subjective evaluation NASA-TLX results showed that the haptic feedback of the joystick should not be a high mental and physical burden to the user.

Automation offers a new way of driving, but often the human error (HE) in the process of take-over results in adverse effects of unrecognized risks. Hence, the impact of HE in safety of automated driving remains a major problem. This paper proposed a Human error analysis method based on analysis of the root cause of HEs events to understand the process of take-over and identify root cause of take-over failure in automated driving. Simulated driving practice with videos and questionnaire were conducted to identify the main factors leading to HEs in take-over. Human factors events diagram was used to better understand take-over as a human factor event and to provide information for root cause of take-over failure recognition. The results reveal that the most common failure mode in take-over is cognition error caused by driver poor mental state such as driver fatigue and reaction ability, followed by control error caused by inappropriate take-over request (TOR). Determination of these failure modes provide evidence for increasing or repairing barriers in the process of take-over. The suggested cognition-corresponding model of take-over showed that take-over is a complex human-machine interaction process, thus the causes of HEs should be discussed from a multi-dimensional perspective, and explored through empirical research.

With the rapid development of the intelligent vehicle industry, in order to improve the efficiency and usability of systems, the interface design of the Human-Computer Interaction of intelligent vehicles has received great attention. In this article, firstly, combined with the domestic and foreign taxonomy of automated driving systems and collaborative design concepts, a framework of human-computer collaborative interaction (Human-Engaged Automated Driving, HEAD) is proposed. Secondly, we discussed the engagement of people and intelligent vehicle at different levels. We analyzed the interaction flow chart under the Human-Computer Collaboration, established the information architecture of Human-Computer Engagement, and conducted the design practice on the Human-Machine Interface. Finally, a case study of the HMI design of Adaptive Cruise Control was performed to validate the HEAD framework. SUS usability test was performed together with an experiment evaluating interface elements in two interface designs: Original UI design and Iterative UI design. This also including a Likert scale questionnaire. The results show that HEAD can guide and improve the Human-Machine Interface design to enhance the efficiency of Human-Computer Interaction and user experience.

To investigate the full potential of non-speech sounds, this study explored the effects of different multimodal takeover request displays in semi-automated vehicles. It used a mixed design - the visual and auditory notification lead time was within-subjects, whereas the auditory notification spatiality was between-subjects. The study was conducted in a motion-based driving simulator with 24 participants. All participants were engaged in four 9-min driving tasks in level-3 automated vehicle and simultaneously performed a non-driving related task (NDRT, online game). Each driving session contained three hazardous events with takeover request (in total 12 requests per user). The results showed that 3-s lead time evoked the fastest reaction time but caused high perceived workload and resulted in unsafe and non-comfortable maneuver. In terms of workload and maneuver, 7-s lead time showed better results than others. Auditory displays with directional information provided significantly better reaction times and reaction types. Subjective evaluation, on the other hand, did not show any significant differences between non-directional and directional displays. Additionally, the results showed that braking is a more common first reaction than steering, and that the NDRT did not influence the takeover request.

We argue that driving styles demand adaptive classifications, and such mechanisms are essential for adaptive and personalized Human-Vehicle Interaction systems. To this end, we conduct an in-depth study to demystify complicated interactions between driving behaviors and styles. The key idea behind this study is to enable different numbers of clusters on the fly, when classifying driving behaviors. We achieve so by applying Self-Clustering algorithms (i.e. DBSCAN) over a state-of-the-art open-sourced dataset of Human-Vehicle Interactions. Our results derive 8 key findings, which showcases the complicated interactions between driving behaviors and driving styles. Hence, we conjecture that future Human-Vehicle Interactions systems demand similar approaches for the characterizations of drivers, to enable more adaptive and personalized Human-Vehicle Interaction systems. We believe our findings can stimulate and benefit more future research as well.

Automobile intelligence improves the perception and decision-making capabilities of cars. This change of technology makes human and machine a joint cognitive and decision-making system, consequently changing the paradigm of human-machine interaction. The machine is no longer a mere tool but a team partner. Both academia and industry are actively investigating human-machine cooperative driving, such as take over, shared control and cooperative driving. However, currently, there is no cooperative driving framework that considers the cognitive characteristics of the interaction between humans and agents. We propose a cooperative interface interaction framework based on human-machine team cognitive information elements that need to be exchanged by both parties in cooperative driving, such as intention, situation awareness, prediction, and their impact on driving tasks. The proposed framework can provide a cognitive dimension for cooperative driving research, which can be used as a reference for the design of interaction in highly automated vehicles.

In an optimally integrated HMS (Human Machine Systems) human and machine understand each other to provide an optimum integration. This is one of the core principles which is applicable for the research frameworks in vehicle navigation domain for effectively conducting research for creating optimal guidance information for the human driver. Creation and integration of human cognitive models for navigation is necessary to follow this principle effectively. BeaCON: Behaviour-and Context-Based Optimal Navigation is an existing research framework in the car navigation domain, for conducting analysis for the research problem “Giving the driver adequate navigation information with minimal interruption”. Currently BeaCON does not use the human cognitive models for navigation for the creation of guidance information and because of that the integration with the human driver is not achieved to an optimum level. In this paper, we present enhancement of BeaCON by integrating behaviour and cognitive models of navigation. Understanding the human thoughts while driving enables BeaCON to have a granular analysis of user cognitive state while creating guidance information, which results further cognitive load reduction for navigation tasks by creating more effective guidance information.

While driving, the drivers have to put their hands on the steering wheel and keep their eyes on the forward. Besides, the distance from the driver to the screen of the central control panel (center stack) of a vehicle is also limited, which lowers the precision of the interaction between drivers and the wireless communication, entertainment, and driver assistance systems in the vehicle. Fortunately, the idea of Active Interaction of AI-Empowered Product, which enables the systems to provide scenario-based recommendations, could be a solution, which is aiming to enhance the user experience of the interaction between the drivers and the systems in the vehicle during driving. So how could it be created to better meet the behavior habits of drivers? Twenty-four drivers were recruited as participants and were asked to give subjective evaluations about their satisfaction and the degree of disturbance of the scenario-based recommendation function. We simulated the AI Empowered Product, which provides a precise recommendation to meet the drivers’ needs in the vehicle through a Wizard-of-Oz Experiment, and explored factors which we proposed that may have an influence on the drivers feeling and reaction to the active interaction of AI-Empowered Product in the vehicle, including (a) the real-time traffic conditions outside, (b) the in-vehicle driver distraction, for instance, whether the driver was listening to music or not, (c) the content of the suggested information, and (d) different ways of information transmission. Furthermore, we also observed and analyzed the drivers’ reactions to the active interaction. With the ANOVAs of the satisfaction scores and the degree of disturbance evaluated by participants, combined with the analysis of the participants’ reactions, the results show that (a) regarding safety, drivers are not willing to accept recommendation during a traffic jam in which the drivers have to focus on the road conditions; (b) they are more reluctant to be bothered while listening to music; (c) they prefer to accept information that helps them boost driving efficiency (d) they like audio message best, because it is the most efficient way for them to acquire and understand the information, and it would be better if with the visual presentation, such as pictures, to improve the efficiency of information acquisition. Based on the experimental results and behavior analysis, we concluded suggestions for the product design process.

As the market share of electric bicycles (e-bikes) continues to rise, the number of related deaths and injuries e-bike also increase. Road safety for e-bike cyclists constitutes an emerging public health challenge. This survey study adopted the Driving Activity Load Index (DALI) measure and used subjective anxiety and arousal perception measures to investigate the psychological factors influencing e-bike cycling crashes. The survey was recruited cyclists who visited repair stations to maintain their e-bikes. A total of 180 individual e-bike owners completed a paper-and-pencil version of the survey. A Python-based Random Forest Regression algorithm from the scikit-learn library was adopted for predicting regression. Results showed that the model constructs of DALI, age, anxiety, and arousal were useful predictors of e-bike crashes in traffic environments. Among these factors, the DALI value was the strongest predictor. It verified that cyclist-perceived mental load while e-bike ride on the road is very important in the context of road safety.

The purpose of this study is to propose a concept design for an In-Vehicle Information System (IVIS) of an autonomous bus that provides an Autonomous Mobility on Demand (AMoD) service. First, we conducted a literature study to investigate what information is salient to the driving conditions of autonomous buses followed by a user diary method and in-depth interview approach to investigate what information passengers need. The key findings are as follows. First, autonomous buses operate on different operating conditions from existing city buses; thus, new information needs to be delivered to passengers accordingly. Second, the information changes in real-time as autonomous buses operate based on user demand. Third, the information must be delivered to passengers such that they reach their destination. Finally, all of the information passengers need should be communicated in in an easy-to-understand and convenient way. Based on these findings, the IVIS concept design was proposed from a user-centered design perspective. To verify user acceptance of the proposed concept design, we configured five scenarios and conducted one-on-one interviews. The proposed concept design contributes insofar as it investigated what kind of information is needed for passenger experience before the commercialization of autonomous buses. We hope that our findings will help designing of an IVIS for autonomous buses for a better bus travel experience for passengers in the future.

Despite being a comparatively recent phenomenon, e-scooters enjoy an immense popularity in the cities in which they are available. As a result, their potential impact on road safety has been questioned, as injury crashes and violations of road rules are reported with increasing frequency. It can be suspected that at least to some degree, a lack of awareness with regard to the existing rules, coupled with a certain lack of agreement with these rules, might be the cause. Aim of the study presented in this paper was to quantify this issue with the help of an online survey. With a usable sample of 337 participants (105 users of e-scooters, 232 non-users), we looked into rule knowledge, agreement with the rules (users only) and past behaviour, including violations, while riding (users only). The results indicate that there might be reason for concern. While users seemed to outperform non-users on the knowledge questions, their rates of correct responses were far from perfect. Among the users, agreement with the rules was generally high, yet there were also clearly visible minorities who considered several rules as being too strict. It seems that in order to improve compliance, there is a need for a structured instruction of the users that addresses issues of rule knowledge, but also instils the motivation to follow these rules.

E-scooters are a comparatively new means of transportation. At the same time, it is considered more dangerous than established means of transport such as bicycles. To find out more about the traffic behavior of e-scooter riders, we investigate the performance of shoulder glances. To do this, we first had to define when a turning over the shoulder was counted as complete shoulder glance. In a study with 21 subjects, we used mobile eye-tracking glasses to investigate whether the gaze behavior of e-scooter riders and cyclists differs. In the situations we investigated where shoulder glances are necessary to protect oneself from other road users, we found little difference between the cyclists and e-scooter drivers. On average, the test persons performed more than half of all possible shoulder glances: 57.93% for the e-scooters and 60.71% for the bicycles. In addition, the difference in the average shoulder glance duration is negligible. We also observed that car drivers seem to be more likely to stop at crosswalks for cyclists than e-scooter riders.

Shared e-scooters, whose supply and coverage keeps increasing in many cities around the globe, are rapidly changing mobility in urban road environments. As rising injury rates have been observed alongside this new form of mobility, researchers are investigating potential factors that relate to safe/unsafe e-scooter use. In Germany, e-scooter sharing platforms were only recently permitted in the middle of 2019, and their number has increased steadily since then. The aim of this study was to assess key factors that relate to their safe use, through a direct observation of e-scooters conducted at three observation sites around Berlin. Helmet use, dual use, type of infrastructure use, and travel direction correctness were registered for 777 shared e-scooters during 12.5 h of observation. Results reveal a high level of rule infractions, with more than one quarter of observed shared e-scooter riders using incorrect infrastructure, and one in ten e-scooter users riding against the direction of traffic. Dual use (i.e., two riders per e-scooter), was observed for 5.1% of shared e-scooters. Moreover, none of the riders observed in this study used a helmet on their shared e-scooter. These results point to a need for better communication and enforcement of existing traffic rules regarding infrastructure use and dual use. Further, they indicate a lack of efficacy of safety-related advice of shared e-scooter providers, who promote helmet use in their smartphone application and directly on their e-scooters.

More and more assisting and entertaining systems find their way into the cockpit [1]. But the proposed benefits of increased safety, efficiency, and comfort can only come into effect if drivers decide to use the systems. Therefore, it is essential to understand what determines drivers’ acceptance of technology in the vehicle. A lot of research addresses technology acceptance applying quantitative methods [2–4]. This work gives an outline on the Technology Acceptance Model (TAM) [5] and driving-related adaptations as well as the potential of qualitative research in this field. Further, we conducted a qualitative online study (N = 600) on factors influencing technology usage. We examined the reasons why drivers do not use a system although their car is equipped with it. The qualitative statements were analyzed according to Mayring [6]. The category scheme was developed inductively and compared with the TAM 3 [7]. The analyses show that 56.87% of the reported statements address usefulness and 12.57% ease of use. Seven additional categories emerged accounting for 27.85% of the statements. The results reveal what is subjectively important for drivers and enhance our understanding of barriers for technology usage in the car. The work outlines the potential of qualitative insights adding to the existing body of research.

The COVID-19 pandemic has strongly impacted people’s main routine, which certainly includes their mobility habits. This paper aims to assess the pandemic’s mobility impacts and whether these may have increased the already existing inequality between men and women. In particular, the variation of mode choice in a pre-COVID and post-COVID scenario is investigated, focusing on the use of transport mode defined as Smart Mobility. The analysis is performed on data collected in thirteen European countries between July and September 2020 through a survey designed using an intersectional approach. Responses are analyzed to highlight correlations between different factors affecting mobility changes: some interest is reserved to the modes used according to the journey scope (work, errand, shopping). Overall, results reveal more people walking for their daily journeys, while a significant decrease in the use of public transport is observed. Although these changes affect women more, the main reason behind this is the need for more safety in terms of low risk of contagion, irrespective of gender. A specific focus on using modes commonly associated with a Smart Mobility offer (such as shared modes, public transport, walking, and biking) reveals differences originating when comparing men and women responses and various age ranges.

The value of rollers for the elderly and other groups who need walking assistance has been underestimated both in terms of practice and in knowledge production. This paper aims at scrutinising the roller as an age-based and gendered innovation. Using the theoretical notion of scripts, it demonstrates how rollers and their users are intertwined in everyday practices and how these relationships intersect with notions of age and social welfare provision. Based on contrasting images of rollers and their users, as well as semi-structured interviews, this paper examines the puzzle of how the take-up of new (technological) devices comes about. It argues that the roller can be seen as a simple, disruptive innovation emerging from the bottom up with contradictory scripts of gender and age. The paper concludes with a perspectivation of how the rollers could be made both smarter and digital!

In order to reduce the burden on caregivers, we developed an intelligent electric wheelchair. We held workshops with caregivers, asked them regarding the problems in caregiving, and developed problem-solving methods. In the workshop, caregivers’ physical fitness and psychology of the older adults were found to be problems and a solution was proposed. We implemented a cooperative operation function for multiple electric wheelchairs based on the workshop and demonstrated it at a nursing home. By listening to older adults, we obtained feedback on the automatic driving electric wheelchair. From the results of this study, we discovered the issues and solutions to be applied to the intelligent electric wheelchair.

To exploit the benefits of automated vehicles (AVs), the systems’ functions need to be accepted by the driver and other traffic participants. Thus, the human-machine interaction should be considered as a key issue. Manual driving is often coordinated by implicit communication cues. Therefore, specific parameters such as drivers’ gap acceptance (GA) should be investigated to be prospectively implemented in AVs. The current study aimed at identifying the effects of different interaction partners, encountering speeds and participants’ age and gender on GA. The video material displayed a left-turn scenario from the drivers’ perspective including encountering interaction partners from the left. The study investigated four different interaction partners (passenger car, motorcycle, e-scooter, bicycle), all approaching at four different speeds (10/15/20/25 km/h). In sum, 121 participants contributed to the online study. The results revealed main effects for interaction partner, encountering speeds and participants’ age on GA. Participants selected the smallest comfortable time gaps for the bicycle and the largest gaps for the passenger car, indicating that participants anticipated the potential threat of the interaction partner when selecting comfortable gaps. In accordance with previous studies, smaller gaps were accepted at higher speeds resulting in riskier decisions. Younger participants accepted smaller gaps than older participants did. Hence, AVs should consider the types and the speeds of the encountering interaction partners when selecting a comfortable gap as a form of implicit communication. Moreover, drivers’ characteristics should also be considered when implemented driving styles in AVs, e.g., by selectable driving style profiles.

Being both affordable and sustainable, bike-sharing schemes have a promising potential of providing smart and sustainable mobility solutions for all. However, for bike-sharing to become part of a convenient, sustainable, and accessible mobility system, it must meet the needs of a wide range of users. Today, existing supply of bike-sharing schemes rarely take diversity into account: people who travel with kids, people who do not feel secure in biking or people who carry heavy luggage, do not have the opportunity to use the system. The lack of diversity in the contemporary bike-sharing supply presents a problem for visions of smart mobility for all. While a body of research points to differences in bicycling due to socio-economic factors and norms, there is little knowledge on how diverse mobility needs affect the attractiveness of using a bike-sharing scheme. This paper addresses non-users’ perceptions of public bike-sharing schemes in Denmark and Sweden. The empirical material includes 14 in-depth interviews and two focus groups with non-users. Research questions include what everyday mobility needs the informants have, and if they can be meet by the local bike-sharing scheme, as well as how the bike-sharing scheme meets the diversity in restrictions, needs, and preferences of transport. The paper finds that non-use of bike-sharing schemes in Scandinavia can be explained through three overall narratives: ‘I have my own bicycle’, ‘I travel with kids’, and ‘I don’t feel safe.’ It argues that obstacles of using bike-sharing schemes in part can be explained the ‘one fits all’ approach that dominates bike-sharing design today. By adding a perspective on diversity, the paper contributes to filling the research gap in new mobility solutions and diversity.

This research considers the efficacy of auditory alert systems in semi-autonomous vehicles (SAVs) from the perspective of the neurological processing of multi-modal information. While SAVs are growing in popularity, there is much to be discovered concerning driver safety. Understanding how the brain integrates multi-modal information is essential to determining the efficacy of auditory alerting systems in SAVs and whether or not they suffice as a method for conveying information to drivers. Investigating how younger and older groups process various types of auditory information while engaged in visuospatial tasks of different workload levels is a crucial step to take to optimize safety in SAVs. We report on how auditory processing of deviant and standard tones was impacted by age at decision-making areas of the brain using electroencephalography (EEG). EEG and behavioural data from 10 participants, five older (57–78) and five younger (18–26) were analyzed. Participants completed four rounds of a match-to-sample visuospatial task while paired tones (using an oddball protocol) were delivered through headphones. Regardless of age, deviant tones resulted in greater P200 components, which highlights the importance of auditory alert systems implementing novel alert sounds for emergencies, such as handover tasks. Results also showed that neural responses to salient auditory tones in the second position were attenuated in older adults in difficult conditions of a visuospatial task. Thus, the effects of age and visuospatial workload level on auditory processing are critical to consider, given that features related to SAVs, such as alerting systems, are still being developed.

This article deals with the results of work carried out on the development of audio-based guidance system interfaces for the visually impaired at the THIM laboratory at the University of Paris 8. Research projects with the Paris Transport Authority (RATP) and many other partners have shown the importance of the structure and vocabulary of the instructions generated by the guidance system in guaranteeing the clarity, comprehension and effectiveness of the messages, particularly in conditions of high ambient noise levels. The choice of a vocabulary with optimum intelligibility and the use of structured messages that are as brief as possible ensures easier comprehension of the instructions. This allows the user more time to detect and integrate the audible and other clues to their situation from the local physical environment. This is a critical factor in increasing the safety of the user with visual deficiencies in the complex and unique environment of the Metro. A standard methodology and structure have been developed to integrate these principles in systems tested with success in the Paris Metro system.