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Watch Out!

User-Centered Feedback Design for a V2X-Smartphone App

Part of the Lecture Notes in Computer Science book series (LNISA,volume 10287)


Mobility is a fast developing, technological and simultaneously human field of research. V2X-technology is one major contributor that will influence the behavior, efficiency and safety of traffic participants. To include all participating members of traffic, we developed a V2X-smartphone application to empower vulnerable road user to be part of the technological integration. With a two-tiered research approach, we focused on both, the iconography and the feedback design of that application. One key finding of the presented work is a clear recommendation of combined features (color, size and geometrical form) for rear-end collision scenarios. The article concludes with practical recommendations that facilitate visualization-varieties from a users’ perspective.


  • V2X-technology
  • Smartphone application
  • Feedback design
  • Usability
  • Mobility

1 Research Perspective and State of the Art

Mobility is a key issue in today’s society. People use and depend on different types of transport vehicles and want clean, safe and efficient ways to reach their destination comfortably. With a steadily growing number of road users, the number of drawbacks like congestion and high CO2 emissions, which need to be taken care of, is getting more serious. One of the biggest challenges, however, is still the high number of (fatal) accidents in road traffic [1].

Although, the overall number of accidents in road traffic decreased in the last ten years, 390.000 fatalities are still tremendous and not bearable (2014 in Germany: [2]). From January to September 2016, the number of traffic accidents increased by 6.4% (2428 fatalities in total in Germany) compared to the same period in 2015 [3]. Therefore, road safety remains a key issue for society and politics.

In order to increase safety, the federal government published he program Strategy for automated and connected driving and announced the goal to diminish the traffic incidents by 40% until 2020 [2, 4]. In addition to a growing enforcement of road traffic regulations and a better training of the road users, the program also provides for the promotion of intelligent technologies. The continuous implementation and development of connected technology based driver assistance systems can be detected, while the most important and frequently used way to travel is still the personal car. Aiming at improved traffic safety, new driving support systems were implemented and reportedly decreased the number of crashes since integrating them into vehicles [5, 6]. Taking a closer look on this proceeding, several system features and boundaries, which are not sufficiently researched out of a social science perspective, can be detected.

The multi-level effects of connected and (semi-) automated vehicles on the driver is a focused topic today, but integrating also the vulnerable road users (e.g. wheelchair users, pedestrians or bicyclists) into intelligent transportation systems is a future challenge.

To address this challenge, an exchange of information between different road users and technical elements in the infrastructure will be necessary. The communication of vehicles among themselves and with elements of the infrastructure is summarized as V2X (“Vehicle-to-Everything”). Modern vehicles are already equipped with numerous technologies such as radar, ultra-sonic and sensor cameras designed to reduce the risk of accidents. Via this way of connected communication, the reduction of rear-end collisions and a more energy-efficient driving mode is intended, e.g. by an optimization of the traffic flow [7]. Although, recent studies focus cooperative interactions with road users [8] or the usability of reminder applications [9], there seems to be a gap in how to integrate vulnerable road users into the intelligent networking of V2X-technology. Following the approach of including the e.g. pedestrians into V2X-communication, GPS and WLAN based prototypes have been tested in recent years [10, 11].

One approach to connect vulnerable road user to V2X-communication is the integration via smartphone applications. The proliferation of mobile devices has grown strongly in recent years: in 2016, 66% of the German population owned a smart device (95% of the 14- to 29-year-old age group in Germany), promising an effective way of connection [12]. Today’s devices are also equipped with numerous sensors, which allow to track the position and speed of a user, which promises a technical way of integration as well [13].

Assuming that pedestrians, cyclists and wheelchair users need special protection in road traffic, their inclusion into vehicle communication could further increase road safety. Taking into account that the detection of vulnerable traffic participants without the solution of a smartphone application is only possible to a limited extent, because the sensor technology of vehicles, such as lasers, radars or video-based solutions, must be in the field of view of the sensor system, a smartphone solution seems effective and efficient. If, for example, a pedestrian is concealed by a parking car, it is difficult to detect by the sensor system of an approaching vehicle [14]. With a V2X-smartphone application however, this challenge could be directly addressed.

2 V2X-Application: Watch Out!

System Architecture.

The V2X-smartphone application presents the current traffic situation to the user and alerts him in case of a critical situation with a warning instruction. Aiming the interconnection of different (smart) traffic technologies, an overview of the own position and all traffic participants of the current situation will be displayed.

The entire system consists of a control server, which is connected to all components (see Fig. 1). These components include traffic lights, smartphones, Cohda boxes and some laser and HD cameras. The Fig. 1 shows only the components that are relevant to this work.

Fig. 1.
figure 1

Overview of important system architecture of the V2X-app (CERM)

The control server is connected directly to the traffic center. This traffic center observes the traffic so that an administrator can step in the event at any time. Furthermore, the control server connects with the Cohda box in the car via a WLAN router and also with the V2X-app on the smartphone. On the one hand, the V2X-app sends all position and motion data to the control server and on the other hand, the app receives the position of all relevant traffic users. This means that when a car passes by on the smartphone and it is no longer a threat, the control system automatically stops to send information to the smartphone. The same happens with the car, concretely meaning, the Cohda box sends all information to the control server and receives all the data from the relevant users.

The data, shared by the V2X-app to the server, are the GPS positions, GPS accuracy, linear acceleration and traffic type. The traffic type defines whether the traveler is by foot, with an E-wheelchair or with a bicycle. In case the type of traffic is set to E-wheelchair, there is the possibility that the control system connects to the control of the E-wheelchair via the smartphone (Bluetooth). In case the E-wheelchair user does not react in time due to his disability, it is possible to brake the wheelchair through the control system until the danger is over. Furthermore, the entire data transfer of the components will be stored and can be used to analyze, to anticipate and finally to avoid critical situations using the neural networks.

Although the previous investigations highlight the technical feasibility, the visualization of the application on the smartphone has so far largely been neglected – out of a social science perspective. However, designing a user interface is crucial to whether or not users are using an application [15].

3 Questions Addressed and Methodological Approach

To develop the application’s user-interface and feedback-system a multi-stage development process was used, integrating the users’ requirements and preferences during every iteration loop. At first, the theoretical foundations have been acquired by identifying both the state-of-the-art and existing knowledge gaps. As can be seen in Fig. 2, the literature research resulted in two parallel development strands. One of them dealt with the graphic representation of traffic situations, including the map display, the illustration of road users and the visualization of hazardous situations that could lead to collisions between motorized traffic participants and the smartphone user.

Fig. 2.
figure 2

Overview of UI and warning development

The other strand broached the issue of developing the ideal, multi-modal feedback and warning to grab the vulnerable road user’s attention in order to trigger a reaction (usually to stop movement) that could avoid a potential collision. Thereby, both efficiency and user preferences were taken into account. Both development strands started with interview- and questionnaire based studies and used low-level prototypes, i.e. animations and wizard-of-oz based systems, in laboratory settings.

After that, both the resulting user interface and the warning system will be implemented into the Android-based application to carry out user studies in a controlled environment on test track in order to iteratively optimize the system before testing under real traffic conditions.

3.1 User Interface Development

Aiming and focusing the development and feedback of the applications’ user interface, we conducted a two-tiered approach using both qualitative and quantitative empirical methods.

First, recognizable and lean road-user icons for the applications’ interface were designed and evaluated via an interview-based preliminary user study. Second, the revised user interface was evaluated with a large-scale questionnaire study combining paper-based and animated prototype elements.

4 Qualitative Design Circle

The first questionnaire-supported interview study was set to gain insights of users’ application preferences of the traffic visualization and to evaluate early prototypes for the visualized traffic participants. To display the individual traffic on the map, icons are used: they serve as a visual representation of various aspects of a user interface [16]. According to the definition of Pierce, an icon is a representation of an object, which is represented only by the distinctive features it exhibits [17]. Here, different aspects need to be considered: the used icons must first be easily and quickly identifiable. Further, different locomotion-roles of vulnerable traffic participants were focused to integrate possible user-centered feedback. Here, perspectives from pedestrians, bicyclists and wheelchair users were interrogated.

4.1 Study Design

The study had a dual focus: First, different forms of traffic visualization were studied. Thereby, the participants had to perform identification and rating tasks. The identification tasks took place as follows: Various visualizations of traffic situations adapted to common smartphone screen sizes were presented to the participants, which had to identify the illustrated road users to determine the average detection rate. The icon set consisted of top view depictions of pedestrians, wheelchairs, bicycles, motorcycles, cars, busses, trucks, trams and the user’s own position. In addition, rating tasks were conducted that dealt with the choice of colors, the information density and the potential help during assessing traffic situations. Second, the map design was evaluated distinguished by different traffic situations. Here, the alternative traffic visualization of a heat map view had to be evaluated.

4.2 Sample

The twelve (N = 12) interviewees had an age range from 24 to 62 years old (Mean = 35.5, Standard Deviation = 13.9). The gender distribution was quiet symmetrical with seven men and five women, further, two of the participants were wheelchair users.

4.3 Results: Icon Design

The participants were invited to identify several items on displayed pictures (in smartphone display size) with icons of traffic participants. According to the International Organization for Standardization (ISO), icons with a recognition rate of 67% are still considered acceptable [18]. This mark was reached for the icons displaying Bicycle, Car, Pedestrian, Position and Bus (as can be seen in Tables 1 and 2).

Table 1. Absolute frequency of correctly identified traffic icons (N = 12).
Table 2. Absolute frequency of correctly identified traffic icons (N = 12).

The icons Wheelchair, Motorcycle, Truck and Tram were not identified correctly according to the ISO mark. The icon Wheelchair was e.g. taken for a pedestrian, whereas the icon Motorcycle was misinterpreted for e.g. a bicycle and the Tram icon as long truck.

Due to the low identification rate of the mentioned traffic icons above, a quantitative re-evaluation was conducted, also validating the beforehand positive results of the correctly identified traffic icons.

4.4 Results: Map Design

To evaluate, whether a street map view with the given icons is the right design, the participants were invited to assess a different design: the heat map (see Fig. 3).

Fig. 3.
figure 3

V2X-application heat map-view (“Berlin Stadtmitte U2”, Data source: © 2017 Geobasis-DE/BKG (©2009), Google)

Here, the lack of important information was criticized, e.g. the specific color meaning (“vehicles on the street” vs. “critical situation”). One participant concluded, that this type of view might be considerable for a driver of a vehicle, but not for vulnerable road users like pedestrians. Due to the lack of positive feedback and many misunderstandings in the meaning of the visualization, the heat map view was no part in the further study design.

5 Quantitative Design Circle

Taking the results from the qualitative study into account, the user interface was further improved and evaluated by conducting an online survey. A special focus was placed on the presentation format for the current traffic situation and the design of the warning instructions.

5.1 Study Design

The first part of the study dealt with the iconography. Participants had to both identify visualized road users and evaluate the different icons for a road user type in direct comparison. Therefore, three revised icons for each type that showed insufficient detection rates during the preliminary study were presented to identify the optimal representation with regard to identifiability on a smartphone screen. There was also a focus on the distinguishability of road user pairs that had often been confused: pedestrians vs. wheelchairs and bicycles vs. motorcycles.

The second part of the study broached the issue of preferred illustration for safety critical situations. Five different animated sequences were laid out to the participants, all in common mobile device size.

The first sequence shows a colored (red) geometrical figure (circle) with a growing opacity level within the collision warning phase (see a section of it in Fig. 4):

Fig. 4.
figure 4

Sequence A geometrical figure before (left) and during collision time (right). Data source: © 2017 Geobasis-DE/BKG (©2009), Google (Color figure online)

The second sequence shows also a colored geometrical figure (red circle) with a growing opacity level within the collision phase, but the size of the circle form increases as well (see a section of it Fig. 5):

Fig. 5.
figure 5

Sequence B geometrical figure and size before (left) and during collision time (right). Data source: © 2017 Geobasis-DE/BKG (©2009), Google (Color figure online)

The third sequence displays an increasing of the icons’ size within the collision phase, without any coloring (see a section of it in Fig. 6):

Fig. 6.
figure 6

Sequence C icon size before (left) and during collision time (right). Data source: © 2017 Geobasis-DE/BKG (©2009), Google (Color figure online)

The fourth sequence displays a three-tiered coloring approach (green – yellow – red), in a safe phase, short before a critical situation and during collision time (see sections of it in Fig. 7).

Fig. 7.
figure 7

Sequence D icon coloring in safe phase (green: on the left) and short before (yellow: middle) and in collision time (red: right). Data source: © 2017 Geobasis-DE/BKG (©2009), Google (Color figure online)

The fifth sequence shows a combination of coloring the icon and the size of the icon within the collision phase (see a section of it in Fig. 8).

Fig. 8.
figure 8

Sequence E icon coloring and size before (green: on the left) and short before (yellow: middle) and in collision time (red: right). Data source: © 2017 Geobasis-DE/BKG (©2009), Google (Color figure online)

Similar to the preliminary study, all sequences were evaluated regarding preferences, information completeness, deflection, and situation assessment. Last, demographic data, mobility behavior and technical self-efficacy [19] as user characteristics were surveyed.

6-point Likert-scales were used for all rating tasks (min = 0 “no agreement at all”, max = 5 “full agreement”). The level of significance was set to α = .05. Parametric statistical methods were used to analyze the data and crosschecked by their non-parametric counterparts if there were slight violations of requirements. However, for clarity and legibility reasons only the results from parametric procedures will be reported.

5.2 Sample

186 (N) participants replied to the questionnaire. Altogether, 53.2% were male and 46.8% female. The mean age was 37.7 years (SD = 15.6), ranging from 16 to 86 years. The educational level of the sample was rather high: 66.1% of participants had a university degree, 19.9% graduated from high school and 8.6% completed vocational trainings. Furthermore, the sample showed a high average technical self-efficacy with M = 3.54 (scale maximum = 5, SD = 1.08).

The vast majority of participants owned a smartphone (92.5%, n = 171). Map and navigation applications were used frequently by 28.1% and occasional by 46.8% of those. Of all participants, 93.5% owned a driving license. However, 86.2% mentioned that they often cover journeys on foot and 9.9% still from time to time. To better differentiate between preferred modes of transport, groups for pedestrians, bicyclists and wheelchair user were classified depending on usage frequencies:

All participants stating a rare use of bicycles (never to max. one time a month), but a high frequency of walking were defined as pedestrians (44.5%, N = 82). Further, participants stating a rather regular use of bicycles (several times a month to daily) were defined as bicyclists (50.0%, N = 93). The third group was identified due to their use of a wheelchair (4.8%, N = 9).

5.3 Results: Icon Design

Taking the icons Wheelchair, Motorcycle, Truck and Tram into account (not identified correctly according to the ISO mark in the preliminary study), two to three revised icons were presented and in comparison to another evaluated (see Tables 3 and 4).

Table 3. Frequency of chosen traffic icon for wheelchair user and motorcycle (N = 186).
Table 4. Frequency of chosen traffic icon for truck and tram (N = 186).

The most selected traffic icon for Wheelchair is Icon 3 with a total of 68.3% (N = 186, see Table 3). The icon shows detached individual parts of a wheelchair user from a top view. Still, some participants expressed their wish to visualize the Wheelchair like the “common 2D symbol from side view”.

An expected confusion with other traffic participants (e.g. pedestrians) could be avoided in all three visual representations: In several identification tasks, in which the participants had to decide whether the presented icon is a pedestrian or a wheelchair user, the traffic icon was successfully identified from at least a total of 87.6% (N = 186).

Further, the most selected traffic icon for Motorcycle is Icon 2 with 50.0%, (N = 186), followed by Icon 3 with 41.9% (see Table 3). Here, a closer look to the identification task, in which the participants had to decide whether the presented icon is a motorcycle or a bicycle shows, that both icons were identified successfully (Icon 2: 80.1% and Icon 3: 82.3%). Here, Icon 1 was not identified correctly: 46.8% believed it to be a bicycle and 44.1% identified it as a motorcycle. The icons vary from one another with different helmet sizes of the motorcycle driver and thickness of wheels.

Regarding the traffic icon for Truck, 75.8% selected Icon 2 as preferred representation (see Table 4). The Icon 2 shows a vehicle from top view with a detached driver cabin. With 57.0%, the representation most selected for Tram was Icon 3, followed by Icon 2 with 40.3% (see Table 4). The preferred icon shows a clear distinction between two railway wagons and a pantograph on one of the wagons. Also the connection to further wagons is portrait, again from top view.

5.4 Results: Feedback Design

The presentation of the results of the feedback design will be structured as follows: First, the comparisons between the possible sequences will be presented. Second, user group specific differences will be analyzed.

Overall Comparison of Sequences.

As can be seen from both Figs. 9 and 10 several differences in the evaluation of the different possible visualizations were found.

Fig. 9.
figure 9

Arithmetic means of statement agreement differentiated by sequences A to E (5 = max. agreement).

Fig. 10.
figure 10

Arithmetic means of statement agreement differentiated by sequences A to E (5 = max. agreement).

First, the participants were invited to evaluate, whether the information provided was sufficient. Overall, the sequences evaluation differentiated significantly from each other F(4,732) = 66.76, p < .001). A pairwise comparison shows that only sequence A and E did not differentiate significantly (p ≥ .05).

A closer look shows that sequence B has highest approval rate (M = 3.56, SD = 0.97), followed by sequence A. The evaluation of sequence C reveals a slight disapproval to the given information (M = 2.17, SD = 1.36).

Next, the participants were questioned whether the sequence contained too many distracting elements, which was rejected (see Fig. 9 (middle): all M < 2.5, whereas 5 = max. agreement). Overall, the sequences evaluation differentiate significantly from each other (F(4,736) = 20.95, p < .001). A pairwise comparison shows that only sequence A and B did not differentiate significantly (p ≥ .05). The lowest approval rate scored sequence A, the colored geometrical figure with a growing opacity level (M = 1.55, SD = 1.01).

Third, the assessment of the situation was evaluated. Here, four out of five sequences were agreed upon an immediate assessment of the situation (see Fig. 9). Again, almost all sequences are evaluated with a significant difference (F(4,720) = 56.98, p < .001). Due to a pairwise comparison sequences D and E do not differentiate significantly (p = .146). Sequence B (color and size changing geometrical figure) has the highest approval rate (M = 3.64, SD = 0.99), whereas sequence C scores the lowest agreement rate (M = 2.26, SD = 1.30).

After that, the participants were asked to decide whether they needed further help to interpret the situation displayed correctly (see Fig. 10), which was rejected (see Fig. 10 (left): all M < 2.5, with 5 = max. agreement) although the sequences show significant differences in their rating (F(4,720) = 30.93, p < .001). The pairwise comparison shows that the evaluation of sequence C differentiates from all other sequences significantly (p ≤ .001). The increasing icon size (sequence C) shows the highest agreement on further help (M = 2.36, SD = 1.46), whereas sequence B shows the lowest approval (M = 1.41, SD = 1.15).

An increasing road safety by using the application was evaluated next. Here, the overall approval was rather low (see Fig. 10). Only the sequences A (M = 2.52, SD = 1.51) and B (M = 2.73, SD = 1.56) could score an average approval over 2.5. Again, all sequences show significant differences in their rating (F(4,732) = 43.64, p < .001). A pairwise comparison shows that only sequence D and E did not differentiate significantly (p ≥ .05). Sequence C scored the lowest approval.

Finally, the participants were asked, if the sequence shown was perceived well. To sum up, the sequences were evaluated significantly different from one another (F(4, 724) = 57.18, p < .001), except sequences D (three-tiered icon coloring) and E (three-tiered icon coloring and size increasing) as a pairwise comparison showed (p ≥ .05). Sequence C scored lowest approval (M = 1.69, SD = 1.30), whereas sequence B had the highest approval rate (M = 3.20, SD = 1.29).

Summarizing the iterative evaluation, sequence B showed the highest approval rates considering all statement agreements and given qualitative feedback. A closer look at the sequence reveals a warning icon sign combined with a text at the top of the screen (see a section of it in Fig. 11), faded in, when the situation is becoming increasingly serious. All sequences share this information.

Fig. 11.
figure 11

Section of “best choice” sequence B (colored geometrical figure with growing size and opacity level before collision phase) portrait with all given information. (Color figure online)

Further, a red colored geometrical figure overlaid on the traffic icon of the upcoming vehicle is shown. The geometrical figure, a circle, increases in size and opacity level by coming closer to the own position, here, displayed as purple arrow. The combination of figure size, opacity level and warning reference is the recommended visualization for a collision warning in our smartphone application.

User Diverse Comparison of Sequences.

To gain a first insight of possible user diverse evaluation patterns, further statistical analyses were performed, addressing different mobility groups, gender and age.

First, a comparison of the sequence evaluation by the mobility groups (bicyclist, pedestrian and wheelchair user) was conducted. No significant differences of the mean agreement ratings could be identified, revealing a joint result of the sequence evaluation.

Further, gender was focused as possible influencing user factor. Again, no significant difference in the evaluation could be found.

Nevertheless, concerning age several indications could be identified. Whereas no significant difference could be found regarding sequence A, sequence B showed a negative, significant correlation (r = −.166, p = .023, N = 186). Younger participants agreed significantly stronger to the statement, that the sequence provided sufficient information. Another finding indicates, that older participants agreed significantly stronger to the statement regarding help to interpret the situation correctly (r = .192, p = .009, N = 186).

Sequence C as well as sequence E showed no significant differences according to age. The only further finding regards sequence D: here, older participants agreed significantly stronger to the statement that the road safety will increase, if they use the application (r = .184, p = .012, N = 185).

6 User-Centered Feedback Design

Aiming a first step towards enhancing safety in road traffic, we addressed vulnerable road users like pedestrians, wheelchair user or bicyclists in order to integrate them via V2X-smartphone application into V2X-communication systems. We worked with a well educated, highly technical affine, but diverse sample in terms of mobility behavior. The multi-stages development process was laid out, followed by presenting results in both qualitative and quantitative studies, which addressed the icon design of all participating road users as well as the user centered feedback design in a rear-end collision traffic scenario.

Due to the results of both studies, a suitable feedback design interface recommendation for the V2X-smartphone application could be developed. Here, an adequate representation of the current traffic situation by using distinctive icons could be identified. In addition, new upcoming challenges with the user interface were detected. Thus, a further development of this application is essential, bearing an enormous potential for future research.

Addressing the first research approach (icon design), recognizable and lean road-user icons for the interface were evaluated (in both the preliminary interview-based study and the online survey). At first, most of the presented icons (like car or bicycle) could be easily identified – meeting the definition of Pierce [17] and the ISO mark. However, four icons had been misinterpreted (e.g. wheelchair user for pedestrian) and showed challenges, probably due to the smartphone display size. After re-designing the icons and re-evaluation, new recommendations with promising identification marks could be developed. Most of the re-designed icons have now detached parts, making it easier to be spotted. Another reason for the better identification success may be attributed to the research procedure: the first study was a qualitative procedure with paper-prototypes, whereas the following study was an online survey, making it possible for the participants to see the icons on-screen in much higher quality. Nevertheless, it was possible to clarify the misinterpretations with the new icons. Here, a clear recommendation of traffic user icons can be given. Understanding the participants wish to rely on already established icons, like the 2D side view wheelchair icon, a definite recommendation for a top view icon can also be given.

Further addressing the second research approach (map design), a preferred layout can be recommended. After testing a different map layout in the qualitative study, namely the heat map design, we decided to work with a classic top view of a street map design. According to Bojko [20], “[h]eat-maps help us quickly see ‘the big picture’ including any patterns or trends that may exist in the data”, but the layout made it difficult for the participants to identify individual traffic participants or understand the colors intuitively.

Aiming a recommendation for a user centered display visualization for safety critical situations in traffic, we therefore evaluated five different animated sequences in a street map view. Here, the participants evaluated the information completeness, deflection and the situation assessment. All sequences varied in size of the used traffic icon, use of geometrical figures, coloring or combinations of all features.

Regarding the sufficiency of information, all sequences were evaluated positively. However, the given information handled a rather low information density, which need to be taken into account. Therefore, it is only logical, that the question whether one of the sequences contained too many distracting elements was fully rejected. Here, a closer look to different traffic scenarios, such as traffic jam, rush hour or emergency drive-thru may reveal, if the given details are still transparent enough to assess the situation immediately.

Only two sequences, namely A and B (both using a geometrical, colored figure to alert the application user about a possible collision), were evaluated positively regarding the question, whether the road safety will increase by using the application with the introduced feedback design. This leads to the conclusion, that a combination of features for feedback design of a V2X-smartphone app is the right direction. Both sequences seem to convey a perception of enhanced safety for its user, providing a manageable overview of information and enabling an independent use of that information.

Stepping ahead, sequence B showed additionally the highest (arithmetic mean) approval rates for an overall positive impression, which corroborates the recommendation of the combined features of that particular sequence for feedback design. In comparison, sequence C (change of icon size) was evaluated “worst choice”. As only sequence without influence of color in comparison to the other sequences, a key factor for feedback design could be identified. Coloring obviously helped the participants to successfully assess a situation without further help, validating the statement of Baldassi and Burr [21], which claim that changes in size as the only distinguishing feature are less pronounced.

A final look on user diverse evaluation patterns showed only a few interesting results. Whereas the preferred form of mobility and gender had no significant effect at all on the evaluation, the consequence arises that the forms of mobility were probably to similar to one another. Only distinguishing by speed, all of the analyzed forms were vulnerable road user – and, even more important – the evaluation was an on-screen testing. A hands-on evaluation in a test environment (e.g. on a stationary bicycle) could reveal differences in a next evaluation loop of the application.

The only influencing user factor so far was age, only affecting single statements in different sequences. Here, younger participants agreed significantly stronger to the statement, that the information level (in sequence B) was sufficient – but, overall – the information level was also agreed on being sufficient. This result could hint to personal adjustments in terms of information density on-screen.

7 Outlook and Limitations

The findings revealed interesting insights into iconography and feedback design as well as small user diverse effects of a first V2X-smartphone application prototype. Although, the results identified further needed development and evaluation loops on both topics, first clear and lean recommendations could be made. The general perception of a safety increase via V2X-app could be determined. Due to the small participating wheelchair users in the the study, the sample is not representative for all vulnerable road users. A closer analysis of the icon and feedback design with cooperative feedback from actual wheelchair users will be necessary. Low-level prototyping and animations are just the first step in our methodological approach and will be taken further with hands-on outdoor tests in real environments. User studies in controlled environments like a test track and further, testing under real conditions, are future steps in the development of the application and our goal of integrating vulnerable road user into the traffic infrastructure: Watch out!


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We would like to thank the research group on mobility at RWTH Aachen University, which works in the Center for European Research on Mobility (CERM) supported by the Excellence Initiative of German State and Federal Government. Many thanks go also to Florian Groh, Pierre Schoonbrood and Christian Klein for their valuable research input.

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Schmidt, T., Philipsen, R., Dzafic, D., Ziefle, M. (2017). Watch Out!. In: Duffy, V. (eds) Digital Human Modeling. Applications in Health, Safety, Ergonomics, and Risk Management: Health and Safety. DHM 2017. Lecture Notes in Computer Science(), vol 10287. Springer, Cham.

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