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5.2 Preparing Take Over Requests in a Stereoscopic 3D Representation

5.2.2 Results

In the course of this study, the performance during the take over maneuver, the workload of the participants and their user experience was assessed. In the following we present the results of the study. To assess the significance of the results a paired t-test, Friedman test [200] and Wilcoxon Signed-Rank Test [236] is used.

Take Over Performance

We compared the driven trajectory of the baseline and the S3D visualization. To assess the driven trajectory, the lateral lane position is recorded. As the driving task requires the driver to change the lane the objection was to drive on the lane without the obstruction on the road. In the S3D condition the lateral lane positions were generally closer to the lane without an object (Mean = -108.31, SD = 1.15) and results obtained by a t-Test revealed a significant difference between the baseline condition (Mean = -109.51, Sd = 1.08) and the S3D condition (p = 0.04). Additionally the steering wheel angle of the driving maneuver was recorded. This gives us information about the steering behavior of the driver, like uncontrolled or smoother lane changes. Though the S3D condition has overall smaller values with a mean of 10.24 and a SD of 4.82 and the baseline condition has higher values with a mean of 15.86and a SD of 8.45, the p-value indicates no significant difference between the two conditions. As a third evaluation measure the speed was taken into account. The speed in the S3D

Preparing Take Over Requests in a Stereoscopic 3D Representation

condition was lower (Mean = 3.82 m/s, SD = 5.93 m/s) than the baseline condition (Mean = 8.63 m/s, SD = 10.72 m/s), which indicates that the user adapted the speed earlier and more appropriately with the S3D visualization. The p-value of a t-Test is 0.04, indicating significant differences in the speed values in both conditions.

Workload and User Experience

The workload of the system was assessed using the NASA TLX (see Figure 5.7) on a 20-point Lik-ert scale. The temporal demand of the S3D condition is highest (Mean = 7.65, SD = 5.27), which indicates that the participants needed to spend time on the digital twin to understand the digital rep-resentation. The biggest difference between both tests is the effort value (Baseline condition: Mean

= 11, SD = 1.02; S3D condition: Mean = 5.6, SD = 4.86). That could be an indication that the digital twin mentally prepares the driver better on the TOR and thus lessens the effort of the TOR. We tested the data on significance with a Friedman test [200], since the data is not normally distributed (tested with the Kolmogorov-Smirnov test [63]). The measurement scales of mental demand ( ˜χ2(1) = 7.2, p = 0.007), performance ( ˜χ2(1) = 4, p = 0.046), effort ( ˜χ2(1) = 14.22, p = 0.0002) and the over-all measure ( ˜χ2(1) = 11.84, p = 0.0006) show a significant difference between the two tests. The Wilcoxon Signed-Rank test confirms the statistical significance (mental demand (p-value = 0.007), performance (p = 0.037), effort (p = 0.0004), overall measure (p = 0.02314)). The S3D condition has a lower score in most measures and performs better than the baseline condition.

The acceptance scale is used to analyze the acceptance of the digital twin. The acceptance is measured by evaluating the usefulness and satisfaction scale. Participants have a positive attitude towards the digital twin with a mean of 1.06 (SD = 0.76) in the usefulness and a mean of 1.16 (SD = 0.80) in the satisfaction scale. One remark of a participant indicated that the understanding was better, when a TOR was issued and in assessing the proper point of time to take over the vehicle with the S3D visualization.

5.2.3 Discussion

The results show that a visualization of the digital representation of the highway can improve the driving performance. The driven trajectory was significantly improved by the S3D visualization compared to the baseline. Furthermore, the speed was significantly lower with the S3D visualization.

Drivers adapt their speed earlier and more appropriately with a S3D visualization. Additionally, the workload is significantly lower regarding the mental demand, performance, effort and the overall measure. The participants generally have a high acceptance of the S3D visualization. Overall it can be concluded that a digital twin in S3D does prepare drivers of upcoming take over requests. This results in a more foresighted, safer driving.

Limitations In this study it is assumed that the driver has enough time to interact with the digital representation of the highway and that therefore, an overview of the traffic situation can be gained.

While the overview completely represents all traffic objects, the placement of it forces the driver to look away from the road. If drivers need to take over control of the vehicle, first they need to turn towards the steering wheel again. Therefore, this visualization would just be beneficial if drivers have enough time to react on the critical situation. It is questionable that a digital twin would be very helpful in case of a high frequency of take over requests.

0 2 4 6 8 10 12 14 16 18 20

Mental Demand

Physical Demand

Temporal Demand

Performance Effort Frustration Overall

Score

NASA TLX Category

Baseline Condition S3D Visualization Condition

Figure 5.7: Workload scores of the NASA TLX. The error bars show the standard error of each category.

With an automated detection of critical events, drivers could still inform themselves about the critical situation, how the situation looks like and how the traffic is reacting upon it. Emergency vehicles could get information, if a rescue lane is formed, if other emergency vehicles are on their way and the location of the accident.

Nevertheless, for smart infrastructure representations for drivers, many questions remain. For exam-ple, if it is possible to increase the situation awareness of drivers in a more non-intrusive manner, without having a complicated real world representation. The digital twin is not abstracted and the context information needs to be derived from the representation itself. In the next section, we there-fore look at abstract visualizations of upcoming situations that indicate that a critical situation is ahead.

Visualizations that Look Beyond