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Malfunctions are a major challenge in partially automated driving. During such malfunctions, the driver must be able to adequately take over vehicle guidance without being requested to intervene. This may be particularly difficult in urban areas due to their high complexity and information density. Augmented Reality Head-Up Displays (ARHUDs) may have the potential to support the driver during the monitoring task by providing driving-related information at its required location in the primary field of view. The effects of an ARHUD compared to a Baseline concept in case of malfunctions were investigated in a driving simulation experiment with 52 participants. In a partially automated urban drive, participants experienced a longitudinal and a lateral malfunction in permuted order. The concepts -ARHUD or Baseline- were presented as a between-subject factor. The results showed significantly shorter take-over times when using the ARHUD, resulting in fewer crashes. For those who were able to avoid the crash, no differences in the take-over quality between both concepts were found. There was one difference in visual attention: the attention ratio on the instrument cluster was lower for the ARHUD. In addition, the ARHUD revealed a significantly higher trust and usability rating. However, there were no differences in acceptance and subjective workload between the two concepts. The results showed that the ARHUD has more potential to prevent crashes in the event of malfunctions compared to the Baseline. Nevertheless, the high number of crashes, regardless of the concept, showed the importance of a fallback level for partially automated urban driving.

Driver take-overs at system limits and the corresponding requests to intervene are not obligatory in highly automated driving. Therefore, minimal risk maneuvers may occur. In order to clearly communicate the automation status and the driver’s task at such system limits, ambient light displays seem to have a high potential. Therefore, two ambient light displays were investigated in a driving simulation experiment, mounted either at the bottom of the windshield or on the steering wheel. Forty participants experienced two request to intervene scenarios, and two minimal risk maneuver scenarios during a highly automated drive. In general, both ambient light displays seem to ensure correct driver’s reactions and safety, and lead to positive subjective ratings. Results revealed no significant differences between the two ambient light display positions regarding the take-over behavior, gaze behavior and subjective rating, except in terms of the perceived brightness.

With the introduction of ADAS systems and vehicle automation, an interface informing the driver of the automation state is required. This study evaluates the suitability of a visual interface comprising up to 64 LEDs on the steering wheel perimeter; it displays continuous visual feedback about the automation state—including notifications of an unscheduled hand-over due to sudden system failure. Three HMI (Human Machine Interface) designs were evaluated: two versions with visual cues on the steering wheel and one without (baseline). We implemented the designs in a driving simulator and compared the subjective responses of 38 participants to questionnaires measuring user experience, trust, and acceptance. The designs with visual cues improved the participants’ user experience, as well as their trust in, and acceptance of, automated vehicles. Moreover, both designs were well perceived by participants.

Vehicles with conditional automation will be introduced to the market in the next few years. However, the effect of fatigue as one component of the driver state on the take-over performance still needs to be quantified. To examine this question, a valid, real-time capable and preferably non-invasive method for assessing fatigue while driving automatically is required. For this purpose, we developed an objective driver fatigue assessment system based on the data of a commercial remote eye-tracking system. The fatigue assessment system fuses various metrics based on eyelid opening and head movement. In a validation study with 12 participants in a driving simulator, the fatigue assessment system achieved a sensitivity of 90.0 % and a specificity of 99.2 %. This approach makes a fatigue-state-dependent study design possible and can also provide a basis for advancing existing fatigue assessment systems in automated vehicles.

Fatigue is hypothesized to deteriorate take-over performance in conditionally automated driving. In a previous study, we found that fatigued drivers caused low take-over quality compared to alert drivers, however, did not produce more accidents. In this study, we replicated this experiment, but the time budget of taking over control was reduced from 6s to 5s evaluated take-over performance and take-over behavior to get a clearer picture of the effect of fatigue. In a further step, we consolidated the data of both experiments to have a greater sample size and to evaluate the influence of the scenario on take-over performance. Results revealed that fatigued drivers again showed a take-over behavior of lower quality (high decelerations, inappropriate trajectories and initial responses to the TOR, more crashes) compared to alert drivers. The time budget had a significant effect on take-over time and longitudinal acceleration.

In partially automated driving the vehicle takes over lateral and longitudinal control in specific situations. The driver acts as a supervisor of the driving automation system, and must be able to intervene immediately if functional limits are reached. Therefore, the driver needs to be aware of actual and planned system actions. Due to the complexity of the urban traffic environment when compared with that of well-structured highways, optimized driver-vehicle interaction is essential. The choice of a suitable display seems to be a key factor, since most information is perceived visually while driving. A promising method for supporting the driver is an Augmented Reality Head-Up Display (ARHUD). An ARHUD visualizes information at its actual location in the primary field of view in contrast to a conventional Head-Up Display (HUD). To investigate the potential of an ARHUD in comparison with a HUD, 30 participants took part in a driving simulator study experiencing a partially automated journey supported by an ARHUD or a HUD in a within-subjects design. Results show significant differences in gaze behavior for the two display concepts. The ARHUD results significantly in higher trust and lower subjective workload compared to the HUD. However, no significant differences exist in usability.

Instrument clusters represent the primary human-machine-interface for displaying driving-related information. Due to the changing relevance of driving-related information in automated driving, a reconfiguration of the display should be investigated. A display concept for an instrument cluster for partially and highly automated driving was developed that adapts the display and positioning of information to the level of automation. The developed concept was compared with a conventional display concept. Thirty participants took part in an experiment performing an occlusion task and a choice reaction time task. The statistical analysis revealed no significant differences in the response accuracy of the occlusion task between the display concepts. The results of the choice reaction time task show a significantly faster reaction time and in contrast to the occlusion task, a significantly lower response accuracy for the adaptive display concept compared to the conventional display concept. The subjective analysis revealed a preference of the adaptive display concept.

Due to the short range of the sensor technology used in automated vehicles, we assume that the implemented driving strategies may initially differ from those of human drivers. Nevertheless, automated vehicles must be able to move safely through manual road traffic. Initially, they will behave as carefully as human learners do. In the same way that driving-school vehicles tend to be marked in Germany, markings for automated vehicles could also prove advantageous. To this end, a simulation study with 40 participants was conducted. All participants experienced three different highway scenarios, each with and without a marked automated vehicle. One scenario was based around some roadworks, the next scenario was a traffic jam, and the last scenario involved a lane change. Common to all scenarios was that the automated vehicles strictly adhered to German highway regulations, and therefore moved in road traffic somewhat differently to human drivers. After each trial, we asked participants to rate how appropriate and disturbing the automated vehicle&rsquo, s driving behavior was. We also measured objective data, such as the time of a lane change and the time headway. The results show no differences for the subjective and objective data regarding the marking of an automated vehicle. Reasons for this might be that the driving behavior itself is sufficiently informative for humans to recognize an automated vehicle. In addition, participants experienced the automated vehicle&rsquo, s driving behavior for the first time, and it is reasonable to assume that an adjustment of the humans&rsquo, driving behavior would take place in the event of repeated encounters.

This article investigates the simultaneous interaction between an automated vehicle (AV) and its passenger, and between the same AV and a human driver of another vehicle. For this purpose, we have implemented a multi-vehicle simulation consisting of two driving simulators, one for the AV and one for the manual vehicle. The considered scenario is a road bottleneck with a double-parked vehicle either on one side of the road or on both sides of the road where an AV and a simultaneously oncoming human driver negotiate the right of way. The AV communicates to its passenger via the internal automation human&ndash, machine interface (HMI) and it concurrently displays the right of way to the human driver via an external HMI. In addition to the regular encounters, this paper analyzes the effect of an automation failure, where the AV first communicates to yield the right of way and then changes its strategy and passes through the bottleneck first despite oncoming traffic. The research questions the study aims to answer are what methods should be used for the implementation of multi-vehicle simulations with one AV, and if there is an added benefit of this multi-vehicle simulation compared to single-driver simulator studies. The results show an acceptable synchronicity for using traffic lights as basic synchronization and a distance control as the detail synchronization method. The participants had similar passing times in the multi-vehicle simulation compared to a previously conducted single-driver simulation. Moreover, there was a lower crash rate in the multi-vehicle simulation during the automation failure. Concluding the results, the proposed method seems to be an appropriate solution to implement multi-vehicle simulation with one AV. Additionally, multi-vehicle simulation offers a benefit if more than one human affects the interaction within a scenario.

During automated driving, there is a need for interaction between the automated vehicle (AV) and the passengers inside the vehicle and between the AV and the surrounding road users outside of the car. For this purpose, different types of human machine interfaces (HMIs) are implemented. This paper introduces an HMI framework and describes the different HMI types and the factors influencing their selection and content. The relationship between these HMI types and their influencing factors is also presented in the framework. Moreover, the interrelations of the HMI types are analyzed. Furthermore, we describe how the framework can be used in academia and industry to coordinate research and development activities. With the help of the HMI framework, we identify research gaps in the field of HMI for automated driving to be explored in the future.

During highly automated driving, the passenger is allowed to conduct non-driving related activities (NDRA) and no longer has to act as a fallback at the functional limits of the driving automation system. Previous research has shown that at lower levels of automation, passengers still wish to be informed about automated vehicle behavior to a certain extent. Due to the aim of the introduction of urban automated driving, which is characterized by high complexity, we investigated the information needs and visual attention of the passenger during urban, highly automated driving. Additionally, there was an investigation into the influence of the experience of automated driving and of NDRAs on these results. Forty participants took part in a driving simulator study. As well as the information presented on the human&ndash, machine interface (system status, navigation information, speed and speed limit), participants requested information about maneuvers, reasons for maneuvers, environmental settings and additional navigation data. Visual attention was significantly affected by the NDRA, while the experience of automated driving had no effect. Experience and NDRA showed no significant effect on the need for information. Differences in information needs seem to be due to the requirements of the individual passenger, rather than the investigated factors.