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2: Visualization of SSE, SSR & SST. 

2: Visualization of SSE, SSR & SST. 

Source publication
Thesis
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Highly automated driving is currently being widely discussed and likely to enter series vehicles within the next few decades. While the system takes over longitudinal and lateral control, the driver is still needed as a fall-back level in case system limits emerge. In those situations, the system prompts a take-over request, and the driver has to r...

Citations

... The countdown decreased in 1-min increments when more than 3 min remained and in 30-s increments when lower. Since acoustical RtIs 7 s before the system limit are widely used for emergency RtIs (Gold, 2016), an acoustical cascade (28, 14, and 7 s prior to system limit) was implemented to inform users about upcoming predictable transitions. Additional pop-up text boxes explained the reason for the takeover (e.g., "Construction Site please take-over now"). ...
Article
With the introduction of Level 3 and 4 automated driving, the engagement in a variety of non-driving related activities (NDRAs) will become legal. Previous research has shown that users desire information about the remaining time in automated driving mode and system status information to plan and terminate their activity engagement. In past studies, however, the positive effect of this additional information was realized when it was integrated in or displayed close by the NDRA. As future activities and corresponding items will be diverse, a device-independent and non-interruptive way of communication is required to continuously keep the user informed, thus avoiding negative effects on driver comfort and safety. With a set of two driving simulator studies, we have investigated the effectiveness of ambient light display (ALD) concepts communicating remaining time and system status when engaged in visually distracting NDRAs. In the first study with 21 participants, a traffic light color-coded ALD concept (LED stripe positioned at the bottom of the windshield) was compared to a baseline concept in two subsequent drives. Subjects were asked to rate usability, workload, trust, and their use of travel time after each drive. Furthermore, gaze data and NDRA disengagement timing was analyzed. The ALD with three discrete time steps led to improved usability ratings and lower workload levels compared to the baseline interface without any ALD. No significant effects on trust, attention ratio, travel time evaluation, and NDRA continuation were found, but a vast majority favored the ALD. Due to this positive evaluation, the traffic light ALD concept was subsequently improved and compared to an elapsing concept in a subsequent study with 32 participants. In addition to the first study, the focus was on the intuitiveness of the developed concepts. In a similar setting, results revealed no significant differences between the ALD concepts in subjective ratings (workload, usability, trust, travel time ratings), but advantages of the traffic light concept can be found in terms of its intuitiveness and the level of support experienced.
... The driver state transition means the reorientation of the driver state from non-driving related task (NDRT) or any other non-attentive state to a wakeful attentive driver state. The driver intervention [9] refers to the deactivation of the automated mode by the driver, which can be issued in distinguished ways depending on the system design. The control stabilization interval is an additional time window required by the driver to gain the driving precision and to increase the control performance to the average driving performance of the individuals. ...
Article
Full-text available
In automated vehicles, the collaboration of human drivers and automated systems plays a decisive role in road safety, driver comfort, and acceptance of automated vehicles. A successful interaction requires a precise interpretation and investigation of all influencing factors such as driver state, system state, and surroundings (e.g., traffic, weather). This contribution discusses the detailed structure of the driver-vehicle interaction, which takes into account the driving situation and the driver state to improve driver performance. The interaction rules are derived from a controller that is fed by the driver state within a loop. The regulation of the driver state continues until the target state is reached or the criticality of the situation is resolved. In addition, a driver model is proposed that represents the driver’s decision-making process during the interaction between driver and vehicle and during the transition of driving tasks. The model includes the sensory perception process, decision-making, and motor response. The decision-making process during the interaction deals with the cognitive and emotional states of the driver. Based on the proposed driver-vehicle interaction loop and the driver model, an experiment with 38 participants is performed in a driving simulator to investigate (1) if both emotional and cognitive states become active during the decision-making process and (2) what the temporal sequence of the processes is. Finally, the evidence gathered from the experiment is analyzed. The results are consistent with the suggested driver model in terms of the cognitive and emotional state of the driver during the mode change from automated system to the human driver.
... This could lead to higher risk tolerance, even though we could not see any indication for over-trust or any similar phenomena. In addition, the absolute values of the dependent variables longitudinal and lateral acceleration might be limited due to the missing vehicle dynamics of the static driving simulator compared to dynamic driving simulators or real vehicles [93]. Nevertheless, we consider our used driving simulator suitable for the relative comparison of the HMI concepts [93]. ...
... In addition, the absolute values of the dependent variables longitudinal and lateral acceleration might be limited due to the missing vehicle dynamics of the static driving simulator compared to dynamic driving simulators or real vehicles [93]. Nevertheless, we consider our used driving simulator suitable for the relative comparison of the HMI concepts [93]. ...
Article
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.
... Furthermore, although the exercise of NDRA has been added in studies of the take-over procedure, only the influence of NDRA on fatigue state has been studied [10]. Other authors focus on the take-over quality and duration, but without including the previously performed activity [11,12]. In the development of an attention and activity assistant, NDRA are considered, but the studies are mainly limited to the Level 3 mode and regarding Level 4, they only refer to the take-over duration after a sleep phase [13]. ...
... He must then place the book in the center console with his right hand (2-4) and stow his reading glasses (5)(6)(7)(8). Finally, to take over, he must place his hands on the steering wheel (9), look ahead (10) and simultaneously guide his foot to the pedal (11). As another example, the NDRA "watching a movie with VR glasses" is structured using the HoMoTo concept in Table 4 and described in the MTM process language in Table 7. ...
... First, upon receiving a TOR, the driver must remove and stow the VR glasses (1)(2)(3)(4)(5). Then, he must straighten his seat, put himself in an upright position (6)(7)(8)(9), and rotate his seat 90° (10)(11)(12) to put on his shoes (13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26). After that, he must again rotate his seat to the driver position (27-29) and advance (30-32). ...
Conference Paper
Autonomous driving allows for the first time from a legal point of view to permanently pursue non-driving related tasks. While during highly automated driving (SAE Level 3) the driver must be constantly ready to take over, this is no longer the case in fully automated mode (SAE Level 4). Nevertheless, there will be situations in which take-over is required. The take-over situations in Level 4 will be more complex, since more activities will be permitted. Automobile manufacturers must ensure a safe take-over process with the aid of appropriate vehicle interior design. With the help of the HoMoTo-approach presented here, take-over scenarios can be broken down into substeps and fixed time values can be assigned to the individual movement sequences using the Methods-Time-Management technique. Two examples show that the application of this method is suitable for optimizing the take-over process, however further adjustments to the procedure are necessary in order to obtain valid results.
... With intervention rates up to 70% in at least one of the MRMs during a 25 minute drive, we can expect that drivers will tend to take over manual control in the future as well. This in turn leads to the known problems of transition phases of level 3 automation [11,12] that were observed in our study. Therefore, more countermeasures need to be taken in the future. ...
Chapter
Minimal Risk Maneuvers (MRMs) are introduced to reduce the risk of an accident during the transition from automated to manual driving. In this paper, we present the results of a dynamic driving simulator study with 56 participants with the control authority as the independent variable, i.e. allowing and blocking driver input during the MRM. In order to not communicate wrong information, input blocking was established by disabling the brake and gas pedal but not the steering wheel. The latter turned according to the performed MRM and participants had to overcome high counterforces to change the vehicle’s direction. Two scenarios on a highway were investigated with the ego vehicle located in the right lane and only differing in the implemented MRM, i.e. stopping in the own lane or maneuvering to the shoulder lane combined with a standstill. Our results show a high intervention rate in both groups. Participants intervened mainly by maneuvering into the middle lane and after the Human-Machine-Interface announced the upcoming maneuver. In total, four accidents and five dangerous situations occurred due to interventions in both groups. Trajectories during re-entering into traffic showed that participants favored the middle lane over the shoulder lane here as well. To conclude, allowing or blocking driver intervention did not reduce the risk of an accident and more countermeasures need to be taken.
... The selection of test cases comprises mostly non-critical situations due to safety aspects of the test track experiments. Critical situations, e.g. with a limited time budget for take-overs, are important for safety-related assessments of ADS, such as controllability assessments [11], and have a low probability of occurrence. For evaluating the usability, especially the constructs efficiency and satisfaction [14], frequently recurring situations are of greater importance. ...
Chapter
The introduction of conditionally automated driving [25] implies repeated transitions of the driving task between the human operator and the automated driving system (ADS). Human-machine interfaces (HMIs) facilitating these shifts in control are essential. Usability serves as an important criterion to assess the quality of an HMI design. This paper derives a study design for assessing the usability based on the best practice advice by [1]. The paper covers the applied definitions of usability, the sample characteristics, the test cases, the HMIs, the dependent variables, the procedure, the conditions of use, and the testing environment. The study design will be applied in a driving simulator and three test track experiments in different countries within an ongoing project. This involves a number of safety, technical and resource constraints in the development of the study design. This paper describes the challenges and limitations of applying a generic best practice advice to the varying test settings. Furthermore, two HMI concepts are developed and evaluated in an expert assessment. The two concepts will serve as the research subjects in the series of experiments. The proposed study design is suitable for application in different test settings. Therefore, the comparability between the experiments is high. This paper provides a first step in a validation project with the overall goal to propose a practical approach to usability testing of ADS HMIs that covers different constructs of usability and appropriate dependent variables within their application areas.
... At the extent of our knowledge, no research studied the possibility of using ML to predict takeover quality for MaxSWA. RT was studied by Gold [8] (called Take-Over Time in the cited research) as well as lateral acceleration, longitudinal acceleration, and Time-to-Collision. Gold showed that computing the regression of RT was possible, opening the way to more complex models. ...
Chapter
Full-text available
Takeover requests in conditionally automated vehicles are a critical point in time that can lead to accidents, and as such should be transmitted with care. Currently, several studies have shown the impact of using different modalities for different psychophysiological states, but no model exists to predict the takeover quality depending on the psychophysiological state of the driver and takeover request modalities. In this paper, we propose a machine learning model able to predict the maximum steering wheel angle and the reaction time of the driver, two takeover quality metrics. Our model is able to achieve a gain of 42.26% on the reaction time and 8.92% on the maximum steering wheel angle compared to our baseline. This was achieved using up to 150 s of psychophysiological data prior to the takeover. Impacts of using such a model to choose takeover modalities instead of using standard takeover requests should be investigated.
... Furthermore, although the exercise of NDRA has been added in studies of the take-over procedure, only the influence of NDRA on fatigue state has been studied [10]. Other authors focus on the take-over quality and duration, but without including the previously performed activity [11,12]. In the development of an attention and activity assistant, NDRA are considered, but the studies are mainly limited to the Level 3 mode and regarding Level 4, they only refer to the take-over duration after a sleep phase [13]. ...
... He must then place the book in the center console with his right hand (2-4) and stow his reading glasses (5)(6)(7)(8). Finally, to take over, he must place his hands on the steering wheel (9), look ahead (10) and simultaneously guide his foot to the pedal (11). As another example, the NDRA "watching a movie with VR glasses" is structured using the HoMoTo concept in Table 4 and described in the MTM process language in Table 7. ...
... First, upon receiving a TOR, the driver must remove and stow the VR glasses (1)(2)(3)(4)(5). Then, he must straighten his seat, put himself in an upright position (6)(7)(8)(9), and rotate his seat 90° (10)(11)(12) to put on his shoes (13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26). After that, he must again rotate his seat to the driver position (27-29) and advance (30-32). ...
... All in all, there can be different reasons for such take-over situations. Gold (2016) differentiates between three reasons: ...
... A high time budget goes along with low urgency and a low time budget goes along with a high urgency. Gold (2016) differentiates between low, medium and high temporal criticality. Petermann-Stock, Hackenberg, Muhr, & Mergl (2013) assume that take-over times (i.e. ...
... In order to evaluate how good the participants' responses in these experiments are and in order to interpret the results, it is necessary to identify metrics that allow such a statement. In previous experiments, two categories of metrics have been used frequently (Gold, 2016): ...
Thesis
Full-text available
In Conditional Driving Automation, the human driver can engage in Non-Driving-Related Tasks and just has to intervene when requested by the system. In the current thesis, it was investigated how task-engagement while driving automated may affect drivers' fatigue and take-over performance. Therefore three driving-simulator and one on-road experiment were conducted. Results suggest that fatigue may occur in automated driving and negatively affects take-over performance.
... The term take-over request (TOR) was commonly used in publications up to 2017, e.g. see Damböck (2013), van den Beukel and van der Voort (2013), Gold (2016), Kerschbaum (2017). It was succeeded by RtI following a previous version of SAE J3016 (2018) and Marberger et al. (2017). ...
... • Take-over performance Take-over performance is an umbrella term for various metrics quantifying driver behavior during a take-over. The definition is taken from Gold (2016). The term incorporates time aspects, such as the TOT and quality aspects such as accelerations or the time to collision (TTC). ...
... questions due to drivers exiting and entering the driver-vehicle control loop. The take-over in CAD is identified to be the crucial element (Gold, 2016). The underlying understanding of take-overs in this thesis is depicted in Figure 2.2. ...
Thesis
Full-text available
The take-over is of crucial interest for the safety and comfort of conditionally automated driving. The basis of this thesis comprises four experiments and a modeling approach, providing an empirical comparison of various effects on take-over performance. Results show strong situational effects and very limited effects from the driver state. Subjective ratings from drivers show benefits of an HMI offering additional information on the take-over situation. The quantification of idiosyncratic effects utilizing mixed models offers a novel and more comprehensive understanding of human factors challenges.