While the introduction of automated driving is intended to make driving safer and more comfortable, it may also make the interaction between user and vehicle more complex and prone to confusion. It is therefore important to be able to assess to what extent ADSs provide their users with clear and unambiguous information concerning system state and concerning allowed or expected actions from the side of the user. In this project, we have developed a questionnaire to measure whether the information provided by an ADS to its user is experienced as clear and unambiguous. The questionnaire consists of nine items, which were shown to discriminate between good and bad HMI designs in terms of user perception and comprehension. The development and evaluation of this questionnaire was done in several steps. First, a set of 15 questionnaire items was developed that could be presented as Likert scale items on a 7 point scale. Next, an online survey was conducted to test the reliability and internal consistency of the questionnaire as well as explore underlying factors. Data analysis showed that responses on some of the items were highly correlated. A subset of 9 items was found to be able to discriminate well between HMIs with different levels of clarity and (un)ambiguity. Exploratory factor analysis revealed two underlying factors, which could be interpreted to relate to perception and comprehension of HMI information. In the final step, the 9-item questionnaire was tested in an experimental setup. 23 Participants were presented with movie clips of transitions from or to automated driving, including a view of the traffic situation (through the windshield) and of the instrument cluster and steering wheel. Again, HMIs were designed to be either clear and unambiguous or unclear/ambiguous. The results showed that the questionnaire discriminated reliably between both versions of the HMI. As in the online survey, high reliability and internal consistency were observed. Additionally, qualitative measurement of user experience by means of Product Reaction Cards also showed clear differences between the two different HMI versions.

To develop safe automated driving functions, knowing road-user’s lane change behaviour is critical. This detection problem may depend on multiple aspects such as road conditions, location, and weather. To understand the effect of these situational variables, this work introduces a lane change detection algorithm and assessed its performance under various light conditions, road types and weather conditions. The algorithm has developed in L3Pilot: a large-scale European pilot project on level 3 automation. In the current study, the algorithm was tested with data from a Dutch Field Operational Test on SAE Level 2 systems. The algorithm was assessed against manually annotated video recordings. New is that validation was executed with Dutch Field Operational Test data of different participants and vehicles, distinguishing three situational variables factors. These were day vs night, motorways vs trunk roads and dry vs rain. A bootstrap procedure was used to assess the statistical significance of differences among the conditions. The conclusion is that the algorithm in combination with the provided data is effective in detecting lane changes when data is collected on a sample of Dutch motorways, irrespective of light and precipitation conditions. However, the quality of the sensor signals was worse on trunk roads, yielding significantly worse lane change detection performance (for all light and precipitation conditions).

Redirected walking techniques allow people to walk in a larger virtual space than the physical extents of the laboratory. We describe two experiments conducted to investigate human sensitivity to walking on a curved path and to validate a new redirected walking technique. In a psychophysical experiment, we found that sensitivity to walking on a curved path was significantly lower for slower walking speeds (radius of 10 m versus 22 m). In an applied study, we investigated the influence of a velocity-dependent dynamic gain controller and an avatar controller on the average distance that participants were able to freely walk before needing to be reoriented. The mean walked distance was significantly greater in the dynamic gain controller condition, as compared to the static controller (22 m versus 15 m). Our results demonstrate that perceptually motivated dynamic redirected walking techniques, in combination with reorientation techniques, allow for unaided exploration of a large virtual city model.

The aim of Redirected Walking (RDW) is to redirect a person along their path of travel in a Virtual Environment (VE) in order to increase the virtual space that can be explored in a given tracked area. Among other techniques, the user is redirected on a curved real-world path while visually walking straight in the VE (curvature gain). In this paper, we describe two experiments we conducted to test and extend RDW techniques. In Experiment 1, we measured the effect of walking speed on the detection threshold for curvature of the walking path. In a head-mounted display (HMD) VE, we found a decreased sensitivity for curvature for the slowest walking speed. When participants walked at 0.75 m/s, their detection threshold was approximately 0.1m-1(radius of approximately 10m). In contrast, for faster walking speeds (>;1.0m/s), we found a significantly lower detection threshold of approximately 0.036m-1(radius of approximately 27m). In Experiment 2, we implemented many well known redirection techniques into one dynamic RDW application. We integrated a large virtual city model and investigated RDW for free exploration. Further, we implemented a dynamic RDW controller which made use of the results from Experiment 1 by dynamically adjusting the applied curvature gain depending on the actual walking velocity of the user. In addition, we investigated the possible role of avatars to slow the users down or make them rotate their heads while exploring. Both the dynamic curvature gain controller and the avatar controller were evaluated in Experiment 2. We measured the average distance that was walked before reaching the boundaries of the tracked area. The mean walked distance was significantly larger in the condition where the dynamic gain controller was applied. This distance increased from approximately 15m for static gains to approximately 22m for dynamic gains. This did not come at the cost of an increase in simulator sickness. Applying the avatar controller did reveal an effect on walking distance or simulator sickness.

Despite many recent developments in virtual reality, an effective locomotion interface which allows for normal walking through large virtual environments was until recently still lacking. Here, we describe the new CyberWalk omnidirectional treadmill system, which makes it possible for users to walk endlessly in any direction, while never leaving the confines of the limited walking surface. The treadmill system improves on previous designs, both in its mechanical features and in the control system employed to keep users close to the center of the treadmill. As a result, users are able to start walking, vary their walking speed and direction, and stop walking as they would on a normal, stationary surface. The treadmill system was validated in two experiments, in which both the walking behavior and the performance in a basic spatial updating task were compared to that during normal overground walking. The results suggest that walking on the CyberWalk treadmill is very close to normal walking, especially after some initial familiarization. Moreover, we did not find a detrimental effect of treadmill walking in the spatial updating task. The CyberWalk system constitutes a significant step forward to bringing the real world into the laboratory or workplace.

For us humans, walking is our most natural way of moving through the world. One of the major challenges in present research on navigation in virtual reality is to enable users to physically walk through virtual environments. Although treadmills, in principle, allow users to walk for extended periods of time through large virtual environments, existing setups largely fail to produce a truly immersive sense of navigation. Partially, this is because of inadequate control of treadmill speed as a function of walking behavior. Here, we present a new control algorithm that allows users to walk naturally on a treadmill, including starting to walk from standstill, stopping, and varying walking speed. The treadmill speed control consists of a feedback loop based on the measured user position relative to a given reference position, plus a feed-forward term based on online estimation of the user's walking velocity. The purpose of this design is to make the treadmill compensate fully for any persistent walker motion, while keeping the accelerations exerted on the user as low as possible. We evaluated the performance of the algorithm by conducting a behavioral experiment in which we varied its most important parameters. Participants walked at normal walking speed and then, on an auditory cue, abruptly stopped. After being brought back to the center of the treadmill by the control algorithm, they rated how smoothly the treadmill had changed its velocity in response to the change in walking speed. Ratings, in general, were quite high, indicating good control performance. Moreover, ratings clearly depended on the control algorithm parameters that were varied. Ratings were especially affected by the way the treadmill reversed its direction of motion. In conclusion, controlling treadmill speed in such a way that changes in treadmill speed are unobtrusive and do not disturb VR immersiveness is feasible on a normal treadmill with a straightforward control algorithm.