Drones are likely to enter social spaces in the foreseeable future. Novel Human-Drone Interactions (HDI) will foster beyond typical drone-operator interaction, posing new human factors challenges. However, the specific focus areas for HDI research remain unclear. This study conducts 11 expert interviews to identify potential use cases and human factors challenges for HDI in public spaces. Initial drone use cases include emergency response and delivery scenarios, where the general public may interact as recipients and bystanders, each posing unique challenges. Uncertainty, stemming from a lack of awareness, emerges as a significant human factors concern, impacting perceived risk. Addressing this uncertainty, especially in recipients, may involve refining drone behaviour, physical appearance, and interface design. The challenges identified in this study lay the groundwork for future HDI research in public spaces.

The use of drones is expected to increase for delivering groceries or medical equipment to individuals. Understanding how people perceive drone behavior, specifically in terms of approach trajectories and delivery methods, and identifying factors that induce feelings of uncertainty is crucial for perceived safety and trust. This virtual reality experiment investigated the impact of drone approach trajectories and delivery methods on feelings of uncertainty. Forty-five participants observed a drone approaching in an orthogonal or a curved path and either, delivering packages by landing or using a cable while hovering above eye level. We found that participants felt uncertain and unsafe, especially when looking up at drones approaching with orthogonal paths. Curved paths led to lower feelings of uncertainty, with comments such as being more natural, trustful, and safe. Feelings of uncertainty arose while landing on the ground due to altitude changes and potential collision concerns. Using a cable instead of actually landing for delivery reduced feelings of uncertainty and increased trust. The study recommends drones avoid hovering near humans, especially after landing. Furthermore, the study suggests exploring design solutions, including design aesthetics and human–machine interfaces, that clearly convey drone intentions to help reduce feelings of uncertainty.

Drones will likely enter public spaces soon for deliveries, interacting with humans as recipients or bystanders. Understanding their requirements and factors contributing to uncertainty is crucial for safer Human-Drone Interaction design. Twelve participants were interviewed and engaged in focus groups. Preliminary results indicate the need for clear communication of drone intentions through design and interfaces, guiding future research and design concepts to be tested with user studies for delivery drones in public spaces.

With the introduction of driving automation, the driving task has become a shared task between driver and vehicle. Today, an increasing amount of driving tasks can be performed by the automation and the view of driver and automation acting as collaborative partners has been well established. Although this notion has been adopted in the research and design domains, means to assess the quality of the driver-automation interaction in a structured way are still lacking. Moreover, most design evaluations are usually addressed either from a technical stance or from a human factors viewpoint, which does not comply with a general acknowledged view of a unified driver-vehicle system. The aim of the current study is therefore to investigate the possibility to quantitatively evaluate the quality of the driver and vehicle cooperation. Seven dimensions indicative for the quality of cooperation are identified, based on a literature survey and expert input during focus groups. This work potentially supports road authorities, legislation, regulation and original equipment manufacturers to monitor, evaluate and design driver-vehicle cooperation.

This paper assessed four types of human-machine interfaces (HMIs), classified according to the stages of automation proposed by Parasuraman et al. [“A model for types and levels of human interaction with automation,” IEEE Trans. Syst. Man, Cybern. A, Syst. Humans, vol. 30, no. 3, pp. 286-297, May 2000]. We hypothesized that drivers would implement decisions (lane changing or braking) faster and more correctly when receiving support at a higher automation stage during transitions from conditionally automated driving to manual driving. In total, 25 participants with a mean age of 25.7 years (range 19-36 years) drove four trials in a driving simulator, experiencing four HMIs having the following different stages of automation: baseline (information acquisition-low), sphere (information acquisition-high), carpet (information analysis), and arrow (decision selection), presented as visual overlays on the surroundings. The HMIs provided information during two scenarios, namely a lane change and a braking scenario. Results showed that the HMIs did not significantly affect the drivers' initial reaction to the take-over request. Improvements were found, however, in the decision-making process: When drivers experienced the carpet or arrow interface, an improvement in correct decisions (i.e., to brake or change lane) occurred. It is concluded that visual HMIs can assist drivers in making a correct braking or lane change maneuver in a take-over scenario. Future research could be directed toward misuse, disuse, errors of omission, and errors of commission.

Conditionally automated driving systems may soon be available on the market. Even though these systems exempt drivers from the driving task for extended periods of time, drivers are expected to take back control when the automation issues a so-called take-over request. This study investigated the interaction between take-over request modality and type of non-driving task, regarding the driver's reaction time. It was hypothesized that reaction times are higher when the non-driving task and the take-over request use the same modality. For example, auditory take-over requests were expected to be relatively ineffective in situations in which the driver is making a phone call. 101 participants, divided into three groups, performed one of three non-driving tasks, namely reading (i.e., visual task), calling (auditory task), or watching a video (visual/auditory task). Results showed that auditory and tactile take-over requests yielded overall faster reactions than visual take-over requests. The expected interaction between takeover modality and the dominant modality of the non-driving task was not found. As for self-reported usefulness, auditory and tactile take-over requests yielded higher scores than visual ones. In conclusion, it seems that auditory and tactile stimuli are equally effective as take-over requests, regardless of the non-driving task. Further study into the effects of realistic non-driving tasks is needed to identify which non-driving tasks are detrimental to safety in automated driving.

The task of car driving is automated to an ever greater extent. In the foreseeable future, drivers will no longer be required to touch the steering wheel and pedals and could engage in non-driving tasks such as working or resting. Vibrotactile displays have the potential to grab the attention of the driver when the automation reaches its functional limits and the driver has to take over control. The aim of the present literature survey is to outline the key physiological and psychophysical aspects of vibrotactile sensation and to provide recommendations and relevant research questions regarding the use of vibrotactile displays for taking over control from an automated vehicle. Results showed that a distinction can be made between four dimensions for coding vibrotactile information (amplitude, frequency, timing, and location), each of which can be static or dynamic. There is a consensus that frequency and amplitude are less suitable for coding information than location and timing. Vibrotactile stimuli have been shown to be effective as simple warnings. However, vibrations can evoke annoyance, and providing vibrations in close spatial-temporal proximity might cause a lack of comprehension of the signal. We describe the sequential stages of a take-over process and argue that vibrotactile displays are a promising candidate for redirecting the attention of a distracted driver. Furthermore, vibrotactile displays hold potential for supporting cognitive processing and action selection while resuming control of an automated vehicle. Finally, we argue that multimodal feedback should be used to assist the driver in the take-over process.