Keywords

1 Introduction

Automated vehicles (AVs) are seen as one of the key technologies and major technological advancements with the potential to influence and shape our future mobility and quality of life [2]. In recent years, significant progresses have been achieved in automated driving technologies and level 3 to 4 AVs are expected to become available to end-user market within a decade [3]. The advent of autonomous vehicles and automated driving have the potential to bring considerable benefits to modern societies, specifically: increasing road safety through reducing accidents caused by human errors; increasing transport system efficiency, reducing road congestion, energy consumption and emission of vehicles; increase users’ comfort and freedom, enabling users to engage in other activities when on board of AVs; ensure mobility for impaired users and elderly.

This last point is particularly relevant considering that many countries around the world are facing a demographic shift toward an aging society. Because of higher life expectancy and consistently low birth rates there is a shift of the age distribution in global population. According to predictions of UN [4], the number of people aged 60 and above will double from 962 million in 2017 to 2.1 billion in 2050, while the number of people aged 80 and over will triple from 137 million in 2017 to 425 million in 2050. Level 3 to 4 AVs will allow users to be released from the driving task for extended periods, however they will be requested to take back control of the vehicle in specific situations. The increase in the elderly population will result in a growing demand of transport systems in many countries and an increase of older road users, which could lead to significant complications in safety as well as mobility and sustainability. This highlights the importance of designing user friendly, safe, adaptable and accessible for elderly drivers AVs interfaces.

Furthermore, [5], through an aggregation of the material found on Scopus, indicate that social science references linked to AVs represent less than 6% of the total. This suggests that most AV related research, primarily academic, has focused on technical and technological aspects of AVs and not on the associated social and behavioral issues. With that said, one of the major research needs in the field of AVs is the integration of human factors in the design of HMI for AVs, considering human needs, behaviors and users’ characteristics, aiming at developing ways to foster efficient human-automation interaction and collaboration.

Vulnerable Road Users (VRU) are defined in the ITS Directive as “road users, such as pedestrians and cyclists as well as motor-cyclists and persons with disabilities or reduced mobility and orientation”. According to this definition, traffic participants can be included in the VRUs category according the mode of transport used (e.g. bicycle, mopeds, etc.) and by user characteristics (e.g. elderly with different for of disabilities).

1.1 Gender and Age Differences in Automated Vehicles Studies

To design HMIs and effective strategies to foster efficient and safe human-automation collaboration it is crucial to consider users’ personal characteristics. Recent studies focused mostly on gender and age differences in acceptance, considering attitudes, trust and intention to use as its components. Trust and Intention to use are classic constructs of technology acceptance models [6,7,8] and represent the degree of users’ trust in automation and their intention to actually use the system, respectively. König and Neumayr [9] studied differences in attitudes dependent on personal characteristics, and they found that females, older people and people living in urban areas (as opposed to rural ones) tended also to bear less positive attitudes towards AVs. Many studies have tried to assess the difference between male and female about technology-related anxiety. There is a tendency to find greater anxiety among females than males about technology use [10]. Similarly, some research reported more driving anxiety among women than men [11]. Hohenberger et al. [12] explored anxiety and pleasure related to AVs’ usage and found that men were more likely to feel pleasure than women whereas women were more likely to feel anxiety than men. A recent meta-analysis of the effect of gender on attitudes towards technology use show that men have more positive attitude towards the use of new technologies than women [13]. The authors investigated the components of attitudes and observed that the gender difference in affect and self-efficacy is decreasing, while the difference regarding beliefs about the use and societal function of new technologies remain constant. Our research considers these gender-related specificities when proposing a theoretical framework for HMI in AVs. It is of utmost importance to consider those results when designing HMI for AVs in order to reduce barriers to acceptance.

As level 3 to 4 AVs require the driver to take over in specific situations (i.e. take-over request scenarios), driving behavior continues to be a relevant subject of study when considering AV usage of elder population. The effects of age on driving behavior were studied extensively in psychological literature. While the most examined set of variables mediating this relationship are cognitive abilities, researchers in more recent works [14] call for greater attention for integrating affects in research of elders’ driving behavior. A meta-analysis of the association between driving anger and driving outcomes [15] shows that this link is weaker in younger adults than in elders, suggesting that older drivers have a lower risk of driving disruptively because of anger. It is important to note that the negative stereotypes about elderly drivers are the major cause of lower cognitive abilities [16].

1.2 User Interface for Elderly

An efficient and useful human-computer interface must consider the abilities of the users who will operate it and anticipate the eventual difficulties that different users (e.g. elderly) must adapt and deal with [17]. A study by Phiriyapokanon [18] (2011), highlighted that layering different modes of information in interface design can reduce older users’ cognitive workload. Another relevant concept to be taken into account when designing user interfaces are interactive interfaces [19, 20]. Furthermore, Hasan and Ahmed [21] found that the interactive interface concept has direct influence on users’ perceived ease of use and perceived usefulness.

1.3 External Interfaces Addressing Cyclists

Nowadays, the interaction between motorized vehicles and vulnerable road users, such as cyclists is particularly critical in terms of traffic safety [22,23,24]. To maximize the potential benefits of introducing AVs in urban roads it is crucial to address the interactions and communication between AVs and vulnerable road users (i.e. cyclists and pedestrians). This topic has received limited attention in literature so far, but the number studies addressing vulnerable road users’ interactions with AVs is increasing [25]. These interactions rely on the interpretation of two clusters of signals: (a) vehicle dynamics and other vehicle-centric cues using signs, lights and sound; and (b) through gestures, postures, and eye contact between drivers and cyclists [26]. Establishing eye contact serves as a confirmation that a driver and a cyclist have noticed each other [27]. In AVs, with the transfer of control from the driver to the vehicle, there is a risk that pedestrians will not be able to rely on these driver-centric cues anymore. Observational studies have shown that cyclists, when crossing red-light controlled intersections, heavily rely on visual search strategies to find a gap in the oncoming traffic flow [28].

Recent findings indicate that the communicative needs may change due to use of vehicle automation [26]. To sustain a high level of cyclists’ perceived safety in the interaction with AVs, it could be beneficial to provide the cyclist with the corresponding information in some other way (e.g., by means of an external vehicle interface). Those interfaces could also be integrated with wireless detection technology of cyclists, increasing the communication potential and thus the perceived safety [29]. Furthermore, it is important to take into consideration that negative attitudes towards cyclists can influence the acceptance of safety system technologies addressing cyclists [30].

1.4 Human-Machine Cooperation in Automated Driving

In the studies focused on automated driving (AD) systems of SAE Level 3, the focus is mainly on transitions of control between manual driving (MD) and AD [31]. Specifically, the requests for switching from AD to MD from the side of the system in takeover scenarios (“take-over request”, TOR) receive a great degree of attention [32, 33], since this situation can raise significant safety concerns, especially if the driver fails to take over vehicle control. However, there is an evidence gap for scenarios in which an operating AD system shares the control authority with the human driver and our research aims to expand the knowledge base of this issue.

In our view, design of AV HMIs with the capacity of mutual communication with the driver should consider that the driver has to be aware of the system limits in advance, especially in take-over situations [34].

Furthermore, the users tend to attribute intentionality and rationality automated systems, which, in some cases, are considered to be “team members” [35]. This shows that humans are inherently interested in understanding the functioning of automated systems in the attempt to understand and predict behavior of such systems.

The means used by the system to communicate with the driver are another topic covered by our study. As the driver is likely to be engaged in secondary tasks, it is necessary to direct her or his attention to the relevant information about the traffic situation and provide this information in clear and understandable way. Given that elder drivers might have difficulties perceiving certain types of alerts, we hypothesize that the use of multi-modal alerts (i.e. of those that use a combination of visual, haptic, and auditory stimuli) will elicit quicker driver responses than single-mode alerts. This effect was reported by Ho et al. [36], who showed the benefits in term of response time when using a multi-modality warning signals. In multi-modality conditions, the information processing was performing better as well as the spatial experience.

Shared control framework for vehicle control is a frame of reference for research focused on human-machine cooperation within the automotive domain [37].

According to Guo et al. [38], HMI design is one of key factors which enable efficient cooperation between the system and the driver. These authors state that the following HMI principles should be applied when designing AV HMIs:

  • Showing the driving context. This principle follows the concept of common frame of reference as proposed by Hoc [39]. The system should be able to provide information about the driving context so that it shares the perception of the driving situation with the driver.

  • The system communicates its intentions to the driver and provides available alternatives.

  • The system provides a way for the driver to choose an alternative.

Sethumadhavan [40] provides several suggestions about designing safer Human-Automation Interaction:

  • Automation user interfaces should increase the situational awareness of the driver and enable her or him to understand the traffic situations;

  • As the behavior of the system changes because of learning, it should communicate these changes to the user to enable her or him to update the mental model of the system’s functioning;

  • The system should be able to cooperate with the driver, coordinate their intentions and behavior, and collaborate to reach the destination safely.

1.5 Challenges in Human-Automation Interaction and Cooperation

According to the aforementioned notions, in order to foster efficient human-automation collaboration and pleasant interactions between the vehicle and its user, AVs interfaces should be strictly interconnect with the vehicle artificial intelligence. Such artificial intelligence and it’s HMIs should be capable of:

  • Dynamically recognize partially unknown behavior patterns that were not originally implemented in the system;

  • Interacting with humans through fine, low-latency yet socially acceptable control strategies;

  • Use natural and multi-modal communication strategies which mandates common-sense knowledge and the representation of possibly divergent mental models;

  • Assess user states and identify the user’s individual and collaborative cognitive skills;

  • Planning and distributing tasks for the driver and the vehicle based on the situation;

The present study focuses focus on a specific class of interactions: human–robot collaborative task achievement supported by multi-modal and situated communication.

The study will contribute in defining and test an innovative framework for AVs’ HMI design, capable of achieving efficient human-automation collaboration by taking into account, at every stage, the intentions, skills and behavior of its human partner. According to the proposed concept, AVs must be able to recognize, understand and participate in communication actions. As for the driving task, the vehicle should be able to take part in joint actions, both pro-actively (by planning and proposing resulting plans to the human) and reactively, similarly to what has been tested for automated robots by Lemaignan et al. [41].