Society currently faces many complex and significant challenges, dilemmas that lack straightforward solutions and are deeply interconnected. These problems demand a transformative approach that involves reevaluating people’s values, beliefs, attitudes, and behavior. Amidst these ongoing challenges, there is a collective hint of pessimism regarding society’s perception to effectively overcome them.

To navigate this era of transformative change, it becomes increasingly critical to engage in dialogue and define future goals. However, the process of generating specific visions and objectives for the future currently lacks guidance. This project seeks to bridge this gap by integrating futuring methods, creativity, co-creation, and Artificial Intelligence (AI). AI has the ability to enhance the abilities of people in this process. Through this innovative approach, the aim is to empower collective envisioning and goal-setting, enabling individuals and organizations to actively explore and shape desirable futures. More positive images of the future can motivate the broader collective as well.

The initial phase of the project involved an extensive exploration of existing literature on co-creation, future visions, the impact of positive future visions, creativity, and AI. In-depth interviews provided valuable insights from each distinct aspect: creating art with AI, facilitating co-creation, and developing future visions. Furthermore, a cultural probe shed light on the aspects individuals require more guidance in when contemplating the future. The findings underscored the relative ease with which people envisioned negative futures, and potential future inventions, while demonstrating a lesser emphasis on the underlying rationale and societal value of these inventions. Additionally, the perceived difficulty level varied considerably, and the outcomes were diverse. In this regard, the utilization of tools can prove beneficial, facilitating smoother and more efficient conversations while helping individuals initiate and navigate discussions with greater ease.

The accumulated basis of knowledge formed a buildable concept, applying the structure of the creative facilitation phases: problem, idea, and solution finding. This buildable concept was tested through four short sessions. Adding and altering its basis to establish the Minimal Viable Method (MVM) and expand to longer sessions. All sessions were conducted in a real setting with a diverse range of topics relevant to the problem owner(s), allowing for continuous improvement and addressing specific research questions during each session. Participants actively engaged in providing feedback and evaluating their experience based on predefined design criteria.

The end result of this project was a Version 1 (V1) design of a toolkit to guide people through a co-creation session using AI to create future visions. The toolkit allows participants to focus on their specific goals and type of futures, it is designed with a facilitator in mind to guide the process. Although, the toolkit includes explanations and guiding signs usable by people without knowledge of these elements. The toolkit includes underlayments, a card deck; a professional and newbie version, different inspiration museum topic sets, and a more elaborate optional manual to guide new users using AI, creating future visions, and vision statements. The toolkit is available through the website: https://future-framing.nl.

This project adds to the current literature by providing a new method to envision the future in co-creation. Evaluation results from these sessions validate the positive impact of the method on participants through specific design criteria. The majority of participants expressed interest in joining another workshop. Additionally, it finds a general interest of people to acquiring skills to use AI. The design goal, to spark empowerment and ownership in participants while creating their own desired future vision in a co-creation session, is successfully implemented with this project. The findings suggest future development of the V1 toolkit and method can further improve experience and outcome. This correlates with future developments with AI, that shape both too.

Future introduction on automated vehicles (levels 3, 4, and 5) in urban traffic comes with several societal benefits, but also challenges. One of the biggest challenges that the research community expects from this introduction is the possible problems or critical situations caused by the lack of interpersonal communication between drivers and vulnerable road users in the form of eye contact or hand gestures. To solve this problem, external Human-Machine Interfaces seem to be a suitable solutions to enable AV-VRU communication. This project reviews relevant literature on eHMI design and Human-Robot interaction to identify current knowledge gaps. Moreover, generative user research was used to identify needs and wants from vulnerable road users when interacting with motorized vehicles in traffic. Predictability, perception of vehicle’s awareness, and knowing how to act around automated vehicles were found to be key elements for smooth interactions in scenarios that were considered critical by research participants, namely crossings without traffic lights and being passed by vehicles that share the road with cyclists.
In order to approach the problem, an iterative design process was followed to identify the best communication modalities and modes to be included in a final flexible and multimodal eHMI design, showing the potential of abstract light patterns and dynamic projections to enable AV transparency. The final design was evaluated through a video-based experiment in which participants were exposed to a baseline condition and the same scenario with the addition of the eHMI. This evaluation showed an improvement in the experience qualities evaluated in the presence of the eHMI designed, showing overall desirability and pointing out simplicity and flexibility as crucial qualities to design external communication systems.

Automated transportation technology has been rapidly evolving across all modes of transportation, with enormous potential to improve road safety and travel comfort. New challenges arose during the transition to full automation, such as increased instances of disagreements between the driver and the automation system over who should take control. Negotiation plays a crucial role in resolving such conflicts. The Mediator project, a research and innovation programme of the European Union’s Horizon 2020, is developing an intelligent system to meditate between human drivers and highly automated vehicles. This graduation project aims to design a human-machine interaction that can resolve conflicts with appropriate negotiations, thereby creating a pleasant user experience in highly automated vehicles. The challenge lies in striking the right balance between the driver's safety and autonomy in order to ensure that the Mediator system is accepted by a wide range of users. To tackle the challenge, conflicts between human drivers and highly automated vehicles were analyzed to identify the most severe conflict as a main focus. To resolve it, research referred to human-to-human negotiation styles to inspire possible ways of human-to-machine negotiation styles in a context of high automation. To determine the most appropriate negotiation styles, desirable human-to-machine interaction was investigated through interviews.It was found that most participants preferred to have a certain extent of autonomy only when safety is secured. It resulted in an approach of resolving conflicts with negotiation styles based on the reasons for the automation’s takeover requests. More specifically, persuasive negotiation should be employed with a competing style when the reason is about safety. When it comes to comfort, seductive negotiation should be employed with a collaborating style. These insights were translated to interaction qualities for negotiation in the design concept. A design goal was formulated after establishing a future worldview by using the Vision In Product Design (ViP) method. The goal was to create a pleasant experience through negotiation during control conflict. Furthermore, the driver's motivation to follow the automation’s recommendations was investigated through two design interaction cycles. It was found that increasing intrinsic and extrinsic incentives by audio interaction and rewards could effectively motivate drivers to follow recommendations. The final concept is a negotiation ritual consisting of voice messages and rewarding features. When there is a disagreement over who should take over, the negotiation ritual of either persuasive or seductive negotiation will be activated. In the persuasive negotiation ritual, the system will give takeover requests twice, emphasizing the urgency and consequences with a commanding tone and wording. In the seductive negotiation ritual, the system will only ask the driver once, emphasizing the benefit of control transfer with a friendly tone and wording. Furthermore, drivers will get rewards such as parking discounts when following recommendations.