Advanced Driver-Assistance Systems (ADASs) are used for increasing safety in the automotive domain, yet current ADASs notably operate without taking into account drivers’ states, e.g., whether she/he is emotionally apt to drive. In this paper, we first review the state-of-the-art of emotional and cognitive analysis for ADAS: we consider psychological models, the sensors needed for capturing physiological signals, and the typical algorithms used for human emotion classification. Our investigation highlights a lack of advanced Driver Monitoring Systems (DMSs) for ADASs, which could increase driving quality and security for both drivers and passengers. We then provide our view on a novel perception architecture for driver monitoring, built around the concept of Driver Complex State (DCS). DCS relies on multiple non-obtrusive sensors and Artificial Intelligence (AI) for uncovering the driver state and uses it to implement innovative Human–Machine Interface (HMI) functionalities. This concept will be implemented and validated in the recently EU-funded NextPerception project, which is briefly introduced.

This study introduces a new operational tool based on the AEIOU observational framework to support the design of adaptive human machine interfaces (HMIs) that aim to modify people’s behavior and support people’s choices, to improve safety using emotional regulation techniques, through the management of environmental characteristics (e.g., temperature and illumination), according to an approach based on the nudging concept within a design thinking process. The proposed approach focuses on research in the field of behavioral psychology that has studied the correlations between human emotions and driving behavior, pushing towards the elicitation of those emotions judged to be most suitable for safe driving. The main objective is to support the ideation of scenarios and/or design features for adaptive HMIs to implement a nudging strategy to increase driving safety. At the end, the results from a collaborative workshop, organized as a case study to collect concept ideas in the context of sports cars, will be shown and evaluated to highlight the validity of the proposed methodology, but also the limitations due to the requirement of prototypes to evaluate the actual effectiveness of the presented nudging strategies.

Behavioral changes are critical for addressing sustainability challenges, which have become increasingly urgent due to the growing impact of global greenhouse gas emissions on ecosystems and human livelihoods. However, translating awareness into meaningful action requires practical tools to bridge this gap. Mobile applications, utilizing strategies from human–computer interaction (HCI) such as gamification, nudging, and persuasive technologies, have proven to be powerful in promoting sustainable behaviors. To support designers in developing effective apps of this kind, theory-based design guidelines were created, drawing on established theories and design approaches aimed at shaping and encouraging virtuous user behaviors fostering sustainability. To make these guidelines more accessible and enhance their usability during the design phase, this study presents their transformation into the SBAM card deck, a deck of 11 design cards. The SBAM cards aim to simplify theoretical concepts, stimulate creativity, and provide structured support for design discussions, helping designers generate solutions tailored to specific project contexts. This study also evaluates the effectiveness of the SBAM cards in the design process through two workshops with design students. Results show that the cards enhance ideation, foster creativity, and improve designers’ perceived self-efficacy compared to the exploitation of the same design guidelines information presented in traditional textual formats. This paper discusses the SBAM cards design and evaluation methodology, findings, and implications, offering insights into how the SBAM design cards can bridge the gap between theory and practice in sustainability-focused mobile app development. To ensure broader accessibility, the SBAM cards have been made available to the public through a dedicated website.

**Abstract text** Introduction The integration of Artificial Intelligence (AI) in radiology has gained significant attention in recent years. To | POSTER