One of the main causes of lack of acceptance in innovation is ignoring the needs and preferences of potential customers in the development phases. In the case of the connected automated vehicle (CAV), there is an important degree of user skepticism based on the awareness of the complexity and the risks of this technology. Public acceptance is a multi-faceted construct, tightly related to emotional processes and trust in a new technology, beyond the accomplishment of functional performance. However, the current approach based on the technology push threatens social viability of innovative technology like CAV, as it creates a gap between the well-thought technical reliability and public acceptance. The H2020 project SUaaVE (SUpporting acceptance of automated VEhicle) aims to make a change in the current situation of public acceptance of CAV. SUaaVE formulates a new concept called ALFRED, a human centered artificial intelligence to humanize the vehicle actions by understanding the emotions of the passengers of the CAV while also managing corrective actions in vehicle for enhancing trip experience. In line with this research, the H2020 project DIAMOND (Revealing fair and actionable knowledge from data to support women’s inclusion in transport systems) seeks to generate knowledge from data for more inclusive and efficient transport systems, being one of the main objectives to enhance the acceptance of women using and driving automated vehicles. This paper presents the main results obtained of two experimental tests carried out in each of the two projects. More than 50 subjects participated in each test, experiencing different scenarios of L4 automated vehicles in an immersive dynamic driving simulator. In both tests, the physiological response of the participants was measured (HR, EDA and facial EMG), considering other additional biometrics (breathing rate, temperature, sweating) and behavioral (facial expression, blinking, etc.) in the case of SUaaVE project. In case of DIAMOND, the experimentation was focused on estimating the participant emotional state, arousal and valence, by HR, EDA and facial EMG in autonomous driving scenarios. The goal was to explore the influence of gender and related intersectional variables in the emotional response that could lead to autonomous vehicle acceptance. The analysis of the test in SUaaVE has allowed a scientific advance defining an emotional model based on the contextual factors involving the experience in the Ego Car - The trip purpose (work travel, day shift, holidays, etc.) and the state of road (density of cars, weather conditions, safety envelop, etc.) – together with complete monitoring the passenger’s physiology and behavior. The approach presented will facilitate that automated vehicles are able to understand how we feel and use such information to make system more empathic, responding to the occupant emotions in real time. This will allow to OEMs and Tier 1 suppliers a detailed characterization of the passenger needs, enabling them the development of strategies to enhance the in-cabin experiences and, in case of DIAMOND project, include needs in women in CAV deployment strategies.

Mr. Nicolás Palomares, Instituto de Biomecánica de Valencia, Universitat Politècnica de València, València, SPAIN Dr. Juan Manuel Belda, Grupo de Tecnología Sanitaria del IBV, CIBER de Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), SPAIN Ms. Sofía Iranzo, Instituto de Biomecánica de Valencia, Universitat Politècnica de València, València, SPAIN Mr. Javier Silva, Instituto de Biomecánica de Valencia, Universitat Politècnica de València, València, SPAIN Dr. Eng. Begoña Mateo, Instituto de Biomecánica de Valencia, Universitat Politècnica de València, València, SPAIN Dr. Eng. José Laparra-Hernández, Instituto de Biomecánica de Valencia, Universitat Politècnica de València, València, SPAIN Dr. Eng. José Solaz, Instituto de Biomecánica de Valencia, Universitat Politècnica de València, València, SPAIN