SUaaVE Project


    While the deployment of connected automated vehicle (CAV) turns into reality, its acceptance has been called into question. Societal issues regarding public acceptance, user awareness and ethics, therefore, become priority concerns. The approach based on the technology push, jeopardizes social viability of innovative technology like CAV, as it creates a gap between the well-thought technical reliability and public acceptance.

    SUaaVE (SUpporting acceptance of automated VEhicle) will solve this gap by leaning on a Human-Driven Design (HDD) approach, enhancing synergies social science, human factors research and automotive market by means of an iterative process of assessment, co-design and prototyping. Participatory process will involve above 4.000 users (passengers, traditional and future drivers, VRU) and 100 experts and stakeholders along the project.

    The main outcomes will be:

    • A new paradigm of automation: ALFRED -Automation Level Four+ Reliable Empathic Driver-, that will “colour the decision-making processes of the CAV with human emotions” with: (a) EMpathY Unit to understand the emotional and cognitive state of the passenger, while taking into account society ethical principles; and (b) an Adaptive, Cognitive and Emotional Interface with a set of services (vehicle dynamics, ambient and postural comfort) to enjoy the ride and enhance passenger experience.
    • An immersive Virtual Human Centred Design (V-HCD) platform, allowing the simulation of CAV focused on Human factors to assess their acceptance and speed up market deployment.
    • Guidelines to support Public Authorities, representing a breakthrough in the public acceptance of future CAVs for both the society and, in particular, for all road users.

    SUaaVE partners will exploit ALFRED and V-HCD platform. Moreover, SUaaVE will contribute to generate new business models based on exploitation of personal data where the user is the owner and decide how to exploit it, and new adaptive services to user status in real time.


    This project has received funding from the European Union’s Horizon 2020 Research and Innovation Programme under Grant Agreement number 814999.

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