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Building Acceptance and Trust in Autonomous Mobility

Periodic Reporting for period 1 - Trustonomy (Building Acceptance and Trust in Autonomous Mobility)

Reporting period: 2019-05-01 to 2020-10-31

Despite technological breakthroughs in connected and automated transport, the total transformation of existing transportation into a fully autonomous system is still decades away. In the meantime, mixed traffic environments with semi-autonomous vehicles proactively passing the dynamic driving task back to the human driver, whenever system limits are approached, is expected to become the norm. Such a Request to Intervene (RtI) can only be successful and met with trust by end-users if the driver state is continuously monitored and his/her availability properly evaluated and sufficiently triggered (through tailored human-machine interfaces - HMIs). In parallel, driver training has to evolve to account for the safe and sensible usage of semi-automated driving, whereas driver intervention performance has
to be made an integral part of both driver and technology assessment. Besides, the ethical implications of automated decision-making need to be properly assessed, giving rise to novel risk and liability analysis models. The vision of Trustonomy (a neologism from the combination of trust + autonomy) is to maximise the safety, trust and acceptance of automated vehicles by helping to address the aforementioned technical and non-technical challenges through a well-integrated and inter-disciplinary approach, bringing domain experts and ordinary citizens to work closely together. Trustonomy will investigate, setup, test and comparatively assess, in terms of performance, ethics, acceptability and trust, different relevant technologies and approaches, including driver state monitoring systems, HMI
designs, risk models, and driver training methods. This will be done through both simulator and field based studies, in a variety of autonomous driving and RtI scenarios, covering different types of users (in terms of age, gender, driving experience, etc.), road transport modes (private cars, trucks, buses), levels of automation (L3 - L5) and driving conditions.
The main results accomplished include:
• Achievement of a common vision for the successful investigation of Trustonomy research and innovation specific objectives, through the operational context definition, the elicitation of system and user requirements (functional and non-functional), the Trustonomy frameworks definition and a set of methodological guidelines.
• Definition of preliminary specifications for the overall Trustonomy architecture and the detailed and executive design of underlying frameworks.
• Delivery of first version of the frameworks prototypes, ready to be deployed and validated in the preliminary trials.
• Preliminary Trials specifications and preparatory activities
• Set up and consultation with the Trustonomy Advisory Board
• Implementation of the project dissemination strategy through targeted communication, webinars for Trustonomy Panel members and organization and participation at European mobility related events
• Clustering and relationships with other Cooperative, Connected and Automated Mobility projects and Research and Innovation projects
• Evaluation of project ethics compliance
• Design of a preliminary exploitation strategy according to project outcomes and their envisioned deployment in pilots
• Technological trends monitoring via the Innovation Management Observatory and participation to Standardization working groups
The main impacts from Trustonomy are:
• a methodological and technological framework for the operational assessment of different Driver State Monitoring (DSM) systems, enabling studies for the suitability of different DSM technologies.
• a framework for the operational assessment of various HMI designs, including visual, auditory, haptic, timing and content factors,to study their effectiveness in terms of ergonomics and influence on driver performance.
• an automated-decision-support framework covering risk assessment (precursors and forecasting models), to issue early Request to Intervene (RtI) warnings, and emergency trajectory generation to react to critical risks and RtI failures.
• novel driver training curricula for human drivers of ADS, according to emerging training needs in the non-fully autonomous era, based on risk mapping of CAD operation activities, addressing safe driver-vehicle interaction and making use of innovative, ICT-based driver training methods.
• a driver intervention performance assessment (DIPA) framework consisting in a set of widely acceptable methods comprising time-based intervention performance measures, as well as objective and subjective take-over quality measures.
• measurement of performance, trust and acceptance of human drivers of ADS, specifically investigating the mediating effects of the multi-dimensional aspects of trust on driver acceptance and engagement with autonomous vehicles, to assess innovative and safe ways of building, maintaining and regaining driver trust, focusing on driver intervention and transition of control scenarios.
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