Periodic Reporting for period 1 - DriveToTheFuture (Needs, wants and behaviour of 'Drivers' and automated vehicle users today and into the future)
Reporting period: 2019-05-01 to 2020-10-31
Transport automation is a reality; it is now a matter of how to make the best of it. User awareness, acceptance and training formulate priority challenges. Questions on vehicle taking over control from humans, change of mobility habits and experience, cost of future commuting and travelling, ethical decisions of a machine vs. a human, the need of new driver training incentives for adapting to the technological evolution in future vehicles, require research-based answers. Drive2theFuture develops training, HMI concepts, incentives policies and other cost efficient measures to promote and comparatively assess alternative connected, shared and automated transport use cases for all transport modes and types of users (drivers, travelers, pilots, VRUs, fleet operators and other stakeholders), in order to understand, simulate, regulate and optimize their sustainable market introduction; including societal awareness creation, acceptance enhancement and training on use. Its mission is to prepare future “drivers”, travelers and vehicle operators to accept and use connected, cooperative and automated transport modes and the industry of these technologies to understand and meet their needs and wants.
Work performed from the beginning of the project to the end of the period covered by the report and main results achieved so far
All project objectives for the reporting period are met. A kick-off, 4 plenary, 2 cluster, 1 Advisory Board meetings, and 1 project workshop, were realized. Project management (WP9) ensured the smooth progress of the project. 21 Deliverables were submitted, upon internal peer review processes. WP1 identified “driver”, traveler and stakeholder needs & wants by clustering users and setting up a compendium of common terminology (120 terms; a new definition of VRUs for vehicle automation), along with an online voice-of-customers survey (in 18 languages; 11500 answers from 30 countries), the identification of 50 acceptance risks, 43 research priorities for all modes, 37 solutions analyzed for their transferability between modes and a taxonomy of knowledge & skills required to efficiently operate AVs per mode. 12 use cases were defined, considering modes and types of vehicles, the respective users and the tools to be tested. In WP2, an inclusive database was set, analyzing 22 projects datasets on autonomous technologies, functions, services and systems, while data analytics & fusion frameworks were developed. A complete simulation platform was designed for modelling AV behavior and public acceptance, along with a 2-layer microsimulation model mimicking AV behavior. A Neural Network model Architecture was developed and used for AV sentiment level extraction of almost 40000 posts from social media. In WP3, review of 40 HMI concepts, in different vehicle types, transport modes & user clusters, led to extracting HMI good practices. A framework and guidelines for affective, persuasive, personalized and trusted HMI were defined, along with HMI concepts for the interaction between AVs and non-automated traffic participants. A wearable sensor for stress detection and a facial recognition algorithm for user’s emotional state were selected and WP1 use cases were analyzed, regarding relevant parameters for personalization and adaptation features. In WP4, AV users’ training needs were analyzed for all transport modes and AV levels, for the selection of VR/AR and multimedia tools and development of application scenarios. The pilots’ training needs, existing training programs and material were identified, towards the development of training curricula. An e-learning tool was designed, to include material for different user clusters and modes. In WP5, pilot plans, evaluation framework and tools were defined for all 12 pilot sites. Phase I testing was performed, while Phase II activities are ongoing. In WP6, an impact assessment framework is proposed with subjective and objective measurements (upon prioritizing impact areas and relevant KPIs) while business schemes and incentives of combined automation with MaaS were studied, for identifying best practices. WP7 investigated ethical, sociocultural, gender, safety, security and legal issues affecting user acceptance, through review and analysis of 60 literature sources and the conduction of 20 interviews with experts. WP8 created the project dissemination material and tools (poster, roll up, leaflet, newsletter, website, social media, user forum). A significant number of news, publications and representation in events occurred and a body of AV Ambassadors is under creation. General business model principles and initial project exploitation plans were defined. In WP10 an Ethics Board and ethics requirements were established to monitor ethical issues across the project, updated and continuously monitored in WP9 (where the Data Management Plan was also issued). GRPD related processes & roles in the Consortium, ethics data protection and import/export considerations were defined.
Progress beyond the state of the art and expected potential impact (including the socio-economic impact and the wider societal implications of the project so far)
Drive2theFuture approaches AVs user awareness and acceptance issues in a holistic and user-oriented manner, addressing all user categories and modes of transport, their needs and different characteristics. Going beyond the state-of-the-art, it captures the feelings and attitudes of the different user groups (through the voice-of-customer survey, acceptance risk assessment and sentiment analysis) towards AVs, also considering the expected transformation of each group's role. The development of an AV driver behavioral model (for passenger cars -with transferability potential to other modes) aiming to ensure the maintenance (if not enhancement) of the safety level when the system takes control, and its integration in an AV developer’s simulation suite (incorporating big data management, modelling & prediction tools, wearables detecting user reaction to AV functions, simulation platform), shall bring a valuable tool for developers and evaluators of existing and new AV functions & HMI, to deliver safe, effective and acceptable products, through a user-centric approach. HMI is a crucial part for user acceptance; thus effort is also put in the adaptation and further improvement of good practices through iterative testing with real users, using VR demonstrators as development and training tools, towards an educated expectation of automated transport to the public. Training is in the core of the project activities, investigating the training needs and suggesting programs and curricula for all user clusters and modes, with multiple means. Special focus is put on the future workforce, identifying the new knowledge and skills required for increasing competences and competitiveness in the continuously evolving working environment in transportation. A maximizing factor for the benefits of AVs is their link to other transportation trends, such as MaaS; relevant best cases, business models, stakeholder rules and incentive strategies are investigated, to achieve a common deployment and convergence towards a Connected Automated Vehicles Shared Mobility. Policy schemes, measures and incentives are tackled in the broader concept of AVs promotion, linked to appropriate business models. Ultimately, the progress of technological achievements paired with this of users’ and stakeholders’ awareness and the customization with the new era, shall guide the creation of an Automation User Acceptance Creation Roadmap, determining the steps for automation deployment towards achieving maximum acceptance, covering all transportation modes with common and differentiated milestones per mode.