Project description DEENESFRITPL Innovative techniques for safe driving An improved understanding of drivers' profiles and driving pattern identification could enhance the safety of conventional drivers and human-mimic autonomous vehicles. Driving behaviour analytics rely mainly on the analysis of traffic accident data arising from human factors. The EU-funded RHAPSODY project will introduce a new approach to driving behaviour models by identifying unsafe and optimal driving behaviour. The project will analyse the dynamic evolution of driving behaviour on macro and microscopic levels through machine learning and artificial intelligence techniques applied to existing European naturalistic driving data. To recognise the benchmarks of optimal driving and investigate the conditions favouring best driving performance, RHAPSODY will identify different driver profiles, driving patterns, and their response to rapid changes under diverse conditions. Show the project objective Hide the project objective Objective Driving behaviour analytics is an emerging field with new potential for addressing the human factors that are persistently causing a huge burden of traffic injuries. However, there is need for new insights regarding driving profiles and patterns identification and a robust relevant methodology is lacking. The objective of RHAPSODY is to provide evidence for a shift of focus in driving behaviour models, targeting to identify not only the unsafe but also the optimal driving, through the analysis of the dynamic evolution of driving behaviour on both macro- and microscopic levels. Machine learning (ML) and artificial intelligence (AI) techniques will be applied on existing European naturalistic driving data to identify different driver profiles and driving patterns, their rapid changes under different conditions and their variability over individual drivers and populations. Ultimately, RHAPSODY will recognize the benchmarks of optimal driving and investigate the conditions under which drivers may demonstrate best performance. These can be applied for the improvement of safety of both conventional drivers and human-mimic autonomous vehicles (AVs).Hosted at Delft University of Technology, RHAPSODY will allow the Fellow to enhance his individual competences by acquiring new skills on transport safety analysis, AVs, human factors, data management, AI and ML, as well as on responsible innovation, impact creation and commercialization. RHAPSODY will thus strongly benefit his interdisciplinary expertise and ensure his high employability as a transportation R&D data scientist.A two-way transfer of knowledge is guaranteed since RHAPSODY combines his expertise in transportation data analysis with the host’s expertise in safety, human factors and responsible AI application. Therefore, RHAPSODY will contribute to Europe’s knowledge-based growth and societal benefit, through both its novel research outputs and the development of a highly skilled Fellow on transport safety. Fields of science natural sciencescomputer and information sciencesdata scienceengineering and technologymechanical engineeringvehicle engineeringautomotive engineeringautonomous vehiclesnatural sciencescomputer and information sciencesartificial intelligencemachine learningsocial sciencespsychologyergonomics Keywords Transport safety human factors naturalistic driving data machine learning artificial intelligence autonomous vehicles Programme(s) H2020-EU.1.3. - EXCELLENT SCIENCE - Marie Skłodowska-Curie Actions Main Programme H2020-EU.1.3.2. - Nurturing excellence by means of cross-border and cross-sector mobility Topic(s) MSCA-IF-2020 - Individual Fellowships Call for proposal H2020-MSCA-IF-2020 See other projects for this call Funding Scheme MSCA-IF - Marie Skłodowska-Curie Individual Fellowships (IF) Coordinator TECHNISCHE UNIVERSITEIT DELFT Net EU contribution € 175 572,48 Address STEVINWEG 1 2628 CN Delft Netherlands See on map Region West-Nederland Zuid-Holland Delft en Westland Activity type Higher or Secondary Education Establishments Links Contact the organisation Opens in new window Website Opens in new window Participation in EU R&I programmes Opens in new window HORIZON collaboration network Opens in new window Total cost € 175 572,48