Recognition of HumAn PatternS of Optimal Driving for safetY of conventional and autonomous vehicles

Project Information

RHAPSODY

Grant agreement ID: 101027222

DOI 10.3030/101027222

Project closed

EC signature date 26 March 2021

Start date 1 June 2021

End date 31 May 2023

Funded under

• EXCELLENT SCIENCE - Marie Skłodowska-Curie Actions

Total cost € 175 572,48

EU contribution € 175 572,48

Coordinated by

TECHNISCHE UNIVERSITEIT DELFT
Netherlands

Project description

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.

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 (EuroSciVoc)

CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: https://op.europa.eu/en/web/eu-vocabularies/euroscivoc.

• natural sciences › computer and information sciences › data science
• engineering and technology › mechanical engineering › vehicle engineering › automotive engineering › autonomous vehicles
• natural sciences › computer and information sciences › artificial intelligence › machine learning
• social sciences › psychology › ergonomics

You need to log in or register to use this function

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

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
• Activity type: Higher or Secondary Education Establishments
• Total cost: € 175 572,48