Description du projet
Des techniques innovantes pour une conduite sécurisée
Une meilleure compréhension des profils des conducteurs et de l’identification des modes de conduite pourrait améliorer la sécurité des conducteurs conventionnels et des véhicules autonomes imitant la conduite humaine. L’analyse du comportement de conduite repose principalement sur l’analyse des données d’accidents de la circulation résultant de facteurs humains. Le projet RHAPSODY, financé par l’UE, introduira une nouvelle approche des modèles de comportement de conduite en différenciant les comportements de conduite dangereux et optimaux. Le projet analysera l’évolution dynamique du comportement de conduite aux niveaux macro- et microscopique grâce à des techniques d’apprentissage automatique et d’intelligence artificielle appliquées aux données de conduite naturalistes européennes existantes. Pour reconnaître les références de la conduite optimale et étudier les conditions favorisant les meilleures performances de conduite, RHAPSODY identifiera différents profils de conducteur, modes de conduite et leur réponse aux changements rapides dans diverses conditions.
Objectif
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.
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MSCA-IF - Marie Skłodowska-Curie Individual Fellowships (IF)Coordinateur
2628 CN Delft
Pays-Bas