Descrizione del progetto
Tecniche innovative per una guida sicura
Una migliore comprensione dei profili dei conducenti e dell’identificazione dei modelli di guida potrebbe rafforzare la sicurezza dei conducenti convenzionali e dei veicoli autonomi che imitano l’uomo. L’analisi del comportamento di guida si basa principalmente sull’analisi dei dati sugli incidenti stradali causati dall’uomo. Il progetto RHAPSODY, finanziato dall’UE, introdurrà un nuovo approccio ai modelli di comportamento alla guida identificando comportamenti di guida non sicuri e ottimali. Il progetto analizzerà l’evoluzione dinamica del comportamento di guida a livello macro e microscopico attraverso tecniche di apprendimento automatico e intelligenza artificiale applicate ai dati di guida naturalistici europei esistenti. Per riconoscere i parametri di riferimento della guida ottimale e studiare le condizioni che favoriscono le migliori prestazioni di guida, RHAPSODY identificherà diversi profili di guida, modelli di guida e la loro risposta a rapidi cambiamenti in condizioni diverse.
Obiettivo
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)Coordinatore
2628 CN Delft
Paesi Bassi