Descripción del proyecto
Técnicas innovadoras para una conducción segura
Una mejor comprensión de los perfiles de los conductores y la identificación de los patrones de conducción podrían mejorar la seguridad de los conductores convencionales y de los vehículos autónomos que imitan a los humanos. El análisis del comportamiento al volante se basa principalmente en el análisis de los datos de los accidentes de tráfico derivados de factores humanos. El proyecto RHAPSODY, financiado con fondos europeos, introducirá un nuevo enfoque en los modelos de comportamiento al volante que identificará las conductas inseguras y las óptimas. El proyecto analizará la evolución dinámica del comportamiento al volante a escala macro y microscópica mediante técnicas de aprendizaje automático e inteligencia artificial aplicadas a los datos existentes sobre conducción natural en Europa. A fin de reconocer los puntos de referencia de la conducción óptima e investigar las condiciones que favorecen una mejor conducción, RHAPSODY identificará diferentes perfiles de conductores, patrones de conducción y su reacción a los cambios rápidos en diversas condiciones.
Objetivo
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)Coordinador
2628 CN Delft
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