H2020Индивидуална стипендия2021–2023

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

„Хоризонт 2020“ — Действия „Мария Склодовска-Кюри“

Период: 2021-06-01 → 2023-05-31
Финансиране от ЕС: 175 572 €
Участници: 1
Схема: MSCA-IF

Линиите свързват координатора с партньорите.

Накратко на български

Поведението на шофьорите се анализира чрез профили и конкретни модели, като например промени в скоростта или сърцебиенето преди резки маневри. Това помага за разбирането на оптималното шофиране, за да се намалят катастрофите, които са основна причина за смъртност сред младите хора.

Този кратък обзор е генериран от изкуствен интелект

Кратко обяснение, генерирано от езиков модел по текста на CORDIS. Оригиналът е по-долу.

Резултати накратко

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

Road traffic injuries are a major public health problem in the WHO European Region and cause the premature death of over 80,000 people every year. They are the leading cause of death in young children and adults aged 5 to 29 years. In addition, about 2.7 million people are estimated to be seriously injured annually. These cause a substantial economic loss to society: up to 3% of the gross domestic product of any given country. Human factors are persistently the main cause of road crashes, with a percentage of 65%–95%. Therefore, it is crucial to understand them in-depth and suggest new approaches to shape safe driving behaviours. An important gap in existing research is the absence of a robust methodology for the identification of: a) macroscopic driver profiles, b) repetitive microscopic driving patterns in terms of speed or heart beats e.g. before harsh events or manoeuvers and c) optimal driving and its characteristics. In this context, the objective of RHAPSODY was to provide evidence for a shift of focus in driving behaviour models, from the unsafe to the optimal driving, through the analysis of the driving behaviour on both a macroscopic and microscopic level. The project shed light on the number of different driver profiles and driving patterns that exist and revealed how these patterns differentiate on an individual and driver population level. This research also provided insights on the relationship of these driver profiles and driving patterns with driver characteristics such as age, income and crash history data. Finally, RHAPSODY recognized the individual and driver population benchmarks of optimal driving, investigated the different optimal driving patterns that exist and explored what should be the individual recommendations to drivers to reach this optimal level.

Текст от CORDIS, на английски · Данни: CORDIS, © Европейски съюз

Цел на проекта

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.

Оригинален текст от CORDIS (на английски).

Участници

TECHNISCHE UNIVERSITEIT DELFT · DelftКоординаторНидерландия