Dive right into the 31 public deliverables of the i-DREAMS project by following up on this interview series. Our researchers shed their light on the relevance and added value of a specific deliverable.
Deliverable 2.1 reviewed and assessed state-of-the art approaches and methods to monitor the driver’s mental state and contextual factors of the driving environment that impact task demand. In addition, a selection of driver trait factors (including measurement methods) were summarized and driver behaviour indicators were reviewed.
Learn all about deliverable 2.1 by reading the interview with Susanne Kaiser from project partner KFV.
“The diagnostic power of an intervention system that is dynamic and that is based on the driver’s state and environment information, is what makes i-DREAMS unique”.
Susanne Kaiser – KFV
Deliverable 2.2 aims at reviewing vehicle technologies and applications for safety interventions associated with risk prevention and mitigation. This report provides technologies for safety interventions and selects the criteria of the most appropriate techniques and challenges. Furthermore, the assessment of their effectiveness, reliability an acceptance is also discussed, taking into account cross-modal considerations and differences between professional and non-professional drivers.
Learn all about deliverable 2.2 by reading the interview with Christos Katrakazas from project partner NTUA.
“So, the biggest challenge for us is to find a balance between maximizing effectiveness, and keeping acceptance, usability and driver satisfaction at high levels during and after the trip.”
Christos Katrakazas – NTUAD2.2-Interview_V02_26052021_FINAL_EN-1
Deliverable 3.1 contains a framework for operational design of experimental work in i-DREAMS. Learn all about it by reading this interview. Learn all about it by reading this interview with author Rachel Talbot from project partner Loughborough University.
“It is unlikely that a ‘one size fits all’ approach will be appropriate when designing the i-DREAMS platform. We will have to optimize for each transport mode considered”.
Rachel Talbot – Loughborough UniversityD3.1-Interview_FINAL_EN