
SENSEAIR ALCOHOL SENSING AB
SENSEAIR ALCOHOL SENSING AB
1 Projects, page 1 of 1
Open Access Mandate for Publications assignment_turned_in Project2017 - 2021Partners:ITCL, PROMETEO, SENSEAIR ALCOHOL SENSING AB, SenseAir (Sweden), BRAINSIGNS SRL +29 partnersITCL,PROMETEO,SENSEAIR ALCOHOL SENSING AB,SenseAir (Sweden),BRAINSIGNS SRL,UCSC,UNIVERSITE GUSTAVE EIFFEL,BRAINSIGNS SRL,UI,UNIVERSITE GUSTAVE EIFFEL,AIPSS,SenseAir (Sweden),SENSEAIR ALCOHOL SENSING AB,TMSi,IBM ISRAEL,ITCL,IFSTTAR,IBM ISRAEL,PROGRES123,DELPHI DE,MDH,LINK INNOVA ENGINEERING SL,U.PORTO,Coventry University,Coventry University,EUROPEAN DRIVING SCHOOLS ASSOCIATION, EUROPAISCHE FAHRLEHRER ASSOZIATION E.V, FEDERATION EUROPEENNE,PROGRES123,AIPSS,TMSi,EUROPEAN DRIVING SCHOOLS ASSOCIATION, EUROPAISCHE FAHRLEHRER ASSOZIATION E.V, FEDERATION EUROPEENNE,PROMETEO,DELPHI DE,LINK INNOVA ENGINEERING SL,UIFunder: European Commission Project Code: 723386Overall Budget: 8,739,480 EURFunder Contribution: 7,991,600 EURRoad transport is known to be the most dangerous of all transport modes and poses a major societal challenge for EU. It has been claimed that 90% of road-traffic crashes are caused by driver error, being a significant factor in traffic accidents. Improving road safety means understanding the individual and collective behaviour of actors involved (drivers, two wheelers, pedestrians) and their interaction between themselves and safety-related systems and services. The goal of SIMUSAFE (SIMUlator of Behavioural Aspects for SAFEr Transport) following the FESTA-V model methodology is to develop realistic multi-agent behavioural models in a transit environment where researchers will be able to monitor and introduce changes in every aspect, gathering data not available in real world conditions. Driving simulators of several vehicles (cars, motorcycles, bicycles) and Virtual Reality (for pedestrians) will be used to simulate test environments. This will also enable the evaluation of scenarios which are not possible even with naturalistic driving (dangerous conditions, multiple monitored actors in the same scene, under influence of substances). Data collected from simulations will be correlated with naturalistic driving tests, such that the simulation and model aspects are the closest possible to real world data. From the developed model and collected data, impacting factors causing an event (crash, near-collision, infractions) from the environment and road users will be identified and quantified. Such knowledge will be the base for the development of more effective and pro-active measures for the prevention and mitigation of such factors, with subsequent impact in the safety devices market, regulations and driver education.
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