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Artificial Intelligence och Intelligent Systems

SimuSafe : Simulator of Behavioural Aspects for Safer Transport

The purpose of the SIMUSAFE is to improve road safety by understanding the individual and collective behaviour of road users involved (drivers, two wheelers, pedestrians), their interaction between themselves and safety-related systems and services e.g. assess risk perception and decision making.

Project website

Start

2017-06-01

Planned completion

2021-05-30

Main financing

H2020, Vinnova

Collaboration partners

ITCL INSTITUTO TECNOLÓGICO DE CASTILLA Y LEÓN (the Coordinator), IBM ISRAEL SCIENCE & TECHNOLOGY LTD, INSTITUT FRANCAIS DES SCIENCES ET TECHNOLOGIES DES TRANSPORTS, DE, L’AMENAGEMENT ET DES RESEAUX, BRAINSIGNS SRL, EUROPEAN DRIVING SCHOOLS ASSOCIATION, ASSOCIAZIONE ITALIANA PROFESSIONISTI SICUREZZA STRADALE, SENSAIR AB, PROMETEO INNOVATIONS, Università Cattolica del Sacro Cuore (UCSC),TWENTE MEDICAL SYSTEMS INTERNATIONAL. B.V., COVENTRYUNIVERSITY, UNIVERITY OF PORTO, LINK INNOVA ENGINEERING, DELPHI, THE UNIVERSITY OF IOWA ON BEHALF OF ITSELF AND THE NATIONAL ADVANCED, DRIVING SIMULATOR

Project manager at MDH

Senior Lecturer

Mobyen Uddin Ahmed

+4621107369

mobyen.ahmed@mdh.se

It has been claimed that 90% of road-traffic crashes are caused by driver error, being unsafe behaviour a significant factor in traffic accidents. So, the purpose of the SIMUSAFE is to improve road safety by understanding the individual and collective behaviour of road users involved (drivers, two wheelers, pedestrians), their interaction between themselves and safety-related systems and services e.g. assess risk perception and decision making.

Goal of the project

The goal of the project is make use of state-of-art Simulation, Artificial Intelligence, Virtual Reality and Data Science methodologies to retrieve accurate actor e.g. road users and behavioural models in a transit environment, reproduce the same into controllable settings of Traffic Simulators and be able to determine cause, consequences on incidents of interest, to understand the underling behaviour and motivations of the involved actors.

MDH is leading one work-package WP: Data Analytics. The aim of the WP is to provided data analytics and metric computation functionality for different actor models such as car, pedestrian, two-wheeler. Neurometric indexes of risky attitudes and behaviours based on physiological parameters (HR/HRV, EMG, EEG; EOG, ECG and GSR) jointly with contextual information (e.g., Sleep duration/quality, Activity intensity, Weather, Noise) will comprise risk perception, awareness, attention and decision-making. The activities included in the project through three research cycles:

  • First, project partners will collect naturalistic driving, riding, and walking behaviours in uncontrolled environments for a baseline.
  • Second, we will collect behavioural and physiological responses under more controlled conditions to connect risk taking behaviour and cognition.
  • Third, SIMUSAFE will study the behaviours and responses of road users driving, riding, and walking under high-risk situations and impairment conditions.
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