About Us

Project Vision

Road transport is known to be the most dangerous of all transport modes and poses a major societal challenge for the EU. According to the European Commission (ec.europa.eu), road crashes cause almost 30,000 fatalities and more than 100,000 serious injuries a year in the EU. In 2015, there were more than 26,000 road fatalities in the EU

  • 22% pedestrians
  • 8% bicycle riders
  • 15% motorcycle riders
  • 3% moped (and similar powered two-wheelers) riders
  • 52% motor vehicle occupants

Road crashes also cause tens of thousands of injuries annually ranging from crippling/devastating to minor. It is estimated that associated costs are at least €100 billion a year.[1]

[1]

It has been claimed that 90% of road-traffic crashes are caused by driver error with risky behaviour being a significant factor in traffic collisions. Improving road safety means understanding the individual and collective behaviour of all the actors involved (drivers, two-wheelers, pedestrians) and the interactions between themselves, the transport-related systems, and the infrastructure. Therefore, SIMUSAFE will focus on the most at-risk transportation situations by looking at dangerous road designs as well as the altered driving conditions that frequently impair road users.

Process

In order to build more realistic driving simulators and simulation models, SIMUSAFE will collect and integrate multiple sources of road user data in three research cycles:

  1. First, project partners will collect naturalistic driving, riding, and walking behaviours in uncontrolled environments for a baseline.
  2. Second, we will collect behavioural and physiological responses under more controlled conditions to connect risk taking behaviour and cognition.
  3. Third, SIMUSAFE will study the behaviours and responses of road users driving, riding, and walking under high-risk situations and impairment conditions.
The above data-collection phases will be refined, correlated, and used to create more realistic multi-actor simulation models.

OBJECTIVES

Model Development and Data Collection

  • An Actor Model of each type (car, pedestrian, two-wheeler) integrating neurometrics and aggregated vehicular/environmental data from naturalistic driving and simulators for identification and representation of driving patterns and computation of risk metrics.
  • 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.
  • Integrated Data Collection Module for the filtering of raw data signals and Actor Model descriptor computation with connectivity to cloud-based infrastructure.
  • A quantified risk-taking and risk potential metric for biometric/vehicle data based on the multi-agent model and its equivalent for a simulated virtual driver.
  • Identification of ADCs (risk-perception, awareness) and quantized risk assessment for each class of actor, accordingly with measured data and possible interactions with others actors/environment and its own conditions. Module for observed data incorporation from biometric/vehicle sensors into the simulated agent parameters, such that the behaviour can be reproduced in simulation environment in large-scale.

Accurate Road User Simulation and Integration with Naturalistic Driving Tests

  • Realistic MAS models for driving and traffic simulators able to represent pedestrians, two-wheelers and standard car in traffic context as a dynamic system.
  • Modular API for automotive, two-wheelers or pedestrian simulators, and other actor classes integration.
  • Distributed server-client infrastructure for multi-driver / multi model simulation and DAI based simulation of the various entities of the traffic environment.
  • Modular API for the integration of biometric and vehicle sensors into a simulation module.
  • An in-built automated data analysis module based in Multi-Scale Entropy Analysis (MMSE) to determine relevant descriptors among the data flow produced by the (real/simulated) sensors of the platforms.
  • A methodology for raw data correlation between simulators and naturalistic driving tests based on PCA.
  • A module for data incorporation of measured Actor Model into the Simulated Agent-Based Model such that the behaviour can be reproduced in the simulation environment in large-scale.
  • Analysis and test tool which will reproduce standard test scenarios such as NHTSA pre-crash scenarios and real-world scenarios.