Road 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 unsafe behaviour 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, nearcollision,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.
|First Name||Last Name||Title|
|Mobyen Uddin||Ahmed||Associate Professor|
|Mohammed Ghaith||Altarabichi||Doctoral student|
|Gunnar||Widforss||Project Manager,Senior Project Manager|
Supervised Learning for Road Junctions Identification using IMU (Mar 2019) Mohammed Ghaith Altarabichi, Mobyen Uddin Ahmed, Shahina Begum First International Conference on Advances in Signal Processing and Artificial Intelligence (ASPAI' 2019)
A Machine Learning Approach to Classify Pedestrians’ Event based on IMU and GPS (Jan 2019) Mobyen Uddin Ahmed, Staffan Brickman , Alexander Dengg , Niklas Fasth , Marko Mihajlovic , Jacob Norman International Conference on Modern Intelligent Systems Concepts (MISC'18)
Cloud-based Data Analytics on Human Factor Measurement to Improve Safer Transport (Nov 2017) Mobyen Uddin Ahmed, Shahina Begum, Carlos Alberto Catalina , Lior Limonad , Bertil Hök , Gianluca Di Flumeri 4th EAI International Conference on IoT Technologies for HealthCare (HealthyIoT'17)
Distributed Multivariate Physiological Signal Analytics for Drivers’ Mental State Monitoring (Oct 2017) Shaibal Barua, Mobyen Uddin Ahmed, Shahina Begum 4th EAI International Conference on IoT Technologies for HealthCare (HealthyIoT'17)
Scalable Framework for Distributed Case-based Reasoning for Big data analytics (Oct 2017) Shaibal Barua, Shahina Begum, Mobyen Uddin Ahmed 4th EAI International Conference on IoT Technologies for HealthCare (HealthyIoT'17)
|Hök instrument AB||Industrial|