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MICA - Modelling Interaction between Cyclists and Automobiles

Reference number
Coordinator CHALMERS TEKNISKA HÖGSKOLA AKTIEBOLAG - Fordonssäkerhet
Funding from Vinnova SEK 5 100 000
Project duration March 2018 - May 2020
Status Completed
End-of-project report 2017-05522engelska.pdf (pdf, 795 kB)

Important results from the project

MICA modelled driver behaviour in the approaching phase of an overtaking manoeuvre, when a driver approached a cyclist from behind while facing oncoming traffic. The model predicts the probability for drivers to brake or steer as they approach the cyclists to perform an accelerative or flying manoeuvre. This model has been integrated into a smart collision-avoidance system, that provides early (and yet acceptable) warnings and interventions. A virtual assessment estimated the safety benefits of the smart collision-avoidance system using UDRIVE naturalistic data.

Expected long term effects

MICA delivered 1)a dataset collected on test-track with two robots, 2)a modelling framework , 3)a novel driver model 4)a smart collision-avoidance system which uses the model to generate warnings and automated interventions, and 5)a safety benefit analysis, proving that the new FCW alone may reduce fatalities by 43-93% and injuries by 53-96%. . The TRL of the smart collision-avoidance system was 1-2 before MICA and is now 4. Nine scientific publications describe this project results: one licentiate thesis, three conference contributions, and five journal papers.

Approach and implementation

MICA successfully fulfilled its goals by developing a driver model, integrating it in a collision-avoidance system, and estimating the safety benefit of the system with simulations. The initial project set up, involving three PhD students, and planning from the beginning for an expansion of the project, proved to be a successful strategy for our research to reach the critical mass and have a real impact.

External links

The project description has been provided by the project members themselves and the text has not been looked at by our editors.

Last updated 7 October 2020

Reference number 2017-05522