Multi-disciplinary research for a Safe Transition to automated transport in the cities of tomorrow (MuST)

Research Project, 2020

The Automated Driving Safety (ADS) project, sponsored by Chalmers Area of Advance Transport,  contributed to the birth of a new Marie Curie Innovative Training Network project, SHAPE-IT (15 PhD students), which scales up the ambitions and challenges for cross-disciplinary research within our network. SHAPE-IT promises to make automation safer but it may fail if the network across Chalmers and GU does not properly consider its multi-disciplinary aspects. MuST’s main purpose is to create a platform for senior researchers to share their knowledge and develop a strategy for multidisciplinary research solutions. This strategy may guide SHAPE-IT, and also align it with the ongoing activities and projects coupled to AoA, SAFER, and CHAIR, and within the WASP program. To have a real impact on transportation by facilitating a safe transition to automated transport in the cities of the future, we need to identify synergies and opportunities across research fields, become aware of multi-disciplinary problems and create multi-disciplinary solutions. This is what MuST is about.

This project will identify, analyze, and partially address some critical research questions relating to a smooth transition to a safe transport system of the future that are best answered within a multi-disciplinary framework. Four departments at Chalmers (M2, CSE, E2, and MV) join this project. M2 and CSE are also part of SHAPE-IT. The current (revised; due to budget reductions in the application granting phase) scope of the project specifically focuses on the interactions between researchers on statistical methods for facilitate the assessment of automated vehicles, as well as interactions on the topic of challenges related to the use of C2X communication in traffic safety solutions. In addition, we aim to include a few focus-group-like meetings with experts across involved departments, on how we better can use our multi-disciplinary research strengths to solve research problems related to automated vehicles, so that the transition to automated transport can be as safe as possible.