The consequences of this ongoing revolution are challenging to predict and largely unknown. While the abundance of previous studies proves the positive potential of AI in urban mobility (from autonomous vehicles via optimal routing up to fleet management), the negative impact is overlooked. Conversely, our scenario of interest is the machine-dominated urban mobility system, where (collective) decisions of machine intelligence improve system-wide performance, yet at the cost of humans, now facing e.g. longer travel times, greater monetary costs or being nudged to change natural travel habits into the optimal ones - desired by the machine-centred system.

Such scenarios, however, need to be discovered. To this end, COeXISTENCE embarks on the interdisciplinary expedition inside the virtual environment of urban mobility, where machines and humans play the game for limited resources. In the four pre-identified games I will explore the conflict scenarios, demonstrate them on reproducible case-studies, quantify with proposed measures and finally mitigate with a proposed multi-objective reinforcement learning framework, where machines learn to mitigate conflicts while simultaneously reaching their inherently selfish objectives.

Reaching the projects' objectives will be ground-breaking when new phenomena are discovered and lead to breakthrough when they are mitigated pushing the system towards the synergy of COeXISTENCE.