  Autonomous intersection management (AIM) poses significant challenges due to
the intricate nature of real-world traffic scenarios and the need for a highly
expensive centralised server in charge of simultaneously controlling all the
vehicles. This study addresses such issues by proposing a novel distributed
approach to AIM utilizing multi-agent reinforcement learning (MARL). We show
that by leveraging the 3D surround view technology for advanced assistance
systems, autonomous vehicles can accurately navigate intersection scenarios
without needing any centralised controller. The contributions of this paper
thus include a MARL-based algorithm for the autonomous management of a 4-way
intersection and also the introduction of a new strategy called prioritised
scenario replay for improved training efficacy. We validate our approach as an
innovative alternative to conventional centralised AIM techniques, ensuring the
full reproducibility of our results. Specifically, experiments conducted in
virtual environments using the SMARTS platform highlight its superiority over
benchmarks across various metrics.
