  This work addresses the problem of autonomous traffic management at an
isolated intersection for connected and automated vehicles. We decompose the
trajectory of each vehicle into two phases: the provisional phase and the
coordinated phase. A vehicle, upon entering the region of interest, initially
operates in the provisional phase, during which the vehicle optimizes its
trajectory but is constrained from entering the intersection. Periodically, all
the vehicles in their provisional phase switch to their coordinated phase,
which is obtained by coordinated optimization of the sequence of the vehicles'
intersection usage as well as their trajectories. For the coordinated phase, we
propose a data driven solution, in which the intersection usage sequence is
obtained through a data-driven online classification and the trajectories are
computed sequentially. This approach also allows for the incorporation of macro
information such as traffic arrival rates into the solution. The overall
algorithm is provably safe and can be implemented in a distributed manner.
Finally, we compare the proposed algorithm against traditional methods of
intersection management and against some existing literature through
simulations. Through simulations, we also demonstrate that the computation time
per vehicle remains constant for the proposed algorithm over a wide range of
traffic arrival rates.
