  The properties of cooperative driving strategies for planning and controlling
Connected and Automated Vehicles (CAVs) at intersections range from some that
achieve highly efficient coordination performance to others whose
implementation is computationally fast. This paper comprehensively compares the
performance of four representative strategies in terms of travel time, energy
consumption, computation time, and fairness under different conditions,
including the geometric configuration of intersections, asymmetry in traffic
arrival rates, and the relative magnitude of these rates. Our simulation-based
study has led to the following conclusions: 1) the Monte Carlo Tree Search
(MCTS)-based strategy achieves the best traffic efficiency, whereas the Dynamic
Resequencing (DR)-based strategy is energy-optimal; both strategies perform
well in all metrics of interest. If the computation budget is adequate, the
MCTS strategy is recommended; otherwise, the DR strategy is preferable; 2) An
asymmetric intersection has a noticeable impact on the strategies, whereas the
influence of the arrival rates can be neglected. When the geometric shape is
asymmetrical, the modified First-In-First-Out (FIFO) strategy significantly
outperforms the FIFO strategy and works well when the traffic demand is
moderate, but their performances are similar in other situations; and 3)
Improving traffic efficiency sometimes comes at the cost of fairness, but the
DR and MCTS strategies can be adjusted to realize a better trade-off between
various performance metrics by appropriately designing their objective
functions.
