<div>Vehicle-to-infrastructure (V2I) connectivity technology presents the opportunity
for vehicles to perform autonomous longitudinal control to navigate safely and
efficiently through sequences of V2I-enabled intersections, known as connected
corridors. Existing research has proposed several control systems to navigate
these corridors while minimizing energy consumption and travel time. This
article analyzes and compares the simulated performance of three different
autonomous navigation systems in connected corridors: a V2I-informed constant
acceleration kinematic controller (V2I-K), a V2I-informed model predictive
controller (V2I-MPC), and a V2I-informed reinforcement learning (V2I-RL) agent.
A rules-based controller that does not use V2I information is implemented to
simulate a human driver and is used as a baseline. The performance metrics
analyzed are net energy consumption, travel time, and root-mean-square (RMS)
acceleration. Two connected corridor scenarios are created to evaluate these
metrics, including one scenario reconstructed from real-world traffic signal
data. A sensitivity analysis is also performed to quantitatively identify key
parameters that have the highest impact on the three metrics of interest.</div>