The Scenario-Based Model Predictive Control (SBMPC) is an autonomous collision avoidance algorithm primarily designed for open and coastal waters. Over the years, the algorithm has shown significant promise in both simulations and real-world experiments. However, one of the challenges in adapting SB-MPC for autonomous inland waterway collision avoidance is the inherent resolution insufficiency due to deriving solutions from a finite set of discretized solutions using exhaustive search. To increase the resolution of the solution space, a derivative-based optimization strategy would be required. However, the non-smooth components of the SB-MPC cost function prohibit this approach. Therefore, we propose a newer variant of the algorithm, Smooth Scenario-Based Model Predictive Control (Smooth-SBMPC), specifically designed for the highly constrained and complex navigational environments inherent to inland waterways. It utilizes different techniques, such as using Fuzzy Logic, to convert the non-smooth components of the original SB-MPC cost function into smooth, derivable ones. The effectiveness of Smooth-SBMPC is validated through a comprehensive simulation study, offering insights into its performance in complex navigational environments.