Trajectory planning is an essential issue for autonomous vehicles navigation. It represents a decision-making level that considers several constraints to be respected to navigate safely and comfortably in a dynamic environment. This paper presents a reactive trajectory planning, which consists to generates several candidate trajectories. Then, selecting the best trajectory among candidates is based on different criteria, each described by a cost function. Indeed, the algorithm aims to minimize a global cost function, a combination of several costs, to determine the best trajectory. The main objective of this work is to study the algorithm's sensitivity against parameter tuning and to find a generic range of weighting coefficients for the cost function of the planning algorithm to make the algorithm as reliable as possible against various driving conditions.