<div>Parking an articulated vehicle is a challenging task that requires skill,
experience, and visibility from the driver. An automatic parking system for
articulated vehicles can make this task easier and more efficient. This article
proposes a novel method that finds an optimal path and controls the vehicle with
an innovative method while considering its kinematics and environmental
constraints and attempts to mathematically explain the behavior of a driver who
can perform a complex scenario, called the articulated vehicle park maneuver,
without falling into the jackknifing phenomena. In other words, the proposed
method models how drivers park articulated vehicles in difficult situations,
using different sub-scenarios and mathematical models. It also uses soft
computing methods: the ANFIS-FCM, because this method has proven to be a
powerful tool for managing uncertain and incomplete data in learning and
inference tasks, such as learning from simulations, handling uncertainty, and
capturing expert parking expertise. The results obtained from the proposed
method show that the use of a soft computation method significantly reduces the
cumulative errors: errors resulting from summing up each sub-maneuver. Of
course, the main source of these errors is related to starting from the random
point that exists at the beginning of the predefined complex scenario. This
implies that our method can effectively handle the uncertainty and variability
of parking scenarios.</div>