Soft Computing-Based Driver Modeling for Automatic Parking of Articulated Heavy Vehicles

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.