Abstract
Due to the advent of E-commerce businesses, delivery vehicle traffic has grown by 71% over the last two decades. Hence, the automation of vans has received special attention to improve road safety. This study compares the performance of a trajectory tracking controller powered by three rival AI-based techniques namely, Particle Swarm, Genetic Algorithm and Ant Colony. Through scenario-based testing, all three techniques are compared from the tracking performance, algorithm complexity, accuracy and precision, and simplicity in finding the optimal weighting factors of the lateral position error and heading angle error which define the desired lateral controller output. Taken individual performance, ant colony powered fuzzy tracking controller was superior in terms of computational time, accuracy, and precision whereas, poor in ease of programming and explainability.