The trend towards shorter supply chains and home delivery has rapidly increased delivery van traffic. Consequently, in the 20 years prior to 2018, delivery traffic has increased by 71%, while passenger vehicles have increased only by 13%. This drastic change in traffic patterns presented new challenges to decision makers and fortunately coincided with changes in the automotive industry, i.e., the advent of automation. However, the design of a controller is not straightforward due to the complex and nonlinear vehicle dynamics and the nonlinear relationship between the controller, tracking error and trajectory. This paper proposes a novel hybrid artificial-intelligence-based lateral control system for an autonomous delivery van to address these challenges to achieve the lowest value of tracking error. The strategy consists of multiple simultaneously operating fuzzy controllers. Their output signals are optimally weighted by a genetic algorithm to determine the proper allocation of control signals for calculating the final steering angle. Six different scenarios are implemented to evaluate the algorithm. A comparative analysis is then performed with two alternative state-of-the-art methods: (i) manually weighted and (ii) geometrically weighted controllers. During the tests, the vehicle's speed varied, and the roads considered ranged from simple roads to a series of curves. The results show that the proposed strategy leads to a reduction of up to 91.2% and 61.1% in tracking error compared to the manually and geometrically weighted alternatives, respectively.