In Russia, agriculture is one of the key industries, so the development of innovative solutions in this area is of great importance. A promising approach is to automate field operations using highly automated manufacturing. These vehicles provide many benefits, including increased productivity, accurate information delivery, resource optimization, improved worker safety, flexibility, scalability, addressing labor shortages and relative technological innovation. The purpose of this work is to develop software for controlling unmanned vehicles and effectively managing the field processing devices installed on them. Testing algorithms on your own cars is ineffective due to high resource consumption and reduced comfort. Therefore, a highly automated vehicle simulator was used to debug the algorithm. The simulator offers various features such as route generation using Reeds-Scheppe curves, multiple path identification algorithms, visualization of landscape impacts, metric graphing capabilities, and robust compatibility with real vehicles. This paper proposes the development and implementation of these tools, considering their capabilities and benefits for promoting automation in agriculture.