Advancing Off-Road Autonomy: Comparative Analysis of Manual and AI-Driven Control in Autonomous Trucks

The deployment of autonomous trucks in off-road environments poses significant engineering challenges due to terrain variability and dynamic operating conditions. While recent advancements in perception, planning, and control architectures have improved vehicle autonomy, experimental validations comparing autonomous and manual control particularly regarding propulsion efficiency remain limited. This study addresses this gap by conducting structured field experiments to evaluate the performance of a heavy-duty truck operating in autonomous and manual modes. Tests were performed on a dedicated proving ground using a multi-sensor autonomous system. Key performance indicators included vehicle speed stability, engine speed regulation, and fuel consumption. The results show that autonomous driving achieved a 4.5% reduction in fuel consumption compared to manual operation. This gain is attributed to the system’s ability to maintain lower speed variance and more consistent engine behavior, especially during curved segments. These findings highlight the operational and energy efficiency advantages of autonomous control strategies in off-road logistics and support their broader adoption in agricultural and industrial applications.