The neurobiological principles underlying parallel-hierarchical networks, inspired by brain-like information processing, enable efficient and adaptive pattern recognition, optimizing real-time decision-making for autonomous vehicles and logistics systems. This enhances route planning, obstacle detection, and traffic management, reducing fuel consumption and emissions, which aligns with environmental sustainability goals. The innovation lies in leveraging bio-inspired algorithms to improve computational efficiency and accuracy in processing complex data from sensors (e.g., lidars, cameras), offering a cutting-edge approach to eco-friendly solutions in the field of rail transport.