A GoA4 Control Architecture for the Autonomous Driving of High-Speed Trains Over ETCS: Design and Experimental Validation

This work deals with the design of a control system enabling the autonomous driving of Grade of Automation 4 (GoA4) for high-speed trains over ETCS. GoA4 requires trains to autonomously adapt their behaviour to the different driving conditions occurring in real-world trips and involving a wide range of possible maneuvers, even in the presence of unexpected events forcing ETCS interventions. To ensure this high automation level, we propose a hierarchical, modular and scalable control architecture, named Autonomous Driving Function (ADF). It embeds the main ATO functionalities, i.e., optimization of the recommended speed profile and train speed tracking, both nontrivial tasks due to train nonlinear dynamics and complex external environment, while being still compliant with railway standards. Hence, ADF is devoted to the real-time trajectory planning, considering the actual data acquired and the current driving situation along with the related on-line constraints, and to the trajectory tracking which, realized via different classes of controllers, ensures a safe and efficient train motion, also compliant with the railway standard. ADF is designed according to the Model Based Control Design (MBCD) approach which fully covers the V-Cycle development process and supports automatic C code generation compliant to standard EN50128, early design validation, testing, simulation and run-time verification. Finally, thanks to the experimental validation, carried out on an inspection high speed prototype vehicle, ADF has the prospect of becoming an inherent architecture for guaranteeing the GoA4 autonomous diving for HST over ETCS.