Fault injection method for safety and controllability evaluation of automated driving

Advanced Driver Assistance Systems (ADAS) and automated vehicle applications based on embedded sensors have become a reality today. As road vehicles increase its autonomy and the driver shares his role in the control loop, novel challenges on their dependability assessment arise. One key issue is that the notion of controllability becomes more complex when validating the robustness of the automated vehicle in the presence of faults. This paper presents a simulation-based fault injection approach aimed at finding acceptable controllability properties for the model-based design of control systems. We focus on determining the best fault models inserting exceptional conditions to accelerate the identification of specific areas for testing. In our work we performed fault injection method to find the most appropriate safety concepts, controllability properties and fault handling strategies at early design phases of lateral control functions based on the error in the Differential GPS signal.Authors wants to thank to the H2020 UnCoVerCPS Project (with grant number 643921) and the ECSEL JU AMASS project under H2020 grant agreement No 692474 and from MINETUR (Spain)

Design-Time Safety Assessment of Robotic Systems Using Fault Injection Simulation in a Model-Driven Approach

International audienceThe rapid advancement of autonomy in robotic systems together with the increasing interaction with humans in shared workspaces (e.g. collaborative robots), raises pressing concerns about system safety. In recent years, the need of modeldriven approaches for safety analysis during the design stage has gained a lot of attention. In this context, simulation-based fault injection combined with a virtual robot is a promising practice to complement traditional safety analysis. Fault injection is used to identify the potential safety hazard scenarios and to evaluate the controller's robustness to certain faults. Besides, it enables a quantitative assessment w.r.t. other techniques that only give qualitative hints, such as FMEA. Thus, it facilitates the refinement of safety requirements and the conception of concrete mitigation actions. This paper presents a tool-supported approach that leverages models and simulation-assisted fault injection to assess safety and reliability of robotic systems in the early phases of design. The feasibility of this method is demonstrated by applying it to the design of a real-time cartesian impedance control system in torque mode as a use case scenario