Driven by higher levels of driving automation, X-by-Wire (XbW) systems such as steer-by-wire and brake-by-wire are currently transitioning from long-standing research topics into viable series-production applications. By removing mechanical fallback layers, these systems necessitate high availability requirements. These are typically met through redundancies in power supply and communication, for which the high levels of automated driving, among other technology drivers, are setting the stage. The safety concepts for XbW systems are currently developed primarily within their respective domains. Whereas over-actuated vehicle architectures offer the potential for cross-domain synergies to compensate for failures. A high-level controller can orchestrate the remaining healthy actuators to enable fault-tolerance on vehicle-level. To evaluate these synergies and compare different actuator topologies at an early concept stage, a holistic and quantified development approach is required. The objective of the PhD thesis is to address this challenge. A simulation-based framework has been developed for the automated risk assessment of actuator failures in accordance with the ISO 26262 standard. To quantify the Automotive Safety Integrity Level (ASIL), the approach assesses the risk associated with trajectory deviations resulting from generic actuator failures. The assessment covers all actuator domains and is independent of the specific root cause, leading to the actuator failure under investigation. A central aspect of this assessment is the quantification of ‘controllability’, describing the share of drivers able to avoid harm in such a hazardous event. To quantify this, stochastic driver behavior models derived from real-world data are utilized to determine the driver’s response. This response is modeled as an open-loop ‘ballistic’ trajectory, which represents the desired evasion maneuver.

To evaluate the fault-tolerance capabilities of various actuator topologies and their ability to follow these desired evasion trajectories with an apparent actuator failure, the task is formulated as an Optimal Control Problem (OCP). A Model Predictive Controller (MPC) is implemented in the framework that aims to minimize trajectory tracking errors and actuator utilization over a receding prediction horizon. The controller follows an integrated approach, combining trajectory tracking and actuator control allocation to handle topology variations and failures within a single model-based approach. This allows for a Design Space Exploration (DSE) of cross-domain actuator topologies, enabling the objective evaluation of the fault-tolerance capabilities and respective necessary actuator performances in early concept assessments. The framework is demonstrated for a set of evasion trajectories, topologies and actuator failures.