Minimum Risk Maneuver Strategy for Automated Driving System Under Multiple Conditions of Sensor Failure

To ensure the safety of vehicles and occupants under failures or functional limitations of ego vehicles, a minimum risk maneuver (MRM) has been proposed as a key automated driving system (ADS) function. However, executing an MRM may pose certain potential risks when sensor failures occur. This study proposed an MRM strategy designed to enhance highway-driving safety during MRM execution under multiple sensor-failure conditions. A hazard and operability study analysis, based on an ADS behavior model, is conducted to systematically identify hazards, determine potential hazardous events, and categorize the associated safety risks arising from sensor failures. Within the proposed strategy, virtual objects are generated to account for potential hazards and support risk assessments. Adaptive MRM behavior is determined in real time by analyzing surrounding objects and evaluating time-to-collision and time headway. The strategy is verified by using a MATLAB–CARLA co-simulation environment across three representative highway scenarios with combined sensor failures. The result demonstrates that the proposed MRM strategy can mitigate collision risk in hazardous scenarios while effectively leveraging the remaining functional sensors to guide the ego vehicle toward an appropriate minimum risk condition during MRM execution.