As technology advances, automated systems are being considered for many safety applications within the aviation and automotive industries. Such applications include airport surface traffic management systems and automotive lane-keeping systems. Possibly misleading sensor data must be considered when designing these systems since they operate in the absence of a human controller. Currently, no integrity requirements (e.g. no sensor anomaly detection requirements) have been defined for these types of automated airport surface and automotive applications. This thesis both demonstrates an integrity risk assessment for an airport surface application and proposes an integrity monitor for an automated lane-keeping system. Within this work, airport surveillance sensors, such as radar, multilateration, and ADS-B, are evaluated and modeled, with a specific emphasis on the impact of their random noise and off-nominal performance characteristics on the system's integrity. Similarly, for automated lane-keeping, two image processing techniques - gradient detection and optical flow - are applied to perform a cross-check in order to verify measurement quality.