Autonomous driving has become a focus for many universities and automotive companies alike, aiming for public introduction within the next few years. However, this requires assurance that the inherent risk in critical systems such as localization is sufficiently low. In aviation, the concept of integrity was established for this purpose. It relies upon a localization system's ability to provide timely and correct alerts notifying when the system must not be used. For autonomous vehicles, alert generation is still a topic of research because the environment is more complex. This work contributes by identifying two alert generation approaches applicable to autonomous driving and comparing their conceptual merits and drawbacks. The focus lies on methods for fault detection and isolation as well as those based on a bounded-error assumption. Both methods addressed here are guaranteed to either detect out-of-tolerance errors or determine a valid solution. However, they differ in how risk is dealt with.