In order to benefit from the potential of automated driving, the systems have to be used appropriately by drivers. Usage is in turn dependent on trust, which is, among other things, influenced by system knowledge and experience. One way of conveying system knowledge is by presenting feedback of limitations and constraints. The present study investigates the influence of specific as opposed to non-specific feedback on the evolution of trust with an intelligent parking assistant (IPA). The research focused on the question how the trust development can be described in a level 2 automation system and how it differs between feedback specificity levels. Therefore, 22 participants took part in an on-road longitudinal study, in which they performed a total of forty parking maneuvers while using the IPA. During the first twenty parking maneuvers no specific feedback was presented in case of a fault due to system limitations, whereas during the second twenty parking maneuvers specific feedback corresponding to situational factors and system constraints was given. The results show that non-specific feedback does not lead to an increase of trust at all. The errors that occur due to system and sensor limitations remain inexplicable and thus unpredictable. On the other hand, specific feedback that corresponds to the situation leads to the formation and stabilization of trust in accordance with the power law of learning. Therefore, it is advisable that users ideally receive specific feedback in order to gain knowledge of a system's limitations. This promotes the development of trust and ensures appropriate system use.