As fully autonomous vehicles come into fruition, the role of the driver will transition from controlling the vehicle to monitoring the autonomy's operation. However, there is substantial evidence that as new levels of automation are introduced, systems still are prone to unsafe behaviors when interacting with humans. This means that the communication and interaction between the driver and the automation must be carefully modeled and optimized to guarantee safe performance. We present a framework that formalizes designing user interfaces for intelligent vehicles, by optimizing informativeness subject to brevity and utility. By modeling the system as a communication channel, we estimate the reduction of entropy of human-autonomy system, given a probability distribution over attributes obtained from user data, and information constraints to ensure brevity. Through this, we observe an approximately quadratic relationship between the amount of information displayed to the user and performance metrics, referred to as the information-performance trade-off curve. This trend was found in situational awareness, driving performance, and trust in the autonomy. Thus, in order to optimize interaction and performance, quantifying the informativeness of attributes and the conciseness of the interface is key in developing systems that must smoothly interact with humans.