In independent vehicle concepts for the Automated Highway System (AHS), the ability to make competent tactical-level decisions in real-time is crucial. Traditional approaches to tactical reasoning typically involve the implementation of large monolithic systems, such as decision trees or finite state machines. However, as the complexity of the environment grows, the unforeseen interactions between components can make modifications to such systems very challenging. For example, changing an overtaking behavior may require several, non-local changes to car-following, lane changing and gap acceptance rules. This paper presents a distributed solution to the problem. PolySAPIENT consists of a collection of autonomous modules, each specializing in a particular aspect of the driving task - classified by traffic entities rather than tactical behavior. Thus, the influence of the vehicle ahead on the available actions is managed by one reasoning object, while the implications of an approaching exit are managed by another. The independent recommendations form these reasoning objects are expressed in the form of votes and vetos over a 'tactical action space', and are resolved by a voting arbiter. This local independence enables PolySAPIENT reasoning objects to be developed independently, using a heterogenous implementation. PolySAPIENT vehicles are implemented in the SHIVA tactical highway simulator, whose vehicles are based on the Carnegie Mellon Navlab robots.