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Abstract-We consider the problem of collision avoidance at vehicular intersections for a set of controlled and uncontrolled vehicles that are linked by wireless communication. Each vehicle is modeled by a first order system. We use a disturbance to account for bounded model uncertainty. We construct a discrete event system abstraction and formulate the problem in the context of supervisory control for discrete event systems with uncontrollable events. This allows us to mitigate computational limitations related to the presence of continuous dynamics and infinite state spaces. For solving the resulting supervisory control problem at the discrete event level, we develop an algorithm that exploits the structure of the transition map to compute the supremal controllable sublanguage more efficiently than standard algorithms. We present implementation results on an intersection with several vehicles.

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We consider the problem of collision avoidance at road intersections in vehicular networks in the presence of uncontrolled vehicles, a disturbance, and measurement uncertainty. Our goal is to construct a supervisor of the continuous time system that is safe (i.e., avoids collisions), nonblocking (i.e., all vehicles eventually cross the intersection), and maximally permissive with respect to the discretization, despite the presence of a disturbance and of measurement uncertainty. We proceed in four steps: defining a discrete event system (DES) abstraction of the continuous time system, using uncontrollable events to model the uncontrolled vehicles and the disturbance; translating safety and non-blocking requirements to the DES level; solving at the DES level; and translating the resulting supervisor back from the DES level to the continuous level. We give sufficient conditions for this procedure to maintain the safety, non-blocking and maximal permissive properties as the supervisor is translated back from the DES level to the continuous level. Prior work on this problem based on similar abstractions assumes perfect measurement of position. Our method for handling measurement uncertainty is to introduce measurement events into the DES abstraction and then to compute the observer of the DES abstraction and the supremal controllable solution of the DES supervisory control problem.