Abstract: This paper describes an {\em `execution monitor'} (EM); it is the key component of a motion control architecture for a vehicle moving in a dynamic and partially known environment. EM endows the vehicle with the reactive capabilities required in an uncertain environment. Its purpose is to generate the commands for the servo-systems of the vehicle so as to follow a given nominal trajectory while reacting in real-time to unexpected events. EM is designed as a fuzzy controller, i.e. a control system based upon fuzzy logic, thus permitting approximate reasoning and a human-like description of the vehicle's reactive behaviour. The main components of EM are an inference engine and a set of `linguistic rules'. The global behaviour of the vehicle results from the combination of several basic behaviours (trajectory following, obstacle avoidance, etc.), each of which is encoded by a specific set of rules. EM differs from classical fuzzy controllers in two novel ways: first, it introduces a new defuzzification technique, the {\em Barycentre of the Centres Of Area}, that permits to better take into account the influence of each and every rule. Second, weighing coefficients are attached to the rules thus permitting a fine tuning of the influence of each basic behaviour. Furthermore it is shown how supervised learning, i.e. learning through samples, can be used to automate the determination of these weights thus suppressing the ever delicate problem of finding such coefficients (the identification problem). EM has been implemented and tested on an electric car-like vehicle prototype. Promising results of preliminary experiments featuring trajectory following and unexpected obstacle avoidance are presented.