A blackboard model of problem solving is applied in the design of a vision system by which an autonomous land vehicle (ALV) navigates roads. The ALV vision task consists of hypothesizing objects in a scene model and verifying these hypotheses using the vehicle's sensors. Object hypothesis generation is based on an a priori map, a planned route through the map, and the current state of the scene model. Verification of an object hypothesis involves directing the sensors toward the expected location of the object, collecting evidence in support of the object, and depositing the verified object in the scene model. An object is a hierarchy of frames connected by part/whole, spatial, and inheritance relationships; these frames reside on a structured blackboard. Each level of the blackboard corresponds to a class of object in the part/whole hierarchy, with the lowest levels containing primitive sensor image features. In top-down verification, an object hypothesis posted at an upper level activates knowledge sources which generate hypotheses at lower levels representing the object's components. In bottom-up analysis, used when knowledge of the environment is limited, sensor-driven hypotheses posted at lower levels generate multiple hypotheses at higher levels. Each blackboard level is a YAPS production system, whose rules represent the knowledge sources, and whose facts are object frames modeled by Lisp Flavors. The implementation strategy thus integrates object-oriented design and production system methodology. The system has been tested successfully with the single task of building a scene model containing a straight road. New feature extractors, sensors, and objects classes are currently being added to the system.