# Situationally Sensitive Path Planning
## 4 Methodology  
In practice, our implementation is divided into two intertwined layers (Figure 3). First, a global manifold stores and updates the current physical (geometric and geographical) space of all traversable regions, as well as their properties/attributes. In essence, this functions as a Geographic Information System (GIS): a geometry interface to an underlying spatial database with lookup functions to convert (map) queries between the two. However, unlike traditional cartographic or geomatic user-facing GIS, our system is designed for model pedestrian lookup and geographic querying to the geometry layer. Further, we maintain a graph layer that is fully connected to the geometry, so that the GIS affords interoperability across a hyperspace (i.e., geographic slipstreaming [176]). In practical terms, the physical layer corresponds to the design space, with a layout of an enclosed space, interior features, and some fixed and movable objects. We refer to this as a \"site\". These site factors are the central terms of adjustment and design manipulation in CADsoftware, representing walls, barriers, entrances and exits, assembly spaces, required egress distances, and so forth [177]. A second layer of the implementation is realized as the cognitive space of model pedestrians that will occupy the site and that will animate it with their individual and collective motion. We consider these as \"situational\". Critically, agents should be responsive to both site factors, as well as being sensitive to situational factors. In our implementation, situational factors will be (1) at fine resolution, embedded within the site space, and (2) be of a higher degree of specificity than site factors relative to model agency. Motion through the enclosed space design, then, should be a converged product of site factors, as well as situational factors. Further, it should be driven by a GIS that allows for typical CAD-based  
Figure 3: An overview of the implementation methodology. (**Design phase**) The user is welcome to design the environmental layout and parameterize segment regions and agents with features and preferences, respectively. (**Simulation phase**) During run time, agents query the global layer to calculate an optimal path based on their preferences. Locally, RVO allows agents to avoid collisions with neighbor agents identified within their synthetic view frustums. design layouts, as well as agent-based pedestrians that can enliven those designs with space-time occupancy and emergent dynamics of movement that result from the design. Our implementation of agency warrants a more detailed explanation. In our pipeline, we follow the concept of situated cognition [149] as the basis for designing and specifying agency, i.e., the notion that pedestrians will move through an environment with a comparatively low level of cognitive load up to the point of encountering situational properties of the (very localized) environment and/or socio-situational factors of ambient pedestrians. At the point of encounter, then, pedestrians context switch from a default of path-following (which is often routine and habitual, and which generally follows a shortest path or least-effort driver) to an alternative movement behavior of perception and response that is driven primarily by vision, and which is likely to be deliberative and bespoke to a given situation. Generally, this context switch takes place through inter-personal proxemics [178; 179; 180; 181; 182]. For example, one generally knows how to walk in a straight line and avoid walls, but it may take some careful choreography of steering and locomotion to avoid bumping into another person, particularly when that person is also counter-considering how to avoid a collision with you [183; 184; 185; 186]. To fold situational agency into the system, we implement a second system, functioning on an agent-based state space composed of individualized automata neighborhoods (synthetic visual fields) that are projected as geography (geometry) downward (in resolution) to individual agents.