# Situationally Sensitive Path Planning
###### Abstract
Operationally, they are cheap, require limited resource usage for computing, and interface well as data structures with geometry objects from design software (e.g., as navigation meshes, or NavMesh structures). In practice, path planning heuristics also generate realistic-_looking_ motion for agent characters in animation software, for example. Figure 1: Agents demonstrating divergent trails and detouring away from the leftmost corridor based on emerging situational factors such as crowd density in corridors. For visualization, the blue–green–yellow agent color ramp represents a low-to-high gradient of pedestrian density within each agent’s path segment and its proxemics. Agents select global least-cost, but situationally sensitive, paths through the environment using factors ranging from the Euclidean-based shortest path to personal preferences such as congestion, safety, and cleanliness. However, in the real world, pedestrians do not move solely by path planning [19] and it is generally preferrable for design problems that pedestrian models produce realistic-_behaving_ pedestrians. In reality, pedestrians' path planning decision is just one component of a very complex set of behaviors that they employ to move through space and time [20]. Path planning provides a guide for movement and a way for pedestrians to assess their progress in terms of time geography [21; 22], a preliminary sketch of their so-called space-time path, or their perception of the spacing and timing available for them to complete an activity (at coarse scale of space-time) or action (at fine-scale). Along the way, using the path as a guide, pedestrians are dynamically influenced by their embodiment in a space [23] or a grounded and enlivened connection to the situational factors that associate them to spatial and geographical context. From this, pedestrians draw meaning from their surroundings, and this meaning informs their trajectory in space and time and the encounters that they perceive as relevant along the way. This embodiment generally takes on two forms: physical embodiment in the space [24] and social embodiment with other pedestrians [25]. Both embodiments require consideration of site (the configurational properties of a space) and situation (the uses that the spaces affords to an individual pedestrian or to a group of pedestrians). Importantly, pedestrians' assessment of embodiment takes hold in very small bubbles of space and time, such that their space-time path can be considered a series of beads of encounter and experience [26]. In this paper, we consider how we might computationally string these beads as situational factors onto existing threads of heuristic paths provided by site factors. We reason that, algorithmically, the shortest path can provide a useful starting point, particularly paths that are instantiated as NavMesh data structures (graphs) in popularly used CAD and animation software. We reason that situational factors can be added to this graph structure in ways that preserve the computational economy of path planning but that add some of the localized \"beads\" of context that are necessary to enliven designs with realistic-behaving (and not just realistic-looking) pedestrians. Further, we do so with consideration of the  
Figure 2: Moving from an origin to a destination across a grid space via A* and Dijkstra heuristics. computational burden of popularly used design platforms such as _Unity 3D_, which already load an array of packages and functions during run time and may often have limited available resources for dynamic models. Specifically, we examine an interstitial solution that bridges the current state of practice in CAD design software, with advances from agent perception modeling. Our approach takes site-based models for path planning and expands them to incorporate situational context, doing so in a manner that is locally sensitive to the plan suggested by the global path.