# Situationally Sensitive Path Planning
## 5 Experiments  
As a prototypical test of our pipeline's ability to support a design task, we ran a limited test on an indoor, enclosed space (Figure 4). Using the design, we then explored our model's ability to embody situationally sensitive agency of virtual pedestrians with respect to both static and dynamic environmental factors in simulation. Virtual pedestrians were spawned at 0.1 s intervals into an entry space and tasked with finding their way to an exit space, with simple intervening barriers in the design of the site. The model pedestrians' task, then, is to move to that destination by initially pursuing the shortest path. What constitutes the shortest path is updated dynamically to account for intervening obstacles that may appear. For local collisions, agents engage RVOs as we discussed above. In addition, to approximate personality, five distinct personality preferences are used to predispose agents toward embodiment as they dynamically encounter situational context that they maintain (personal) sensitivity to along a given path, and these are activated individually on virtual pedestrians. This allows us to observe their influence on the emergent crowd flow and distribution across the virtual manifold. We developed a set of seven scenarios to form a test-bed simulation for our approach. A control scenario (CONTROL) was used with A* path planning [35] alone as a comparison to Dijkstra. This was enabled through Unity3D's in-built NavMesh plugin, which also features an RVO implementation for local agent collision avoidance that emphasizes run-time efficiency. Algorithmically, NavMesh RVO is similar to our model, which allowed us to isolate our observations and findings to the alterations that we made specifically to localize pathing behavior. Together, CONTROL approximates what would be available to a programmer using the available Unity functions. Another simulation was run with a Dijkstra heuristic (DIJKSTRA) alone as a benchmark. A* and Dijkstra simulations yield path planning that considers site factors of collision with fixed obstacles of the built environment, distance to destination, and time to destination. Both graph distance and metric distance are factors. A third static scenario was run with portions of the space coded as \"risky\" (S-RISK). Pedestrians with (personal) risk aversion in their profile will adjust their planning for locomotion away from these fixed sites. We then introduced a set of dynamic scenarios. The first examined dynamic risk: a dog character was introduced to the space, on the loose and running through the enclosed space and crowd in the reverse direction of predominant flow (D-RISK). The dog is designed to introduce a sudden uncharacteristic event to the simulation, which should nudge  
Figure 4: (**A**) This environment is modeled after subway platforms where equidistant pillars control the flow of traffic. Pedestrians are demonstrated to move to the left and right in response to visible trash in the central path. (**B**) An in-engine screenshot featuring risk factors and dirtiness factors represented by litter on the path and a model of a dog. pedestrians off their paths and require that they generate response motion. Agents with a predisposition to risk that encountered the dog in their view would adjust their planning for locomotion paths around the dog. Others without that personality trait (without that sensitivity) would ignore the dog. We also programmed some agents to drop litter (banana peels) on the space as they moved, seeding the space with a dynamic geography of cleanliness/dirtiness (DIRT). Agents with an aversion to littered space would move accordingly when encountering this dirt. Finally, we established a scenario to account for congestion (DENSITY), as polled by agents that are predisposed based on the number of counterpart agents that they encounter in their vision. The final scenario ran all of these factors simultaneously (ALL).