# Situationally Sensitive Path Planning
## 2 Related Work
### Continuum Mechanics Models
#### 2.3.2 Microscopic Mechanics Between Pedestrians
Boids algorithms for complex swarming and flocking have proven to be computationally efficient, and information polling also has conceptual foundations in offering a real-world understanding of pedestrian vision and motion responses to events that unfold in localized surroundings through perception [100; 101; 102; 103; 104; 105; 106]. Although particle-type schemes are mathematically tractable, it is well understood that pedestrians do not move as particles. Intentional vision and inter-personal egotistical motion [107] are the main missing behaviors from particle approaches. Here, we approximate them using the most commonly used velocity-based models. These include Velocity Obstacles (VOs) [108; 109; 110], Reciprocal Velocity Obstacles (RVOs) [111; 112; 113], Optimal Reciprocal Collision Avoidance (ORCA) [114], Hybrid RVO (HRVO) [115; 116], and their various cousins [117] that prioritize velocity-based collision prediction schemes. Some variants of this approach invest in synthetic vision for pedestrians, and as such velocity-based models provide a solid basis for locally aware agents that react accordingly to neighboring agents' anticipated movement. We use the approach in our pipeline, further incorporating aspects of vision-based models that mimic motor motion response maneuver behaviors as synthesized visual fields [118; 40; 119]. Vision-based models are a related subset of cognitive and socio-behavioral agent models that move away from physics as inspiration and toward behavioral parity with real-world agency [105; 120]. Although tangential to our approach, which is focused on weak agency [121] (i.e., just enough artificial intelligence to produce design-relevant motion behavior as a response to embodied situation, and not as much investment in stronger agency that would directly recreate pedestrian cognition-movement-motion behavior), a range of pedestrian psychology models pickup essentially where particle and vision-based models leave off and weave in aspects of agents' socio-psychological interactions. Notable in this tradition is the prevalence of personality, emotion, and reinforcement learning-based models from psychology that have been featured in agent computing for character movement in animation [122]. These include iterations of the personality and emotional hypothesis such as OCEAN (Openness, Conscientiousness, Extraversion, Agreeableness, and Neuroticism) [123; 124]; PEN (Psychoticism, Extraversion, and Neuroticism) [125]; and the OCC (Ortony, Clore, and Collins) [126] methodology. Each concept pays considerable attention to the authenticity and diversity of individual agents/agency. When expressed as states and rules in simulation, these concepts allow for a focus on parameterizing the behavioral, temperamental, and mental characteristics of pedestrians as states in a finite-state machine approach [127] to driving pedestrian motion. For example, OCEAN and OCC have been used to generate distinct pedestrian and character movement models, and they have been successfully bridged together within aggregate multi-agent systems [128]. We also mention that data mining and machine learning-based models [129; 130] can be used for situations in which trajectory samples of motion in enclosed spaces are available. For example, data mining and knowledge discovery have been built into agency to poll highly localized conditions from agent vision filters, e.g., data mining can accommodate very fast lookups of the type \"I am this far from a wall, and in front of me is a barrier with this orientation; I am also surrounded by neighboring pedestrians, and I estimate that I will collide with them here, here, and there if I continue on my planned path\". These types of declarations can be expressed, collated, and quizzed in knowledge discovery architectures, which can be parameterized as \(k\)-d trees for storage and organization [131; 132; 133; 134; 135] and which can use the \(k\) nearest neighbor (\(k\)-NN) search algorithm [136] to efficiently recover results during the run time for quick consultation with a database.