  Road accidents involving autonomous vehicles commonly occur in situations
where a (pedestrian) obstacle presents itself in the path of the moving vehicle
at very sudden time intervals, leaving the robot even lesser time to react to
the change in scene. In order to tackle this issue, we propose a novel
algorithmic implementation that classifies the intent of a single arbitrarily
chosen pedestrian in a two dimensional frame into logic states in a procedural
manner using quaternions generated from a MediaPipe pose estimation model. This
bypasses the need to employ any relatively high latency deep-learning
algorithms primarily due to the lack of necessity for depth perception as well
as an implicit cap on the computational resources that most IoT edge devices
present. The model was able to achieve an average testing accuracy of 83.56%
with a reliable variance of 0.0042 while operating with an average latency of
48 milliseconds, demonstrating multiple notable advantages over the current
standard of using spatio-temporal convolutional networks for these perceptive
tasks.
