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.

翻译：涉及自动驾驶车辆的交通事故通常发生在行人障碍物在极短时间内突然出现在车辆行驶路径中的场景，使得机器人对场景变化的反应时间进一步缩短。为解决该问题，我们提出一种新型算法实现，该算法利用MediaPipe姿态估计模型生成的四元数，以程序化方式将二维帧中随机选取的单个行人意图分类为逻辑状态。该方法避免了使用高延迟深度学习算法，这主要得益于其无需深度感知能力，同时考虑了多数物联网边缘设备固有的计算资源隐性限制。该模型在平均测试准确率达到83.56%、方差稳定为0.0042的同时，实现了平均48毫秒的运行延迟，展现出相较于当前基于时空卷积网络完成感知任务的方案的多项显著优势。