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：涉及自动驾驶的交通事故常发生在行人障碍物突然出现在车辆行进路径中的场景，此时机器人对场景变化的反应时间极为有限。针对该问题，我们提出了一种新型算法实现方案，通过MediaPipe姿态估计模型生成的四元数，以程序化方式将二维画面中任意选定行人的意图分类为逻辑状态。该方法避开了对高延迟深度学习算法的依赖，主要原因在于：深度感知并非必要条件，且大多数物联网边缘设备的计算资源存在隐性上限。该模型以48毫秒的平均延迟实现了83.56%的平均测试准确率及0.0042的可靠方差，在感知任务中展现出相较于当前主流的时空卷积网络的显著优势。