Human activity recognition (HAR) is a wide research topic in a field of computer science. Improving HAR can lead to massive breakthrough in humanoid robotics, robots used in medicine and in the field of autonomous vehicles. The system that is able to recognise human and its activity without any errors and anomalies would lead to safer and more empathetic autonomous systems. During this research work, multiple neural networks models, with different complexity, are being investigated. Each model is re-trained on the proposed unique data set, gathered on automated guided vehicle (AGV) with the latest and the modest sensors used commonly on autonomous vehicles. The best model is picked out based on the final accuracy for action recognition. Best models pipeline is fused with YOLOv3, to enhance the human detection. In addition to pipeline improvement, multiple action direction estimation methods are proposed.

中文翻译：

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基于人类活动识别的自动驾驶汽车路径规划

人类活动识别（HAR）是计算机科学领域的一个广泛研究课题。改进 HAR 可以导致仿人机器人、医学机器人和自动驾驶汽车领域的巨大突破。能够在没有任何错误和异常的情况下识别人类及其活动的系统将导致更安全、更善解人意的自主系统。在这项研究工作中，正在研究具有不同复杂性的多个神经网络模型。每个模型都在提议的独特数据集上重新训练，这些数据集在自动导引车 (AGV) 上收集，并使用自动驾驶汽车上常用的最新和适中的传感器。根据动作识别的最终准确率选出最佳模型。最佳模型管道与 YOLOv3 融合，以增强人体检测。除了管道的改进，