Pedestrian motion prediction is a key part of the modular-based autonomous driving pipeline, ensuring safe, accurate, and timely awareness of human agents' possible future trajectories. The autonomous vehicle can use this information to prevent any possible accidents and create a comfortable and pleasant driving experience for the passengers and pedestrians. A wealth of research was done on the topic from the authors of robotics, computer vision, intelligent transportation systems, and other fields. However, a relatively unexplored angle is the integration of the state-of-art solutions into existing autonomous driving stacks and evaluating them in real-life conditions rather than sanitized datasets. We analyze selected publications with provided open-source solutions and provide a perspective obtained by integrating them into existing Autonomous Driving framework - Autoware Mini and performing experiments in natural urban conditions in Tartu, Estonia to determine valuability of traditional motion prediction metrics. This perspective should be valuable to any potential autonomous driving or robotics engineer looking for the real-world performance of the existing state-of-art pedestrian motion prediction problem. The code with instructions on accessing the dataset is available at https://github.com/dmytrozabolotnii/autoware_mini.

翻译：行人运动预测是基于模块化自动驾驶流程的关键组成部分，它确保了对人类行为主体未来可能轨迹的安全、准确和及时的感知。自动驾驶车辆可利用该信息预防潜在事故，并为乘客与行人创造舒适愉悦的驾驶体验。来自机器人学、计算机视觉、智能交通系统等领域的学者已对此课题开展了大量研究。然而，将前沿解决方案集成至现有自动驾驶技术栈，并在真实场景而非净化数据集中进行评估，仍是相对未被充分探索的方向。本文选取已开源的研究成果进行分析，通过将其集成至现有自动驾驶框架——Autoware Mini，并在爱沙尼亚塔尔图市的自然城市场景下开展实验，以评估传统运动预测指标的实际价值，从而提供实践视角。该视角对于任何关注现有行人运动预测前沿方案在真实场景中性能的自动驾驶或机器人工程师具有重要参考价值。相关代码及数据集访问指南发布于 https://github.com/dmytrozabolotnii/autoware_mini。