Accurate pedestrian intention prediction (PIP) by Autonomous Vehicles (AVs) is one of the current research challenges in this field. In this article, we introduce PIP-Net, a novel framework designed to predict pedestrian crossing intentions by AVs in real-world urban scenarios. We offer two variants of PIP-Net designed for different camera mounts and setups. Leveraging both kinematic data and spatial features from the driving scene, the proposed model employs a recurrent and temporal attention-based solution, outperforming state-of-the-art performance. To enhance the visual representation of road users and their proximity to the ego vehicle, we introduce a categorical depth feature map, combined with a local motion flow feature, providing rich insights into the scene dynamics. Additionally, we explore the impact of expanding the camera's field of view, from one to three cameras surrounding the ego vehicle, leading to enhancement in the model's contextual perception. Depending on the traffic scenario and road environment, the model excels in predicting pedestrian crossing intentions up to 4 seconds in advance which is a breakthrough in current research studies in pedestrian intention prediction. Finally, for the first time, we present the Urban-PIP dataset, a customised pedestrian intention prediction dataset, with multi-camera annotations in real-world automated driving scenarios.

翻译：自动驾驶车辆准确预测行人意图是当前该领域的研究挑战之一。本文提出PIP-Net，一种专为自动驾驶车辆在真实城市场景中预测行人穿越意图而设计的新型框架。我们针对不同摄像头安装方式与配置，提供两种PIP-Net变体。该模型融合驾驶场景的运动学数据与空间特征，采用基于循环神经网络与时序注意力机制的解决方案，性能超越现有最优方法。为增强对道路使用者及其与本车距离的视觉表征，我们引入类别化深度特征图，并结合局部运动流特征，从而提供场景动态的丰富信息。此外，我们探讨了将摄像头视野从本车周围单摄像头扩展至三摄像头的影响，显著提升了模型的上下文感知能力。根据交通场景与道路环境的不同，该模型可提前4秒准确预测行人穿越意图，这成为当前行人意图预测研究中的一项突破。最后，我们首次提出Urban-PIP数据集——一个面向真实自动驾驶场景的多摄像头标注定制化行人意图预测数据集。