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：自动驾驶汽车对行人意图的精确预测（PIP）是该领域当前的研究挑战之一。本文提出PIP-Net——一个专为自动驾驶汽车在真实城市场景中预测行人过街意图而设计的新型框架。我们提供了两种针对不同摄像头安装方式和设置优化的PIP-Net变体。该模型通过整合运动学数据与驾驶场景的空间特征，采用基于循环结构和时间注意力机制的解决方案，性能超越了现有最优方法。为增强交通参与者及其与本车接近程度的视觉表征，我们引入了一种类别深度特征图，并将其与局部运动流特征相结合，从而提供对场景动态的丰富洞察。此外，我们探究了将摄像头视野从本车周围一个摄像头扩展至三个摄像头的影响，这显著提升了模型的上下文感知能力。根据交通场景和道路环境的不同，该模型可提前4秒预测行人过街意图，这在行人意图预测研究中是一项突破性进展。最后，我们首次发布了Urban-PIP数据集——一个针对真实自动驾驶场景中多摄像头标注的定制化行人意图预测数据集。