论文标题
用于行人行动预测的多模式混合体系结构
Multi-Modal Hybrid Architecture for Pedestrian Action Prediction
论文作者
论文摘要
行人行为预测是城市环境中智能驾驶系统的主要挑战之一。行人经常表现出广泛的行为和对这些行为的充分解释，取决于各种信息来源，例如行人外观，其他道路使用者的状态，环境布局等，我们为解决此问题而言，我们提出了一种新型的多模式预测算法，该算法将捕获的各种信息来源，这些信息源于环境中捕获的不同信息来预测行人的未来交叉行动。所提出的模型受益于混合学习体系结构，该结构由馈电和经常性网络组成，用于分析环境的视觉特征和场景的动态。使用现有的2D行人行为基准和新注释的3D驾驶数据集，我们表明我们提出的模型在人行横道交叉预测中实现了最新的表现。
Pedestrian behavior prediction is one of the major challenges for intelligent driving systems in urban environments. Pedestrians often exhibit a wide range of behaviors and adequate interpretations of those depend on various sources of information such as pedestrian appearance, states of other road users, the environment layout, etc. To address this problem, we propose a novel multi-modal prediction algorithm that incorporates different sources of information captured from the environment to predict future crossing actions of pedestrians. The proposed model benefits from a hybrid learning architecture consisting of feedforward and recurrent networks for analyzing visual features of the environment and dynamics of the scene. Using the existing 2D pedestrian behavior benchmarks and a newly annotated 3D driving dataset, we show that our proposed model achieves state-of-the-art performance in pedestrian crossing prediction.
