论文标题
使用活动和姿势相关特征的基于RNN的行人交叉预测
RNN-based Pedestrian Crossing Prediction using Activity and Pose-related Features
论文作者
论文摘要
行人交叉预测是自动驾驶的关键任务。大量研究表明，对行人意图的早期估计可以减少甚至避免大部分的事故。在本文中，提出了深度学习系统的不同变化来解决此问题。提出的模型由两个部分组成：基于CNN的特征提取器和RNN模块。所有模型均已在JAAD数据集上进行培训和测试。获得的结果表明，选择特征提取方法，包括行人凝视方向和离散方向等其他变量以及所选的RNN类型对最终性能有重大影响。
Pedestrian crossing prediction is a crucial task for autonomous driving. Numerous studies show that an early estimation of the pedestrian's intention can decrease or even avoid a high percentage of accidents. In this paper, different variations of a deep learning system are proposed to attempt to solve this problem. The proposed models are composed of two parts: a CNN-based feature extractor and an RNN module. All the models were trained and tested on the JAAD dataset. The results obtained indicate that the choice of the features extraction method, the inclusion of additional variables such as pedestrian gaze direction and discrete orientation, and the chosen RNN type have a significant impact on the final performance.
