基于视野域机制的行人轨迹预测

为提高行人在复杂交通场景中交互的安全性，提出一种基于social-GAN（social-generative adversarial network）的行人轨迹预测算法SAN-GAN（social angle norm-GAN）。该算法首先以行人历史位置信息与头部信息为输入，通过轨迹生成器LSTM网络（long short term memory networks）获取行人隐藏特征信息，并基于行人视野域模块捕捉行人视野域动态变化，对所有行人建立扇形视野域并筛选有效信息，从而驱动神经网络模型预测行人未来轨迹变化。将SAN-GAN与LSTM、social-LSTM（social-long short term memory networks）、social-GAN等轨迹预测算法进行对比实验，结果表明SAN-GAN算法相较于其他算法，在预测3.2 s的行人轨迹时，ADE分别平均降低65.8%、51.2%、10.7%，FDE分别平均降低73.6%、60.9%、10.4%。SAN-GAN能够有效地预测行人在复杂交通环境中进行交互的未来轨迹。

Pedestrian trajectory prediction based on field of view mechanism

In order to improve the safety of pedestrian interaction in complex traffic scenes, this paper proposed a pedestrian trajectory prediction algorithm SAN-GAN based on social-GAN. The algorithm firstly took the pedestrian''s historical location information and head information as input, obtained the hidden feature information of the pedestrian through the trajectory generator LSTM network, captured the dynamic changes of the pedestrian''s visual field based on the pedestrian visual field module, built a fan-shaped visual field for all pedestrians and filters the valid information, thus driving the neural network model to predict future trajectory changes of pedestrians. By comparing SAN-GAN with LSTM, social-LSTM, social-GAN and other trajectory prediction algorithms, the results show that the SAN-GAN algorithm reduces the ADE by an average of 65.8%, 51.2% and 10.7%, and the FDE by an average of 73.6%, 60.9% and 10.4%, respectively, in predicting the pedestrian trajectory for 3.2 s. The SAN-GAN is effective in predicting the future trajectory of pedestrians interacting in complex traffic environments compared to other algorithms.