论文标题
“如果你能通过我的眼睛看到我”：预测行人的看法
"If you could see me through my eyes": Predicting Pedestrian Perception
论文作者
论文摘要
行人是城市交通中特别脆弱的道路使用者。随着自动驾驶的到来，可以专门开发新技术来保护行人。我们建议使用机器学习工具链来训练人工神经网络作为行人行为的模型。在一项初步研究中，我们使用来自特定人行横情景的模拟中的综合数据来训练各种自动编码器和长期短期记忆网络，以预测行人的未来视觉感知。我们可以在相关时间范围内准确预测行人的未来看法。通过将这些预测的框架迭代地馈入这些网络，可以用作我们结果表明的行人模拟。即使从自动驾驶汽车的角度来看，这种训练有素的网络也可以用来预测行人行为。将来的另一个扩展名是通过现实世界的视频数据重新培训这些网络。
Pedestrians are particularly vulnerable road users in urban traffic. With the arrival of autonomous driving, novel technologies can be developed specifically to protect pedestrians. We propose a machine learning toolchain to train artificial neural networks as models of pedestrian behavior. In a preliminary study, we use synthetic data from simulations of a specific pedestrian crossing scenario to train a variational autoencoder and a long short-term memory network to predict a pedestrian's future visual perception. We can accurately predict a pedestrian's future perceptions within relevant time horizons. By iteratively feeding these predicted frames into these networks, they can be used as simulations of pedestrians as indicated by our results. Such trained networks can later be used to predict pedestrian behaviors even from the perspective of the autonomous car. Another future extension will be to re-train these networks with real-world video data.
