期刊名称：ISPRS International Journal of Geo-Information
电子版ISSN：2220-9964
出版年度：2021
卷号：10
期号：5
页码：336
DOI：10.3390/ijgi10050336
语种：English
出版社：MDPI AG
摘要：Forecasting the motion of surrounding vehicles is necessary for an autonomous driving system applied in complex traffic. Trajectory prediction helps vehicles make more sensible decisions, which provides vehicles with foresight. However, traditional models consider the trajectory prediction as a simple sequence prediction task. The ignorance of inter-vehicle interaction and environment influence degrades these models in real-world datasets. To address this issue, we propose a novel Dynamic and Static Context-aware Attention Network named DSCAN in this paper. The DSCAN utilizes an attention mechanism to dynamically decide which surrounding vehicles are more important at the moment. We also equip the DSCAN with a constraint network to consider the static environment information. We conducted a series of experiments on a real-world dataset, and the experimental results demonstrated the effectiveness of our model. Moreover, the present study suggests that the attention mechanism and static constraints enhance the prediction results.
