Trajectory Prediction from EGO View: A Coordinate Transform and Tail-Light Event Driven Approach
원문 초록
Trajectory prediction plays an important role in modern au-tonomous driving system. The multi-modal characteristics of trajectory prediction makes it difficult to accurately predict the driving intention and future trajectory of vehicles, espe-cially in the complex conditions, such as lane changing and road intersection. To improve the prediction performance, ad-ditional features are needed. High-definition (HD) map fea-ture and tail light feature are employed in this paper, which are fused with trajectory feature to assist vehicle trajectory pre-diction. A prediction model based on temporal convolution network (TCN) and graph convolution network (GCN) is constructed with corresponding loss functions. Experiments are carried out on our simulation dataset and the results show the effectiveness of the proposed feature fusion method as well as the prediction model.
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