Motivated by this idea, this paper addresses the problem of vehicle trajectory prediction over an extended horizon. On highways, human drivers continuously adapt their speed and paths according to the behavior of their neighboring vehicles. Therefore, vehicles' trajectories are very correlated and ...
Oriented toward a mixed human-driven and machine-driven traffic environment, a vehicle trajectory prediction algorithm based on an encoding–decoding framework composed of a multiple-attention mechanism is proposed. Firstly, a directed graph is used to describe vehicle–vehicle motion dependenc...
The prediction accuracy is better than the current mainstream methods. Key words: vehicle trajectory prediction, graph models, attention mechanism, multimodal Cite this article LIAN Jing, DING Rongqi, LI Linhui, WANG Xuecheng, ZHOU Yafu. Vehicle Trajectory Prediction Method Based on Graph Models ...
In the previous work, Recurrent Neural Network model for urban vehicle trajectory prediction is proposed. For the further improvement of the model, in this study, we propose Attention-based Recurrent Neural Network model for urban vehicle trajectory prediction. In this proposed model, we use ...
In order to improve the prediction accuracy of autonomous vehicles in complex traffic scenarios under multi-vehicle interactions, this paper proposes an attention mechanism considering the historical trajectories of vehicles to be predicted and the attention mechanism between interacting vehicles based on con...
This paper presents online-capable deep learning model for probabilistic vehicle trajectory prediction. We propose a simple encoder-decoder architecture based on multi-head attention. The proposed model generates the distribution of the predicted trajectories for multiple vehicles in parallel. Our approach ...
Trajectory prediction is one of the main challenges to autonomous vehicles. Except for the predicted vehicle historical trajectory information, viable solutions for this task must also consider the static geometric context, such as lane center...
Recently, mainstream methods are mostly based on recurrent neural networks, which have achieved state-of-the-art (SOTA) performance on distance-based prediction metrics. In order to consider the influence of static geometric context and dynamic interaction information better for the vehicle trajectory ...
Vehicle Trajectory Prediction (VTP) is one of the key technologies for autonomous driving, which can improve the safety and collaboration of the autonomous driving system. The interaction behavior among vehicles in reality has an impact on VTP. However, many methods ignore the interaction among vehic...
MALS-Net: A Multi-Head Attention-Based LSTM Sequence-to-Sequence Network for Socio-Temporal Interaction Modelling and Trajectory Prediction multi-head attentiontransformervehicle trajectory predictionPredicting the trajectories of surrounding vehicles is an essential task in autonomous driving, especially... ...