Vehicle trajectory predictionDelay minimizationBi-LSTM modelIn task offloading, the movement of vehicles causes the switching of connected RSUs and servers, which may lead to task offloading failure or high ser
Compared to other popular trajectory prediction models based on LSTM, GRU and Transformer, our FECA-LSMN model achieves leading or comparable performance in terms of RMSE, MAE and MAPE metrics while demonstrating notably faster computation time. The ablation experiments show that the incorporation of ...
In this paper, we propose the interaction-aware trajectory prediction model for autonomous vehicles based on LSTM-MLP model. The encoder module encoded the history trajectories to extract the dynamic feature of each vehicle in the scenarios by the LSTM model, and then the interaction module ...
Social LSTM: Human trajectory prediction in crowded spaces. In Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (pp. 961–971). IEEE. (Open in a new window)Google Scholar Altché, F., & de La Fortelle, A. (2017, October). An LSTM network ...
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 centerlines, and
Guan et al.16 proposed a deep learning model utilizing the Bi-LSTM self-attention mechanism to predict the next position of a vehicle based on the historical vehicle trajectory data collected by the checkpoint system. Huang et al.17 constructed a traffic network based on the traffic checkpoint ...
“Agent-based Mesoscopic Urban Traffic Simulation based on Multi-lane Cell Transmission Model.” Procedia Computer Science 151 (2019): 240-247. Choi, Seongjin, Jiwon Kim, and Hwasoo Yeo. “Attention-based Recurrent Neural Network for Urban Vehicle Trajectory Prediction.” arXiv preprint arXiv:...
proposed the “social-LSTM” structure [8], which allows LSTM that is adjacent in space to share each other’s hidden state, thereby capturing the dependencies between multiple related sequences. Ji et al. proposed a vehicle trajectory prediction model based on LSTM [9]. The model first uses...
However, many methods ignore the interaction among vehicles, which results in limited accuracy of prediction results. Therefore, we propose a Long Short-Term Memory (LSTM)-based Graph Attention Network (GAT) method for VTP, which encodes vehicle trajectory information with LSTM networks and ...
Furthermore, a number of studies have explored LSTM-based approaches for trajectory prediction, each offering unique perspectives on how to enhance prediction accuracy. For instance, Min et al. (2024) introduced a hierarchical LSTM-based approach that breaks down trajectory prediction into three ...