… convolutional network (GSTCN) to predict future trajectory distributions of all neighbor … the
vehicle trajectory prediction as estimating future trajectory distributions given past trajectories. …

… 14], random forest classifiers [20] and recurrent neural networks have been used for maneuver
recognition. Trajectory prediction modules output the future locations of the vehicle given …

… the trajectory prediction of vehicles in a connected environment is revisited. The Heterogeneous
Context-Aware Graph Convolutional Net… states of connected vehicles and their driving …

… of trajectory prediction. This is the first work we are aware of to use an end-to-end convolutional
architecture for trajectory prediction (Deo and Trivedi [15] used convolutional pooling for …

… In order to train a neural network for trajectory prediction, we need a set of recorded trajectories
of traffic participants. These can be extracted from data recorded from a vehicle equipped …

… While a majority of previous studies have focused on path prediction of target vehicles in …
addresses vehicle path prediction with uncertainty based on perception for the target vehicles …

… ME loss is not suitable for the trajectory prediction problem due to the mode … -Trajectory
Prediction (MTP) loss, motivated by [37], that explicitly models the multimodality of the trajectory …

… We set up three kinds of recurrent neural networks for training during the temporal prediction
stage in this experiment and distribute the average loss in training based on time (see Fig. 6…

… Our goal is to predict the future trajectory Tpred of all vehicles for the next F … car’s past
trajectory to predict the future trajectory without using any information about the surrounding cars. …

… for joint aggregation of higher-level features in prediction tasks. The memory… vehicle states
as the dynamic context. Second, we propose the use of two 2D convolutional neural networks …