… predict trajectories of surrounding vehicles using a long short-term memory (LSTM) network.
… between the subject and surrounding vehicles using the LSTM network. It is demonstrated …

… surrounding vehicles to be predicted only depend on information observed from the ego vehicle
… to [21] which used information of vehicles around a target surrounding vehicle to the ego …

… We have presented an LSTM encoder-decoder based model for vehicle prediction module
for reasoning about the interdependencies neighboring vehicles’ motion, as depicted in the …

… Activity classification framework: Our goal is to build a robust system that can classify
surround vehicle maneuvers. We propose to use a multi-layer (stacked) LSTM architecture, as …

… Therefore, we propose a data-driven approach to predict future motions of surrounding
vehicles based on their previous motions. The motion predictor based on LSTM-RNN …

… We denote the object vehicle of trajectory prediction as Vs. We assume that one surrounding
vehicle Vi can affect the future motion of Vs, if and only if it is close to the vehicle of interest. …

… prediction method for surrounding vehicles using a hierarchical multi-sequence learning
network. In … interacting vehicles through the proposed structural-LSTM (long short-term memory) …

… of a surrounding vehicle in congested traffic is one of the necessary abilities of an autonomous
vehicle. In … ’s future movement is the result of its interaction with surrounding vehicles. A …

… surrounding vehicles are all relative to the target vehicle, as we expect drivers to usually
make decisions based on perceived distances and relative speeds rather than their values in …

… prediction of surrounding vehicles with the LSTM method. An LSTM network is established,
trained, and tested for the velocity/trajectory prediction of surrounding vehicles based on the …