… Based on the inverse reinforcement learning, we propose a scene-oriented pedestrian
trajectory prediction network named IRLSOT. Figure 1 illustrates the pipeline of the proposed …

… group user trajectory prediction. Specifically, we introduce a mobile user trajectory prediction
… Term Memory networks (LSTM) with Reinforcement Learning (RL) to automate the model …

… In summary, reinforcement learning-based trajectory prediction methods for AVs can be
classified into Table V. Such methods use MDP to maximize the expected cumulative reward …

… method for a trajectory prediction model based on the encoder–decoder architecture,
which has the following advantages over the GMN. First, instead of predicting the probability …

… are essential for accurate trajectory prediction. Afterward, we provide a brief review of inverse
reinforcement learning, which is used to explore the trajectory–scene associations in this …

… trajectory predictor based on Reinforcement Learning (RL) to automatically realize a high-performing
LSTM predictor for a given learning … , we benefit from Transfer Learning (TL). The …

… In this paper, we presented a future trajectory prediction framework based on RNN and
maximum margin IRL. We trained the RNN and the reward function simultaneously by minimizing …

… trajectory prediction framework that combines a multimodal trajectory generation network
with inverse reinforcement … The current research on vehicular trajectory prediction mainly …

… , we address an important gap in trajectory prediction. We propose SafeCritic, a model that
… multiple “real” trajectories with reinforcement learning to generate “safe” trajectories. The …

… in the front queue, we propose a trajectory prediction strategy that consists of two steps. The
first is, at each time step, simulating the trajectories of the vehicle queue in front of CAV. The …