| dc.description.abstract | With the increasing demand for execution speed and judgment accuracy of deep learning computer vision technology applicated in the automatic driving system and robot vision, the task of pedestrian trajectory prediction has become the research focus bin predicting the moving trajectory of pedestrians by the frame image. The automatic driving safety system can give the system more response time and reduce the probability of prediction error. And in robotics, the robot can understand human movement trajectory so that it can cooperate better. However, the movement of pedestrians is not rigid body motion that exists in the physical properties. When pedestrians walk, they should consider the surrounding environment, such as lanes, walls, lawns, roadside trees, etc., and also interact with other pedestrians. However, those mentioned above cannot be quantified or mathematical, which is currently a critical problem to overcome.
To effectively address the issues mentioned earlier, this thesis will handle them from time and spatial aspects. For the time aspect, we utilize CNN to learn the pedestrian’s trajectory features and predict their future trajectory in different environments. For the spatial aspect, we incorporate scene information maps to assist the model in generating more reasonable results during the trajectory prediction process. In brief, this work proposes a novel method that combines CoordConv with autoencoders for pedestrian trajectory prediction, and it can improve accuracy and generate pedestrian trajectories efficiently, achieving real-time prediction levels. Finally, we demonstrate the feasibility of the proposed method through extensive testing data, and the results are superior to many RNN-based model predictions. | en_US |