To enable safe and efficient navigations through the urban environment, autonomous vehicles need to anticipate the future motions of the walking pedestrians who might collide with them. The dynamic and stochastic behavior characteristics of the pedestrians make the trajectory prediction challengeable for most kinematics-based approaches. This paper presents a comparative study of six state-of-the-art learning-based methods for pedestrian trajectory prediction, including Gaussian Process (GP), LSTM, GP-LSTM, Character-based LSTM, Sequence-to-Sequence (Seq2Seq), and attention-based Seq2Seq. The trajectory prediction is formulated as the regression task or sequence generation problem that predicts future trajectories based on observed trajectories. We evaluate the performance of the learning-based methods on a public real-world pedestrian dataset. To address the concern of data scarcity, we employ three forms of data augmentation (i.e., translation, rotation, and stretch) to enlarge the dataset, which produce the transformed trajectories from the original trajectories. By comparison, those learning-based approaches are ranked based on prediction accuracy from high to low as Seq2Seq, attention-based Seq2Seq, C-LSTM, LSTM, GP, and GP-LSTM. Particularly, Seq2Seq model outperforms all baseline approaches, with the mean and final point errors less than 15cm in normal scenarios when predicting 1s ahead.