  As a vital component in autonomous driving, accurate trajectory prediction
effectively prevents traffic accidents and improves driving efficiency. To
capture complex spatial-temporal dynamics and social interactions, recent
studies developed models based on advanced deep-learning methods. On the other
hand, recent studies have explored the use of deep generative models to further
account for trajectory uncertainties. However, the current approaches
demonstrating indeterminacy involve inefficient and time-consuming practices
such as sampling from trained models. To fill this gap, we proposed a novel
model named Graph Recurrent Attentive Neural Process (GRANP) for vehicle
trajectory prediction while efficiently quantifying prediction uncertainty. In
particular, GRANP contains an encoder with deterministic and latent paths, and
a decoder for prediction. The encoder, including stacked Graph Attention
Networks, LSTM and 1D convolutional layers, is employed to extract
spatial-temporal relationships. The decoder is used to learn a latent
distribution and thus quantify prediction uncertainty. To reveal the
effectiveness of our model, we evaluate the performance of GRANP on the highD
dataset. Extensive experiments show that GRANP achieves state-of-the-art
results and can efficiently quantify uncertainties. Additionally, we undertake
an intuitive case study that showcases the interpretability of the proposed
approach. The code is available at https://github.com/joy-driven/GRANP.
