  Predicting surrounding vehicle behaviors are critical to autonomous vehicles
when negotiating in multi-vehicle interaction scenarios. Most existing
approaches require tedious training process with large amounts of data and may
fail to capture the propagating uncertainty in interaction behaviors. The
multi-vehicle behaviors are assumed to be generated from a stochastic process.
This paper proposes an attentive recurrent neural process (ARNP) approach to
overcome the above limitations, which uses a neural process (NP) to learn a
distribution of multi-vehicle interaction behavior. Our proposed model inherits
the flexibility of neural networks while maintaining Bayesian probabilistic
characteristics. Constructed by incorporating NPs with recurrent neural
networks (RNNs), the ARNP model predicts the distribution of a target vehicle
trajectory conditioned on the observed long-term sequential data of all
surrounding vehicles. This approach is verified by learning and predicting
lane-changing trajectories in complex traffic scenarios. Experimental results
demonstrate that our proposed method outperforms previous counterparts in terms
of accuracy and uncertainty expressiveness. Moreover, the meta-learning
instinct of NPs enables our proposed ARNP model to capture global information
of all observations, thereby being able to adapt to new targets efficiently.
