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Predicting surrounding vehicle behaviors are critical to autonomous vehicleswhen negotiating in multi-vehicle interaction scenarios. Most existingapproaches require tedious training process with large amounts of data and mayfail to capture the propagating uncertainty in interaction behaviors. Themulti-vehicle behaviors are assumed to be generated from a stochastic process.This paper proposes an attentive recurrent neural process (ARNP) approach toovercome the above limitations, which uses a neural process (NP) to learn adistribution of multi-vehicle interaction behavior. Our proposed model inheritsthe flexibility of neural networks while maintaining Bayesian probabilisticcharacteristics. Constructed by incorporating NPs with recurrent neuralnetworks (RNNs), the ARNP model predicts the distribution of a target vehicletrajectory conditioned on the observed long-term sequential data of allsurrounding vehicles. This approach is verified by learning and predictinglane-changing trajectories in complex traffic scenarios. Experimental resultsdemonstrate that our proposed method outperforms previous counterparts in termsof accuracy and uncertainty expressiveness. Moreover, the meta-learninginstinct of NPs enables our proposed ARNP model to capture global informationof all observations, thereby being able to adapt to new targets efficiently.

Probabilistic Trajectory Prediction for Autonomous Vehicles with Attentive Recurrent Neural Process