Reciprocal Consistency Prediction Network for Multi-Step Human Trajectory Prediction

Abstract

A reliable prediction of traffic participants’ trajectories is challenging for automated driving. We attempt to integrate human trajectory’s temporal and spatial reciprocal consistency into the prediction network structure. Moreover, humans are intention-driven agents, and modelling human intentions will continuously provide more accurate and detailed information for future trajectory prediction. In this paper, we propose a Reciprocal Consistency Prediction Network (RCPNet), a bi-directional prediction network consisting of the forward and the backward with a similar structure. The prior work of human trajectory prediction is mainly a unidirectional process, while RCPNet fully uses the past and future trajectory information at reverse temporal scales. By training the backward part to optimize the whole network parameters based on the reciprocal consistency, we can improve the prediction accuracy of the forward network. In particular, the framework incorporates a destination variational auto-encoder (DVAE) to estimate the target intention of humans and generate multi-modal trajectory prediction. In order to adapt to the offline prediction situation, we refer to the calculation method of correlation between two variables to present “Predictive Confidence” to clarify the reliability of offline predicted trajectories. Experimental results show that the RCPNet improves state-of-the-art performance on the SDD and ETH/UCY benchmarks.