Q-EANet: Implicit social modeling for trajectory prediction via experience-anchored queries

Accurately predicting the future trajectory and behavior of traffic participants is crucial for the maneuvers of self-driving vehicles. Many existing works employed a learning-based “encoder-interactor-decoder” structure, but they often fail to clearly articulate the relationship between module selections and real-world interactions. As a result, these approaches tend to rely on a simplistic stacking of attention modules. To address this issue, a trajectory prediction network (Q-EANet) is presented in this study, which integrates GRU encoders, MLPs and attention modules. By introducing a new explanatory rule, it makes a contribution to interpretable modeling, models the entire trajectory prediction process via an implicit social modeling formula. Inspired by the anchoring effect in decision psychology, the prediction task is formulated as an information query process that occurs before traffic participants make decisions. Specifically, Q-EANet uses GRUs to encode features and utilizes attention modules to aggregates interaction information for generating the target trajectory anchors. Then, queries are introduced for further interaction. These queries, along with the trajectory anchors with added Gaussian noise, are then processed by a GRU-based decoder. The final prediction results are obtained through a Laplace MDN. Experimental results on the several benchmarks demonstrate the effectiveness of Q-EANet in trajectory prediction tasks. Compared to the existing works, the proposed method achieves state-of-the-art performance with only simple module design. The code for this work is publicly available at https://github.com/Jctrp/socialea.