Causal Reasoning in Multi-Object Interaction on the Traffic Scene: Occlusion-Aware Prediction of Visibility Fluent

Abstract

Occlusion caused by multi-object interaction makes the traffic scene understanding intractable. In this paper, we focus on predicting the visibility status of vehicle in the framework of causality perception. The visibility fluent is employed to present the varying state of an object, involving visible and occluded. We introduce a probabilistic grammar model, named Hierarchical And-Or Graph (H-AOG), to construct the causal relations between fluents and actions. It consists of a Causal And-Or Graph (C-AOG) module and an Action And-Or Graph (A-AOG) module. An influence field is constructed by the polar coordinate transformation to model interactions in the A-AOG module. This method interprets the occurrence of occlusion due to multi-vehicle interaction. We evaluate our approach on both synthetic data and real data from the KITTI dataset. Compared to the state-of-the-art models like LSTM/GRU, it proves to achieve a promising accuracy in prediction of objects' visibility states and have better generalization on real data.