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.
Highlights
Multi-object interaction on the traffic scene leads to change in objects’ states and responsive actions
The visibility of an object frequently varies over time, e.g., it changes from visible to invisible, which influences the performance of motion prediction
We propose to reason the visibility using a Hierarchical And-Or Graph (H-AOG), which represents the causal relations between visibility fluents and multi-object interactions
Summary
Multi-object interaction on the traffic scene leads to change in objects’ states and responsive actions. The visibility of an object frequently varies over time, e.g., it changes from visible to invisible, which influences the performance of motion prediction. It is hard to predict the motion of occluded object in most state-of-the-art trackers. If it has no ‘‘cognition’’ of the existence of occluded object, its planning would be full of uncertainty and risk. Modeling interactions between multiple objects on the traffic scene is rather significant, helping to prejudge the occurrence of abnormal events and improve driving safety. Many recent work focuses on the interaction modeling by various ways [1]–[3], but it is still a challenging task until now
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