A Multi-Level Movement Intention Inference Approach for an Urban Evasive Target With Unknowable Destinations

Movement intention inference for non-cooperative evasive targets in urban environments is difficult due to the lack of a priori knowledge of the possible target's movement intentions set. To solve this problem, this paper proposes a multi-level intention inference approach to construct and infer the movement intentions of a non-cooperative evasive target in urban environments. Firstly, to reasonably represent the possible movement intentions of the target, we decompose the target's movement intention into different regions at multiple levels according to the urban environment and the location of the target. At the same time, we divide the region at each level into several different sub-regions to represent the different movement intentions of the target at that level. Thus, the possible target's movement intentions are constructed as different regions at different levels where the target intends to go. Secondly, Convolutional Neural Network (CNN)-based intention inference models are developed, which can fuse the urban environment information and the observed target's trajectory to infer the target's movement intentions. Extensive simulation experiments results show that our proposed multi-level intention inference models can accurately and timely (with 79% accuracy and 84% timeliness) infer the region where the target intends to go when the possible destinations of the target are unknowable and maintain a robust inference performance when the target's behavior changes.