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arXiv · 2011.08722

RAIST: Learning Risk Aware Traffic Interactions via Spatio-Temporal Graph Convolutional Networks

Videsh Suman·Phu Pham·Aniket Bera
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Abstract

A key aspect of driving a road vehicle is to interact with other road users, assess their intentions and make risk-aware tactical decisions. An intuitive approach to enabling an intelligent automated driving system would be incorporating some aspects of human driving behavior. To this end, we propose a novel driving framework for egocentric views based on spatio-temporal traffic graphs. The traffic graphs model not only the spatial interactions amongst the road users but also their individual intentions through temporally associated message passing. We leverage a spatio-temporal graph convolutional network (ST-GCN) to train the graph edges. These edges are formulated using parameterized functions of 3D positions and scene-aware appearance features of road agents. Along with tactical behavior prediction, it is crucial to evaluate the risk-assessing ability of the proposed framework. We claim that our framework learns risk-aware representations by improving on the task of risk object identification, especially in identifying objects with vulnerable interactions like pedestrians and cyclists.

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Videsh Suman, Phu Pham, Aniket Bera. 2020-11-17. RAIST: Learning Risk Aware Traffic Interactions via Spatio-Temporal Graph Convolutional Networks. https://arxiv.org/abs/2011.08722

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