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

• URL: http://arxiv.org/abs/2011.08722v3
• Date: Sun, 6 Aug 2023 17:14:33 GMT
• Title: RAIST: Learning Risk Aware Traffic Interactions via Spatio-Temporal Graph Convolutional Networks
• Authors: Videsh Suman and Phu Pham and Aniket Bera
• Abstract summary: A key aspect of driving a road vehicle is to interact with other road users, assess their intentions and make risk-aware tactical decisions. We propose a novel driving framework for egocentric views based on traffic graphs. We claim that our framework learns risk-aware representations by improving on the task of risk object identification.
• Score: 19.582873794287632
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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