Risk Assessment of Cyclists in the Mixed Traffic Based on Multilevel Graph Representation

Abstract

Accurate assessment of the cyclist risk is a crucial task for the safety system of autonomous vehicles (AVs). This paper proposes a framework for defining and evaluating cyclist risk levels, considering behavioral cues. The framework comprises three modules: the cyclist graph construction (CGC) module, the risk label generation (RLG) module, and the risk assessment (RA) module. The CGC module constructs a spatiotemporal graph model of the cyclist with both the behavioral and risk information. The RLG module leverages the graph representation method (GRM) to extract features and assigns risk labels using unsupervised learning. The RA module employs spatiotemporal graph convolutional networks (ST-GCN) to extract features from the cyclist graph. Additionally, it facilitates feature fusion through interactions between the human body and the two-wheeler and between hierarchical levels. The fused features, along with the risk labels, are used to train a classifier for the risk assessment of cyclists. The proposed framework is validated using real-world data, and the comparative results with state-of-the-art methods demonstrate the effectiveness and accuracy of the proposed approach in cyclist risk assessment in mixed traffic.