Dynamic traffic graph based risk assessment of multivehicle lane change interaction scenarios

Vehicles' lane-changing behavior can potentially result in traffic conflicts and crash risks, particularly in scenarios with interactions among multiple vehicles. To assess the crash risk of multi-vehicle interaction lane-changing (MILC) scenarios, this study presents a dynamic traffic graph-based risk assessment method. First, a method for constructing dynamic scene graphs is proposed, along with graph-based indicators for assessing scene and scenario risks. Second, the Gaussian mixture model-latent Dirichlet allocation (GMM-LDA) algorithm is utilized to cluster risk sequences of MILC scenarios, enabling the classification of scenario risks into different levels. The method considers both dynamic and static elements in these scenarios, as well as the spatial relationships among these elements. A case study was conducted to illustrate this approach at a weaving area in China. The findings reveal that diverging segments have the highest probability of high-risk MILC scenarios. Furthermore, the likelihood of high-risk scenarios involving consecutive lane-changing maneuvers is higher compared to single lane changes. Left lane-changing behavior exhibits a higher probability of high-risk scenarios compared to right lane-changing. The proposed risk assessment method facilitates the identification and construction of high-risk MILC scenarios. It also enables the exploration of the risk evolution process.