Historical Prediction Attention Mechanism based Trajectory Forecasting for Proactive Work Zone Safety in a Digital Twin Environment

Proactive safety systems aim to mitigate risks by anticipating future potential conflicts between vehicles and enabling early intervention to prevent work-zone-related crashes. This study presents an infrastructure-enabled proactive work zone safety warning system that leverages a digital twin (DT) environment, integrating real-time multisensor data, detailed high-definition (HD) maps, and a trajectory prediction model based on a historical-prediction attention mechanism (AM). Specifically, the contributions of this study are in twofolds: 1) developing an infrastructure-enabled vehicle trajectory prediction framework that leverages the historical prediction network (HPNet) model integrated with Lanelet2 HD maps and 2) developing a proactive safety warning generation application that incorporates probabilistic conflict modeling and vehicle bounding-box representations. The development and effectiveness demonstration of the presented trajectory prediction model and proactive warning system is conducted using a cosimulation environment integrating simulation of urban mobility (SUMO) and CAR learning to act (CARLA) simulators. To evaluate the accuracy of predicted trajectories (PTs), we use two standard metrics: joint average displacement error (ADE) and joint final displacement error (FDE). Our analyses revealed that the infrastructure-enabled HPNet model demonstrates superior performance on the work zone datasets generated from the cosimulation environment, achieving a minimum joint FDE of 0.3228 m and a minimum joint ADE of 0.1327 m, lower than the benchmarks on the Argoverse (minimumJoint FDE: 1.0986 m, minimumJoint ADE: 0.7612 m) and INTERACTION (minimumJoint FDE: 0.8231 m, minimumJoint ADE: 0.2548 m) datasets. In addition, our proactive safety warning generation application demonstrates its ability to issue alerts for potential vehicle conflicts.