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

TL;DR

This paper introduces a Digital Twin-based proactive safety warning system for work zones, utilizing a novel trajectory prediction model with historical attention to improve early conflict detection and crash prevention.

Contribution

It presents a new trajectory prediction model with historical attention in a Digital Twin environment, enhancing early warning accuracy for work zone vehicle conflicts.

Findings

Superior trajectory prediction accuracy with low FDE and ADE metrics.

Effective early warning alerts for potential vehicle conflicts.

Demonstrated system performance in a co-simulation environment.

Abstract

Proactive safety systems aim to mitigate risks by anticipating 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 environment, integrating real-time multi-sensor data, detailed High-Definition (HD) maps, and a historical prediction attention mechanism-based trajectory prediction model. Using a co-simulation environment that combines Simulation of Urban MObility (SUMO) and CAR Learning to Act (CARLA) simulators, along with Lanelet2 HD maps and the Historical Prediction Network (HPNet) model, we demonstrate effective trajectory prediction and early warning generation for vehicle interactions in freeway work zones. To evaluate the accuracy of predicted trajectories, we use two standard metrics: Joint Average Displacement Error (ADE) and Joint Final Displacement Error (FDE). Specifically, the infrastructure-enabled HPNet model demonstrates superior performance on the work-zone datasets generated from the co-simulation environment, achieving a minimum Joint FDE of 0.3228 meters and a minimum Joint ADE of 0.1327 meters, lower than the benchmarks on the Argoverse (minJointFDE: 1.0986 m, minJointADE: 0.7612 m) and Interaction (minJointFDE: 0.8231 m, minJointADE: 0.2548 m) datasets. In addition, our proactive safety warning generation application, utilizing vehicle bounding boxes and probabilistic conflict modeling, demonstrates its capability to issue alerts for potential vehicle conflicts.