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

2025/08/05 by Minhaj Uddin Ahmad, Mizanur Rahman, Ahmad, Minhaj Uddin +13
Engineering · #Autonomous Vehicle Technology and Safety #FOS: Computer and information sciences #Other Computer Science (cs.OH) #Robotics (cs.RO) #Traffic Prediction and Management Techniques #Traffic control and management

paper · pdf · doi:10.48550/arxiv.2508.06544

openalex publication_date 2025/08/05 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

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.

Citations

• Large Foundation Models for Trajectory Prediction in Autonomous Driving: A Comprehensive Survey
• Trajectory Prediction for Autonomous Driving: Progress, Limitations, and Future Directions
• HPNet: Dynamic Trajectory Forecasting with Historical Prediction Attention
• MSight: An Edge-Cloud Infrastructure-based Perception System for Connected Automated Vehicles
• Harnessing Digital Twin Technology for Adaptive Traffic Signal Control: Improving Signalized Intersection Performance and User Satisfaction
• Reachability-Based Confidence-Aware Probabilistic Collision Detection in Highway Driving
• Argoverse 2: Next Generation Datasets for Self-Driving Perception and Forecasting
• A Kinematic Model for Trajectory Prediction in General Highway Scenarios
• Learning Lane Graph Representations for Motion Forecasting
• VectorNet: Encoding HD Maps and Agent Dynamics from Vectorized Representation
• INTERACTION Dataset: An INTERnational, Adversarial and Cooperative moTION Dataset in Interactive Driving Scenarios with Semantic Maps
• An LSTM Network for Highway Trajectory Prediction
• Attention Is All You Need

Related

• Digital Twin-based Driver Risk-Aware Intelligent Mobility Analytics for Urban Transportation Management
• Digital Twin Technology Enabled Proactive Safety Application for Vulnerable Road Users: A Real-World Case Study
• Evaluation of Infrastructure-based Warning System on Driving Behaviors-A Roundabout Study
• RiskNet: Interaction-Aware Risk Forecasting for Autonomous Driving in Long-Tail Scenarios
• Spatiotemporal Forecasting of Incidents and Congestion with Implications for Sustainable Traffic Control