WZDrive Enhances Trajectory Prediction in Road Construction Zones
Overview
A new model, WZDrive, improves trajectory prediction in work zones using human-in-the-loop data, reducing minADE and minFDE by 7.18% and 13.18% respectively over baselines, according to arXiv Robotics research abstracts from 2026-10-12.
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Event evidence and corrections
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Reported quantity · mean minADE: 1.09 other · Basis not reported
“or. Our proposed multimodal ego-trajectory predictor, WZDrive, achieves a mean minADE (K=6) of 1.09 m and minFDE (K=6) of 2.12 m. Compared with the best-performing state-of-the-art baseline for each metric in our benchmark, WZDrive reduces minADE (K=6) by 7.18% and minFDE (K=6) by 13.18%. This research addresses a gap in autonomous-driving research on urban work zones by combining human-in-the-loop data collection wi”
Exact source · revision 1Source owner not reported
Reported quantity · mean minFDE: 2.12 other · Basis not reported
“or. Our proposed multimodal ego-trajectory predictor, WZDrive, achieves a mean minADE (K=6) of 1.09 m and minFDE (K=6) of 2.12 m. Compared with the best-performing state-of-the-art baseline for each metric in our benchmark, WZDrive reduces minADE (K=6) by 7.18% and minFDE (K=6) by 13.18%. This research addresses a gap in autonomous-driving research on urban work zones by combining human-in-the-loop data collection wi”
Exact source · revision 1Source owner not reported
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- arXiv Robotics — research abstractsTrajectory Prediction in Road Construction Zones Using Human-in-the-Loop Data
Researchers develop WZDrive, a multimodal model that improves trajectory prediction in work zones using human-in-the-loop data, reducing minADE by 7.18% and minFDE by 13.18% over baselines.
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