Digital-Twin Losses for Lane-Compliant Trajectory Prediction at Urban Intersections

TL;DR

This paper introduces a digital twin-driven V2X trajectory prediction model that improves safety and rule compliance at urban intersections by combining cooperative perception with a novel twin loss, achieving accurate and safer predictions.

Contribution

The paper presents a new training approach using a twin loss that encodes infrastructure constraints and safety rules, enhancing trajectory prediction safety in V2X systems.

Findings

Reduces traffic rule violations and collisions in predictions.

Maintains prediction accuracy comparable to existing methods.

Operates in real-time for urban intersection scenarios.

Abstract

Accurate and safety-conscious trajectory prediction is a key technology for intelligent transportation systems, especially in V2X-enabled urban environments with complex multi-agent interactions. In this paper, we created a digital twin-driven V2X trajectory prediction pipeline that jointly leverages cooperative perception from vehicles and infrastructure to forecast multi-agent motion at signalized intersections. The proposed model combines a Bi-LSTM-based generator with a structured training objective consisting of a standard mean squared error (MSE) loss and a novel twin loss. The twin loss encodes infrastructure constraints, collision avoidance, diversity across predicted modes, and rule-based priors derived from the digital twin. While the MSE term ensures point-wise accuracy, the twin loss penalizes traffic rule violations, predicted collisions, and mode collapse, guiding the model toward scene-consistent and safety-compliant predictions. We train and evaluate our approach on real-world V2X data sent from the intersection to the vehicle and collected in urban corridors. In addition to standard trajectory metrics (ADE, FDE), we introduce ITS-relevant safety indicators, including infrastructure and rule violation rates. Experimental results demonstrate that the proposed training scheme significantly reduces critical violations while maintaining comparable prediction accuracy and real-time performance, highlighting the potential of digital twin-driven multi-loss learning for V2X-enabled intelligent transportation systems.