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

• URL: http://arxiv.org/abs/2603.05546v1
• Date: Wed, 04 Mar 2026 20:33:25 GMT
• Title: Digital-Twin Losses for Lane-Compliant Trajectory Prediction at Urban Intersections
• Abstract summary: We create a digital twin-driven V2X trajectory prediction pipeline.<n>We train and evaluate our approach on real-world V2X data sent from the intersection to the vehicle.
• Score: 7.662328004582307
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• A Neural Network Based Teleoperation for Remote Controlled Vehicles [98.38733381483934]
  Direct teleoperation of vehicles faces critical technical bottlenecks.<n> communication latency and the operator's inability to physically perceive unmodeled environmental disturbances.<n>We propose a tailored unilateral teleoperation framework.
  arXiv  Detail & Related papers  (2026-08-11T01:51:08Z)
• PRISA: Proactive Infrastructure LiDAR Framework for Intersection Safety Assessment [3.6737243445877663]
  Urban intersections are among the most hazardous locations in road networks, posing significant risks to vehicles and vulnerable road users (VRUs) such as pedestrians and cyclists.<n>We present PRISA, a modular infrastructure LiDAR framework leveraging privacy-preserving, low-light-robust roadside sensors for long-term traffic observation and real-time risk detection at the edge.<n> PRISA is evaluated on the public R-LiViT dataset and deployed on an NVIDIA Jetson AGX Thor at a live signalized intersection in Chattanooga, Tennessee.
  arXiv  Detail & Related papers  (2026-07-17T17:35:23Z)
• Spatiotemporal Semantic V2X Framework for Cooperative Collision Prediction [5.862522659881676]
  Transportation Systems (ITS) demand real-time collision prediction to ensure road safety and reduce accident severity.<n> Conventional approaches rely on transmitting raw or high-dimensional sensory data from roadside units (RSUs) to vehicles.<n>We propose a semantic V2X framework in which RSU-mounted video cameras generate semantic embeddings of future frames.
  arXiv  Detail & Related papers  (2026-01-23T23:02:04Z)
• Towards Intelligent Transportation with Pedestrians and Vehicles In-the-Loop: A Surveillance Video-Assisted Federated Digital Twin Framework [62.47416496137193]
  We propose a surveillance video assisted federated digital twin (SV-FDT) framework to empower ITSs with pedestrians and vehicles in-the-loop.<n>The architecture consists of three layers: (i) the end layer, which collects traffic surveillance videos from multiple sources; (ii) the edge layer, responsible for semantic segmentation-based visual understanding, twin agent-based interaction modeling, and local digital twin system (LDTS) creation in local regions; and (iii) the cloud layer, which integrates LDTSs across different regions to construct a global DT model in realtime.
  arXiv  Detail & Related papers  (2025-03-06T07:36:06Z)
• Knowledge-Informed Multi-Agent Trajectory Prediction at Signalized Intersections for Infrastructure-to-Everything [7.452533291998081]
  We introduce an Infrastructure-to-Everything (I2X) collaborative prediction scheme.<n>In this scheme, roadside units (RSUs) independently forecast the future trajectories of all vehicles.<n>We propose I2XTraj, a dedicated infrastructure-based trajectory prediction model.
  arXiv  Detail & Related papers  (2025-01-23T08:23:45Z)
• A Multi-Loss Strategy for Vehicle Trajectory Prediction: Combining Off-Road, Diversity, and Directional Consistency Losses [68.68514648185828]
  Trajectory prediction is essential for the safety and efficiency of planning in autonomous vehicles.<n>Current models often fail to fully capture complex traffic rules and the complete range of potential vehicle movements.<n>This study introduces three novel loss functions: Offroad Loss, Direction Consistency Error, and Diversity Loss.
  arXiv  Detail & Related papers  (2024-11-29T14:47:08Z)
• Edge-Assisted ML-Aided Uncertainty-Aware Vehicle Collision Avoidance at Urban Intersections [12.812518632907771]
  We present a novel framework that detects preemptively collisions at urban crossroads. We exploit the Multi-access Edge Computing platform of 5G networks.
  arXiv  Detail & Related papers  (2024-04-22T18:45:40Z)
• Context-Aware Target Classification with Hybrid Gaussian Process prediction for Cooperative Vehicle Safety systems [2.862606936691229]
  Vehicle-to-Everything (V2X) communication has been proposed as a potential solution to improve the robustness and safety of autonomous vehicles. Cooperative Vehicle Safety (CVS) applications are tightly dependent on the reliability of the underneath data system. We propose a Context-Aware Target Classification (CA-TC) module and a hybrid learning-based predictive modeling technique for CVS systems.
  arXiv  Detail & Related papers  (2022-12-24T22:03:08Z)
• Online Metro Origin-Destination Prediction via Heterogeneous Information Aggregation [99.54200992904721]
  We propose a novel neural network module termed Heterogeneous Information Aggregation Machine (HIAM) to jointly learn the evolutionary patterns of OD and DO ridership. An OD modeling branch estimates the potential destinations of unfinished orders explicitly to complement the information of incomplete OD matrices. A DO modeling branch takes DO matrices as input to capture the spatial-temporal distribution of DO ridership. Based on the proposed HIAM, we develop a unified Seq2Seq network to forecast the future OD and DO ridership simultaneously.
  arXiv  Detail & Related papers  (2021-07-02T10:11:51Z)
• Euro-PVI: Pedestrian Vehicle Interactions in Dense Urban Centers [126.81938540470847]
  We propose Euro-PVI, a dataset of pedestrian and bicyclist trajectories. In this work, we develop a joint inference model that learns an expressive multi-modal shared latent space across agents in the urban scene. We achieve state of the art results on the nuScenes and Euro-PVI datasets demonstrating the importance of capturing interactions between ego-vehicle and pedestrians (bicyclists) for accurate predictions.
  arXiv  Detail & Related papers  (2021-06-22T15:40:21Z)
• Congestion-aware Multi-agent Trajectory Prediction for Collision Avoidance [110.63037190641414]
  We propose to learn congestion patterns explicitly and devise a novel "Sense--Learn--Reason--Predict" framework. By decomposing the learning phases into two stages, a "student" can learn contextual cues from a "teacher" while generating collision-free trajectories. In experiments, we demonstrate that the proposed model is able to generate collision-free trajectory predictions in a synthetic dataset.
  arXiv  Detail & Related papers  (2021-03-26T02:42:33Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.