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Related Concept Videos

Transformers with Off-Nominal Turns Ratios01:25

Transformers with Off-Nominal Turns Ratios

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In scenarios involving parallel transformers with disparate ratings, developing per-unit models requires accommodating off-nominal turns ratios. This situation arises when the selected base voltages are not proportional to the transformer’s voltage ratings. Consider a transformer where the rated voltages are related by the term a. If the chosen voltage bases satisfy a relationship involving term b, term c is defined as the ratio of these bases. This ratio is then substituted into the...
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Transformers in distribution systems can be broadly categorized into distribution substation transformers and other distribution transformers. They are crucial for stepping down high transmission voltages to levels suitable for distribution and end-user applications.
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When a car traverses a curved road, its motion can be elucidated by breaking it down into tangential and normal components. The car-centric coordinates attached to the vehicle move with it.
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  11. Transformer-based Vehicle-trajectory Prediction At Urban Low-speed T-intersection.
  1. Home
  3. Research Domains
  5. Engineering
  7. Communications Engineering
  9. Data Communications
  11. Transformer-based Vehicle-trajectory Prediction At Urban Low-speed T-intersection.

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Transformer-Based Vehicle-Trajectory Prediction at Urban Low-Speed T-Intersection.

Jae Kwan Lee1

  • 1Department of Highway & Transportation Research, Korea Institute of Civil Engineering and Building Technology, 283 Goyangdae-ro, Goyang-si 10223, Republic of Korea.

Sensors (Basel, Switzerland)
|July 30, 2025

View abstract on PubMed

Summary
This summary is machine-generated.

This study optimized transformer models for vehicle trajectory prediction in urban intersections. Lightweight models with specific input/output lengths improve accuracy for edge computing and accident analysis.

Keywords:
T-intersectionmicroscopic traffic simulationtrajectory forecastingtransformer model

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Area of Science:

  • Artificial Intelligence
  • Computer Vision
  • Transportation Engineering

Background:

  • Transformer models excel at trajectory prediction but require significant computational resources and struggle with long-term predictions.
  • Vehicle trajectory prediction is crucial for intelligent transportation systems and autonomous driving, especially in complex urban environments.

Purpose of the Study:

  • To develop and optimize a lightweight transformer model for accurate vehicle trajectory prediction in low-speed urban T-intersections.
  • To identify optimal model parameters, including loss function and sequence length, for enhanced prediction accuracy and efficiency.
  • To evaluate the model's generalization capabilities in atypical driving scenarios and assess the impact of additional features.

Main Methods:

  • Generated microscopic traffic simulation data for training and validation, including atypical scenarios.
urban driving
urban driving scenarios
vehicle-trajectory
  • Explored various loss functions, settling on smooth L1 loss for optimal performance.
  • Examined input/output sequence lengths, determining 1s input and 3s output as optimal.
  • Evaluated model generalization using diverse driving-characteristic data.

    • Main Results:

    • The smooth L1 loss function significantly improved prediction accuracy.
    • An optimal input sequence length of 1 second and an output sequence length of 3 seconds were identified.
    • Enhancing model structure proved more effective than diversifying training data for generalization in atypical situations.
    • Incorporating additional features like speed variation reduced model accuracy by approximately 21%.

    Conclusions:

    • Optimized transformer models offer a viable solution for lightweight trajectory prediction in edge computing environments.
    • The findings support the development of trajectory prediction and accident analysis systems for various urban driving scenarios.
    • Focusing on model architecture improvements is key to achieving robust performance in complex and atypical driving conditions.