Highlights To address the issues of inconsistency and loss of abrupt changes that existing methods fail to solve, we propose a bilevel optimization method. For a thorough resolution of the data inconsistency, we utilize the l 2 trend filter to merge the raw position and velocity data. To preserve abrupt changes in trajectory, we utilize the l 1 trend filter to make the trajectory physically feasible and preserve driving characteristics. To validate the effectiveness of the proposed method, we design experiments and calculate the metrics to verify it. We discuss the trend filtering order and demonstrate that the optimization order we proposed is optimal through experiments. We also calculate metrics such as RMSE and jerk value and also compare the other trajectory enhancement methods and the result of the trajectory prediction model before and after our processing method. Our method achieves superior performance on most of them. This approach is essential to ensure the reliability and accuracy of the results. We provide a detailed explanation of the algorithm workflow and present examples. In our work, we take the latest trajectory dataset: CitySim dataset as an example to show how our method processes data and its effectiveness in handling trajectory data. It can serve as a reference and enable researchers to easily understand the method. What’s more, the processing methods proposed are also applicable to other trajectory datasets.

    Abstract Trajectory data play a vital role in the field of traffic research such as vehicle safety, traffic flow, and intelligent vehicles. The quality of trajectory data will determine the safety effectiveness of both research and practical applications. Effectively filtering out noise and errors from trajectory data is crucial for improving data quality and further research. However, most enhancement methods only focus on the smoothness of trajectory but overlook abrupt changes. The processed trajectory still exist issues such as incomplete elimination of inconsistency and loss of driving characteristics. In this paper, we propose a generic optimization-based enhancement method to address the issues above. We propose a bilevel optimization method combined with l 1 and l 2 trend filter. First, we design a l 2 trend filter to fuse raw trajectory data and eliminate the inconsistency. Next, we utilize the l 1 trend filter to optimize the data, ensuring physical feasibility and preserving abrupt changes (emergency driving characteristics). Then, we validate the effectiveness of the method through evaluation metrics and prediction models. The generic optimization-based enhancement method proposed in this paper ensures the safety of both research and application by providing high-quality trajectory data.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    A generic optimization-based enhancement method for trajectory data: Two plus one


    Beteiligte:
    Zhu, Feng (Autor:in) / Chang, Cheng (Autor:in) / Li, Zhiheng (Autor:in) / Li, Boqi (Autor:in) / Li, Li (Autor:in)


    Erscheinungsdatum :

    2024-02-28




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




    Trajectory Optimization for a Generic Hypersonic Vehicle

    Keshmiri, Shahriar / Colgren, Richard / Mirmirani, Maj | AIAA | 2006


    Improved Data-Driven Trajectory Optimization Method Utilizing Deep Trajectory Generation

    Gui, Xuhao / Zhang, Junfeng / Tang, Xinmin et al. | AIAA | 2024


    Learning-based Aircraft Trajectory Planning Enhancement

    Alizadeh, Ali / Humaira, Nazmia / Koyuncu, Emre | AIAA | 2019


    Learning-based Aircraft Trajectory Planning Enhancement

    Alizadeh, Ali / Humaira, Nazmia / Koyuncu, Emre | TIBKAT | 2019


    Heurestic Optimization-Based Trajectory Optimization

    Chai, Runqi / Chen, Kaiyuan / Cui, Lingguo et al. | Springer Verlag | 2023