Vehicle trajectory data is in high demand for transportation research due to its rich detail. Lane information is an important aspect of trajectory data, which is typically obtained using sensors such as cameras or LiDAR, which are able to extract road lane features. However, some sensors for trajectory tracking (e.g., MMW radar sensors) are unable to provide lane information. Vehicle detection and trajectory tracking systems based on these sensing technologies can integrate with lane information through manual calibration during initial installation, but this process is labor-intensive and requires frequent recalibration as the sensors gradually become deviated by wind and vibration. This has posed a challenge for trajectory tracking, particularly for real-time applications. To address this challenge, this paper proposes a method for estimating lane-level road geometrics using microscopic trajectory data. The method involves segmenting the trajectory points using direction vectors and clustering them and fitting a series of cluster center points. The mean error (ME) of the distance between the estimated result and the ground truth reference is used to measure the accuracy of the lane-level road geometrics estimation in different conditions. Results show that when the average trajectory data includes at least approximately 30 points per meter in each segment, the ME is always less than 0.1 m. The method has also been tested on MMW wave radar data and found to be effective. This demonstrates the feasibility of our approach for dynamic calibration of road alignment in vehicle trajectory tracking systems.


    Zugriff

    Download


    Exportieren, teilen und zitieren



    Titel :

    Automated Lane-Level Road Geometry Estimation Using Microscopic Trajectory Data


    Beteiligte:
    Junhua Wang (Autor:in) / Chengmin Li (Autor:in) / Ting Fu (Autor:in) / Lanfang Zhang (Autor:in) / Anae Sobhani (Autor:in) / Jiangtian Xue (Autor:in)


    Erscheinungsdatum :

    2023




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Unbekannt




    Lane departure detection for improved road geometry estimation

    Schon, T.B. / Eidehall, A. / Gustafsson, F. | IEEE | 2006


    Lane Departure Detection for Improved Road Geometry Estimation

    Schon, T. B. / Eidehall, A. / Gustafsson, F. | British Library Conference Proceedings | 2006


    Road lane trajectory estimation using yaw rate gyroscopes for intelligent vehicle control

    Barber, P.A. / King, P. / Richardson, M. | Tema Archiv | 1998


    Generating lane level road data from vehicle trajectories using Kernel Density Estimation

    Uduwaragoda, E.R.I.A.C.M. / Perera, A.S. / Dias, S.A.D. | IEEE | 2013


    Trajectory Planning for Automated Lane-Change on a Curved Road for Collision Avoidance

    Zhong, Hong / Zhuang, Weichao / Qian, Yahui et al. | SAE Technical Papers | 2019