Traffic situation awareness in urban environments is an essential technology for applications such as collision detection and congestion prediction. Correlational research has received much attention due to the significance of traffic security and intelligence. On this issue, the combination of multiple sensors (e.g., lidar, radar, monocular, and binocular cameras) promotes the perception and decision-making process of vehicles, based on an integrated information about the environment. The paper proposed an end-to-end algorithm utilizing multi-sensor data fusion to forecast collision risk of vehicles. The algorithm was implemented in two stages. In the first stage, both radar and camera data was taken as input. Then, motion features of vehicles were extracted through object detection and tracking algorithms. Afterwards, we performed trajectory prediction and motion state estimation using data obtained from last step. During the process, collision risk for each vehicle was estimated according to indicators like time-to-collision (TTC) and minimum-future-spacing (MFS). In the second stage, the extended Kalman filter (EKF) was applied to smooth original predictions, thus improving the degree of confidence in the data. In the algorithm implementation, algorithms of object detection, tracking, and trajectory prediction were connected with necessary modifications, reaching a balance between accuracy and efficiency. The algorithm was tested with nuScenes data set to prove its effectiveness in actual traffic scenarios. Experiments showed that our algorithm could output acceptable collision risk of vehicles over time, thus providing information for further inference about traffic situations.


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    Titel :

    Collision Risk Prediction for Vehicles with Sensor Data Fusion through a Machine Learning Pipeline


    Beteiligte:
    Jiang, Yongpeng (Autor:in) / Hu, Jianming (Autor:in) / Liu, Hantao (Autor:in)

    Kongress:

    International Conference on Transportation and Development 2022 ; 2022 ; Seattle, Washington



    Erscheinungsdatum :

    2022-08-31




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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