This research presented a new approach for vehicle classification using roadside LiDAR sensor. Six features (one feature, object height profile, contains 10 sub-features) extracted from the vehicle trajectories were applied to distinguish different classes of vehicles. The vehicle classification aims to assign the objects into ten different types defined by FHWA. A database containing 1,056 manually marked samples and their corresponding pictures was provided for analysis. Those samples were collected at different scenarios (roads and intersections, different speed limits, day and night, different distance to LiDAR, etc.). Naïve Bayes, K-nearest neighbor classification, random forest (RF), and support vector machine were applied for vehicle classification. The results showed that the performance of different methods varied by class. RF has the highest overall accuracy among those investigated methods. Some types were merged together to serve different types of users, which can also improve the accuracy of vehicle classification. The validation indicated that the distance between the object and the roadside LiDAR can influence the accuracy. This research also provided the distribution of the overall accuracy of RF along the distance to LiDAR. For the VLP-16 LiDAR, to achieve an accuracy of 91.98%, the distance between the object and LiDAR should be less than 30 ft. Users can set up the location of the roadside LiDAR based on their own requirements of the classification accuracy.


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    Title :

    Automatic Vehicle Classification using Roadside LiDAR Data


    Additional title:

    Transportation Research Record


    Contributors:
    Wu, Jianqing (author) / Xu, Hao (author) / Zheng, Yichen (author) / Zhang, Yongsheng (author) / Lv, Bin (author) / Tian, Zong (author)


    Publication date :

    2019-05-05




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



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