This study introduces a comparative evaluation and ranking system for low-level automated vehicles (LAVs) using real-world trajectory data. In the framework of the proposed evaluation system, different LA Vs' behaviors are first modeled based on their trajectory data. Test scenarios are designed according to the specific test goals. Subsequently, simulation tests are then conducted using extracted vehicle behavior models in the designed traffic scenario. Several measurements in terms of safety, environmental impact, and mobility efficiency are used to evaluate and rank the performance of different types of LA Vs. In numerical studies, the Long Short-Term Memory (LSTM) models are used to extract LA VS’ behavior features from the OpenACC dataset, demonstrating high accuracy in vehicle motion prediction and specificity among different types of LA Vs. Simulation tests on a real-world road corridor validate the applicability of the proposed framework. As more data sources on LAVs become available, the proposed evaluation and ranking system has the potential to inform customers and government agencies during decision-making.


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    Title :

    Trajectory-Based Performance Ranking System of Low-Level Automated Vehicles*


    Contributors:
    Ma, Chengyuan (author) / Li, Xiaopeng (author) / Ma, Ke (author) / Zhang, Peng (author) / Long, Keke (author) / Chen, Sikai (author)


    Publication date :

    2023-09-24


    Size :

    603454 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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