Accurately identifying the intrinsic model of Adaptive Cruise Control has the potential to enhance the prediction of automated car-following behavior, helping vehicles' decision-making and contributing to safer and more efficient traffic flows. Moreover, white box models offer an analytical base for evaluating the impact of automated driving functions on macroscopic traffic dynamics, consequently aiding the management of the whole intelligent transportation system. Many existing system identification techniques have been applied to automated vehicles. However, most of these studies focus on identifying parameters for models of a fixed prototype. Their reliance on accurate estimation of state time derivatives prevents their real applications, challenged by low sampling rates, noisy measurements, and limited observation periods. In contrast, the Koopman operator learning framework presents a promising improvement that can identify the nonlinear evolutionary properties of continuous-time systems. In this study, we apply Koopman-based methods to data driven Adaptive Cruise Control model identification. Additionally, as the challenge remains in establishing a practical relationship between identification accuracy and sampling rate, we numerically compared the performance of three Koopman-based learning frameworks, finite-difference, Koopman-logarithm, and a newly devised resolvent-type method, with that of a commonly used offline simulation-based batch optimization approach. We introduce a novel modification to the resolvent-type method, and the experimental results demonstrate its state of the art performance, particularly in identifying the potential existence of parametric noise at lower sampling rates.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Koopman-Based Data-Driven Techniques for Adaptive Cruise Control System Identification


    Contributors:
    Meng, Yiming (author) / Li, Hangyu (author) / Ornik, Melkior (author) / Li, Xiaopeng (author)


    Publication date :

    2024-09-24


    Size :

    1263351 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Koopman Operator-Based Data-Driven Identification of Tethered Subsatellite Deployment Dynamics

    Manzoor, Waqas A. / Rawashdeh, Samir / Mohammadi, Alireza | ASCE | 2023


    Nonlinear Data-Driven Predictive Control for Mixed Platoons Based on Koopman Operator

    Li, Shuai / Chen, Chaoyi / Zheng, Haotian et al. | IEEE | 2024


    Vehicle-following cruise risk identification method for adaptive cruise control system of vehicle

    YU WANGYANG / GUO QI / WANG XIAOMING et al. | European Patent Office | 2022

    Free access