Data Based Parameter Setting Method for Adaptive Cruise Control

Chen, Jie; Yu, Guizhen; Yan, Xu

doi:10.1109/access.2020.2967321

Cited by 2 publications

(1 citation statement)

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“…Another work using learningbased methods such as recurrent neural networks (RNN) to handle interference vehicles during cut-in is presented in [41]. Chen et al analyzed the trade-off ACC system such as safety, comfort, and traffic efficiency based on large amounts of real road data and it bring a guide for engineers to set the ACC parameters based on a data-driven method [42]. The other work using data-driven on ACC are presented by [43], [44].…”

Section: Introductionmentioning

confidence: 99%

Adaptive Cruise Control With Gain Scheduling Technique Under Varying Vehicle Mass

Hidayatullah

Juang

2021

IEEE Access

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Today, advanced driver-assistance systems (ADAS) come up with different abilities. One of them is the adaptive cruise control (ACC) system. The ACC system is a continuation of research on cruise control (CC) system, which integrates spacing control with the existing velocity control on the CC system. The vehicles with an ACC system guarantee traffic safety while at the same time ensure a well-driving sense. Many studies have demonstrated numerous control techniques applied as ACC controllers to accomplish uncertainty and perturbation issues. Nevertheless, most of the existing papers assumed the model vehicle dynamics as a linear time-invariant (LTI) system while designing the ACC controller. This paper proposed an ACC controller using the gain scheduling technique to deal with the model vehicle dynamics as a linear parameter varying (LPV) system. The passenger vehicle's mass varies during ACC operation depending on how many passengers or loads on the vehicle's trunks. Later, the vehicle's mass is estimated by recursive least square (RLS) with a forgetting factor. Then, the disk margin is utilized to provide the high-level robustness at each operating or ''frozen'' point. The robustness performance will be analyzed using the worst-case gain metric while the uncertainty is modeled by integral quadratic constraints (IQC). The LPV system behavior, such as the rate vehicle's mass, is also considered in the analysis. The effectiveness algorithm is validated through joint simulation between Matlab/Simulink and PreScan. The last, the comparison performance between gain scheduling and fixed gain ACC controller is evaluated. INDEX TERMSAdaptive cruise control, advanced driver assistance systems, cruise control, gain scheduling controller, linear parameter varying system, spacing control. NOMENCLATURE B r,max Maximum brake pressure. T h,max Maximum throttle. m, m Actual and estimated mass of the vehicle. δ safe , δ rel , δ 0 Safe distance, relative distance, and minimum distance between vehicles. v ref , v x,h Longitudinal reference velocity and actual of velocity of host vehicle. t g Time gap between vehicles. X a Position of target or other vehicles in front of the host vehicle. X hPosition of the host vehicle.The associate editor coordinating the review of this manuscript and approving it for publication was Michail Makridis .

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Section: Introductionmentioning

confidence: 99%