A Personalized Human Drivers’ Risk Sensitive Characteristics Depicting Stochastic Optimal Control Algorithm for Adaptive Cruise Control

Jiang, Jiwan; Ding, Fan; Zhou, Yang; Wu, Jin−Jei; Tan, Huachun

doi:10.1109/access.2020.3015349

Cited by 11 publications

(7 citation statements)

References 35 publications

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“…The system utilizes a range sensor to detect the preceding vehicle and determines the speed of the lead vehicle and relative distance between two vehicles in order to set the vehicle speed [2]. In one particular paper by Jiang et al, authors develop a stochastic optimal control algorithm that can diversify the controller behavior of the ACC system based on driver characteristics [5]. Future trends go towards highly automated and cooperative systems with no driver intervention.…”

Section: Figure 1 Distribution Of Causes For Vehicle Accidents In Turkey From 2009 To 2018mentioning

confidence: 99%

Particle Swarm Optimization Method Based Controller Tuning for Adaptive Cruise Control Application

Ozkaya¹,

Arslan²,

Şen³

2021

Gazi University Journal of Science

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Major developments in relevant technology make the advanced driver assistance systems and autonomous driving functions more attainable. Thus, conventional practices being applied in vehicle production evolves towards highly automated, safer, and more comfortable vehicles. Although advanced driver assistance systems and autonomous driving functions have many advantages, such as enhanced driver convenience, increased comfort, and fuel economy; concerns related to safety still exist. For instance, failures related to sensors or algorithms being used can lead to critical problems. Therefore, controller algorithms should be more robust and welloptimized in order to eliminate these safety issues. This requires the implementation of automated optimization algorithms for the controller parameter tuning process. The main objective of this study is to optimize the designed controller for an adaptive cruise control system by using the particle swarm optimization method, which is a swarm intelligence optimization technique. Based on the results, it is observed that the use of automated optimization techniques for adaptive cruise control systems leads to better accuracy and safety for driving control. Furthermore, the time consumed for the controller parameter tuning process is also decreased significantly. In conclusion, the adaptive cruise control system requirements can be easily and accurately ensured by the use of particle swarm optimization algorithm.

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Section: Figure 1 Distribution Of Causes For Vehicle Accidents In Turkey From 2009 To 2018mentioning

confidence: 99%

Particle Swarm Optimization Method Based Controller Tuning for Adaptive Cruise Control Application

Ozkaya¹,

Arslan²,

Şen³

2021

Gazi University Journal of Science

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“…Another work using MPC framework as ACC controller is proposed in [14]- [18] to achieve fuel efficiency, comfort, and safety on ACC mode. Jiang et al presented an ACC control law called a linear exponential-of-quadratic Gaussian (LEQG) with risk-sensitive parameters that rendered six-vehicle behaviors and two control modes (expensive and inexpensive control mode) [19]. Liberis et al proposed the predictor-based with integral action to guarantee stability, zero steady-state, string stability, and non-negative impulse response for ACC feedback controller to handle actuator and sensor delay [20].…”

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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“…Wu et al [14] designed an ACC system based on an active braking algorithm to improve vehicle safety and comfort. Jiang et al [15] considered the driver's risk sensitivity under system and measurement uncertainty, and proposed a personalized stochastic optimal adaptive cruise control algorithm. It can be seen that the research on upper control algorithms is increasingly focused on how to better coordinate multiple objectives to meet real needs.…”

Section: Introductionmentioning

confidence: 99%

Research on Vehicle Adaptive Cruise Control Method Based on Fuzzy Model Predictive Control

Mao

Yang

et al. 2021

Machines

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Under complex working conditions, vehicle adaptive cruise control (ACC) systems with fixed weight coefficients cannot guarantee good car following performance under all conditions. In order to improve the tracking and comfort of vehicles in different modes, a fuzzy model predictive control (Fuzzy-MPC) algorithm is proposed. Based on the comprehensive consideration of safety, comfort, fuel economy and vehicle limitations, the objective function and constraints are designed. A relaxation factor vector is introduced to soften the hard constraint boundary in order to solve this problem, for which there was previously no feasible solution. In order to maintain driving stability under complex conditions, a multi-objective optimization method, which can update the weight coefficient online, is proposed. In the numerical simulation, the values of velocity, relative distance, acceleration and acceleration change rate under different conditions are compared, and the results show that the proposed algorithm has better tracking and stability than the traditional algorithm. The effectiveness and reliability of the Fuzzy-MPC algorithm are verified by co-simulation with the designed PID lower layer control algorithm with front feedforward and feedback.

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A Personalized Human Drivers’ Risk Sensitive Characteristics Depicting Stochastic Optimal Control Algorithm for Adaptive Cruise Control

Cited by 11 publications

References 35 publications

Particle Swarm Optimization Method Based Controller Tuning for Adaptive Cruise Control Application

Particle Swarm Optimization Method Based Controller Tuning for Adaptive Cruise Control Application

Adaptive Cruise Control With Gain Scheduling Technique Under Varying Vehicle Mass

Research on Vehicle Adaptive Cruise Control Method Based on Fuzzy Model Predictive Control

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