Predictor-based adaptive cruise control design

Bekiaris-Liberis, Nikolaos; Roncoli, Claudio; Papageorgiou, Markos

doi:10.1109/cca.2016.7587828

Cited by 7 publications

(6 citation statements)

References 38 publications

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“…As such, automated vehicles must be designed carefully to reduce their response delays, and their controllers must be tuned by considering the presence of delays. There exist strategies for automated vehicles that specifically aim to eliminate the effect of delays [8,36,46,56,69]. Still, even with well-designed automated vehicles it may require a relatively large penetration of AVs in the traffic flow to mitigate congestions if human drivers are string unstable (the penetration is 25% in our example, i.e., every fourth vehicle is automated).…”

Section: Simulation Resultsmentioning

confidence: 99%

Virtual Rings on Highways: Traffic Control by Connected Automated Vehicles

Molnár¹,

Hopka²,

Upadhyay³

et al. 2022

Preprint

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This work gives introduction to traffic control by connected automated vehicles. The influence of vehicle control on vehicular traffic and traffic control strategies are discussed and compared. It is highlighted that vehicle-to-everything connectivity allows connected automated vehicles to access the state of the traffic behind them such that feedback can be utilized to mitigate evolving congestions. Numerical simulations demonstrate that such connectivity-based traffic control is beneficial for smoothness and energy efficiency of highway traffic. The dynamics and stability of traffic flow, under the proposed controllers, are analyzed in detail to construct stability charts that guide the selection of stabilizing control gains.

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Section: Simulation Resultsmentioning

confidence: 99%

Virtual Rings on Highways: Traffic Control by Connected Automated Vehicles

Molnár¹,

Hopka²,

Upadhyay³

et al. 2022

Preprint

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“…The second advantage is the ease of implementation. Compared with the prediction-based method (20)(21)(22), no prediction of the predecessor's maneuver or state is required. Compared with the method resorting to incorporating multi-predecessor states (16), the proposed system only involves the communication between the subject vehicle and the direct predecessor.…”

Section: Discussionmentioning

confidence: 99%

“…A prevailing delay-compensating method involves predicting the future status (position, speed, acceleration, etc.) of the preceding vehicle or the subject vehicle (20)(21)(22). It thus needs an accurate estimation of the vehicle dynamics and a precise prediction of the future driving maneuver.…”

Section: Literature Reviewmentioning

confidence: 99%

Control Design, Stability Analysis, and Traffic Flow Implications for Cooperative Adaptive Cruise Control Systems with Compensation of Communication Delay

Zhang

Bai

et al. 2020

Transportation Research Record: Journal of the Transportation R

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Communication delay is detrimental to the performance of cooperative adaptive cruise control (CACC) systems. In this paper, we incorporate communication delay explicitly into control design and propose a delay-compensating CACC. In this new CACC system, the semi-constant time gap (Semi-CTG) policy, which is modified on the basis of the widely-used CTG policy, is employed by a linear feedback control law to regulate the spacing error. The semi-CTG policy uses historical information of the predecessor instead of its current information. By doing so, communication delay is fully compensated, which leads to better stability performance. Three stability properties—local stability, string stability, and traffic flow stability—are analyzed. The local stability and string stability of the proposed CACC system are guaranteed with the desired time gap as small as the communication delay. Both theoretical analysis and simulation results show that the delay-compensating CACC has better string stability and traffic flow stability than the widely-used CACC system. Furthermore, the proposed CACC system also shows the potential for improving traffic throughput and fuel efficiency. Robustness of the proposed system against uncertainties of sensor delay and vehicle dynamics is also verified with simulation.

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“…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]. In the following works, their utilized a partial derivative equation (PDE) as ACC controller, and the Aw-Rascle-Zhang-type (ARZ-type) model is considered as traffic flow model [21].…”

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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Predictor-based adaptive cruise control design

Cited by 7 publications

References 38 publications

Virtual Rings on Highways: Traffic Control by Connected Automated Vehicles

Virtual Rings on Highways: Traffic Control by Connected Automated Vehicles

Control Design, Stability Analysis, and Traffic Flow Implications for Cooperative Adaptive Cruise Control Systems with Compensation of Communication Delay

Adaptive Cruise Control With Gain Scheduling Technique Under Varying Vehicle Mass

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