Human-Centered Trajectory Tracking Control for Autonomous Vehicles With Driver Cut-In Behavior Prediction

Chen, Yimin; Hu, Chuan; Wang, Junmin

doi:10.1109/tvt.2019.2927242

Cited by 75 publications

(27 citation statements)

References 26 publications

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“…where K 1 , K 2 , Ω, and T are coefficient matrices. According to the principle of the MPC [30], the future behavior of the system is predicted according to the prediction model at each moment of system sampling, and the performance indicators of system in the future moment are optimized. The control sequence is obtained by solving the corresponding optimization problem and the first control quantity is applied to the system; then the prediction time domain is aligned one step forward.…”

Section: Problem Solvingmentioning

confidence: 99%

Adaptive Cruise Control for Cut-In Scenarios Based on Model Predictive Control Algorithm

Chen¹,

Guo²,

Guo³

et al. 2021

Applied Sciences

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In a cut-in scenario, traditional adaptive cruise control usually cannot effectively identify the cut-in vehicle and respond to it in advance. This paper proposes an adaptive cruise control (ACC) strategy based on the MPC algorithm for cut-in scenarios. A finite state machine (FSM) is designed to manage vehicle control in different cut-in scenarios. For a cut-in scenario, a method to identify and quantify the possibility of cut-in of a vehicle is proposed. At the same time, a safety distance model of the cut-in vehicle is established as the basis for the state transition of the finite state machine. Taking the quantified cut-in possibility of a vehicle as a reference, the model predictive control (MPC) algorithm, which considers the constraints of driving safety and comfort, is used to realize coordinated control of the host vehicle and the cut-in vehicle. Simulink–Carsim simulation studies show that the ACC strategy for a cut-in scenario can effectively identify a cut-in vehicle and quantify the possibility of cut-in of the vehicle. Faced with a cut-in vehicle, the host vehicle using the ACC strategy can brake several seconds early and switch the following target to the cut-in vehicle. Meanwhile, the acceleration and jerk of the host vehicle changes within a reasonable range, which ensures driving safety and comfort.

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Section: Problem Solvingmentioning

confidence: 99%

Adaptive Cruise Control for Cut-In Scenarios Based on Model Predictive Control Algorithm

Chen¹,

Guo²,

Guo³

et al. 2021

Applied Sciences

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“…In [74], an MPC-based motion controller considering the cut-in behavior of other vehicles is proposed, predicting it using long short-term memory, in addition, the lateral velocity is estimated through a moving horizon estimator. In [75], the cut-in behavior is predicted through a rule-based method and an MPC is used for trajectory tracking under different cut-in scenarios.…”

Section: Motion Control Of Single Agvs Using Mpcmentioning

confidence: 99%

Model predictive control for autonomous ground vehicles: a review

Hirche

Huang

et al. 2021

Auton. Intell. Syst.

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This paper reviews model predictive control (MPC) and its wide applications to both single and multiple autonomous ground vehicles (AGVs). On one hand, MPC is a well-established optimal control method, which uses the predicted future information to optimize the control actions while explicitly considering constraints. On the other hand, AGVs are able to make forecasts and adapt their decisions in uncertain environments. Therefore, because of the nature of MPC and the requirements of AGVs, it is intuitive to apply MPC algorithms to AGVs. AGVs are interesting not only for considering them alone, which requires centralized control approaches, but also as groups of AGVs that interact and communicate with each other and have their own controller onboard. This calls for distributed control solutions. First, a short introduction into the basic theoretical background of centralized and distributed MPC is given. Then, it comprehensively reviews MPC applications for both single and multiple AGVs. Finally, the paper highlights existing issues and future research directions, which will promote the development of MPC schemes with high performance in AGVs.

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“…For a vehicle system including HV and TVs, the comprehensive understanding of TV lateral behavior is essential for HV decision and control. For example, in [4], the cut-in intention of nearby vehicles are predicted and considered in HV trajectory tracking control. The lateral motions of surrounding vehicles are predicted and then integrated into threat assessment algorithm of the decision-making system in [5].…”

Section: Introductionmentioning

confidence: 99%

Short-Term Lateral Behavior Reasoning for Target Vehicles Considering Driver Preview Characteristic

Zhou

Liu

Wei

et al. 2022

IEEE Trans. Intell. Transport. Syst.

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A timely understanding of target vehicles (TVs) lateral behavior is essential for the decision-making and control of host vehicle. Existing physical model-based methods such as motion-based method and multiple centerline-based method are generally constructed based on TV pose and longitudinal velocity, and tend to ignore TV preview driving characteristic and other useful information such as lateral velocity and yaw rate. To address these issues, a driver preview and multiple centerline model-based probabilistic behavior recognition architecture is proposed for timely and accurate TV lateral behavior prediction. Firstly, a driver preview model is used to describe vehicle preview driving characteristic, and TV preview lateral offset and preview lateral velocity are calculated with TV states and road reference information. Then, the preview lateral offset and preview lateral velocity are combined with multiple centerline model for TV lateral behavior reasoning based on the interacting multiple model-based probabilistic behavior recognition algorithm. With this method, TV preview driving characteristic and lateral motion states are combined for precise TV lateral behavior description. Furthermore, to predict short-term lateral behavior, a preview lateral velocity-dependent transition probability matrix model constructed with Gaussian cumulative distribution function is proposed. Simulation and experimental results show that the proposed method considering vehicle preview driving characteristic predicts TV lateral behavior earlier than the conventional method.

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Human-Centered Trajectory Tracking Control for Autonomous Vehicles With Driver Cut-In Behavior Prediction

Cited by 75 publications

References 26 publications

Adaptive Cruise Control for Cut-In Scenarios Based on Model Predictive Control Algorithm

Adaptive Cruise Control for Cut-In Scenarios Based on Model Predictive Control Algorithm

Model predictive control for autonomous ground vehicles: a review

Short-Term Lateral Behavior Reasoning for Target Vehicles Considering Driver Preview Characteristic

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