Coordinated Lateral and Longitudinal Vehicle-Following Control of Connected and Automated Vehicles Considering Nonlinear Dynamics

Wang, Yulei; Bian, Ning; Zhang, Linlin; Chen, Hong

doi:10.1109/lcsys.2020.2997994

Cited by 17 publications

(9 citation statements)

References 24 publications

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“…• They focus on the design of lateral and/or longitudinal controllers [5,[12][13][14][15]]. • They rely on V2V communication [12][13][14][15][16] or GPS [6]. • They lack experimental results [7,8,[13][14][15]].…”

Section: Literature Reviewmentioning

confidence: 99%

“…• They rely on V2V communication [12][13][14][15][16] or GPS [6]. • They lack experimental results [7,8,[13][14][15]]. • They focus on the design of a camera system for leader tracking [2], handling intermittent vision observations [7], or state-estimation techniques [16].…”

Section: Literature Reviewmentioning

confidence: 99%

“…The error model then reads as e = x set − x (13) with distance error e s = s set − s and velocity error e v = v set − v. For the model ( 12), a controller…”

Section: Longitudinal Controlmentioning

confidence: 99%

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Leader-Based Trajectory Following in Unstructured Environments—From Concept to Real-World Implementation

2022

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In this paper, the problem of vehicle guidance by means of an external leader is described. The objective is to navigate a four-wheeled vehicle through unstructured environments, characterized by the lack of availability of typical guidance infrastructure like lane markings or HD maps. The trajectory-following approach is based on an estimate of the leader’s path. For that, position measurements are stored over time with respect to an inertial frame. A new strategy is proposed to rate the significance of position measurements and ensure that a certain threshold of stored samples is not exceeded. Having an estimate of the leader path is essential to prevent the cutting-corner phenomenon and for exact path following in general. A spline-approximation technique is applied to obtain a smooth reference path for the underlying lateral and longitudinal motion controllers. For longitudinal tracking, a constant time-headway policy was implemented, to follow the leader with a constant time gap along the estimated path. The algorithm was first developed and tested in a simulation framework and then deployed in a demonstrator vehicle for validation under real operating conditions. The presented experimental results were achieved using only on-board sensors of the demonstrator vehicle, while high-accuracy differential GPS-based position measurements serve as the ground truth data for visualization.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

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Leader-Based Trajectory Following in Unstructured Environments—From Concept to Real-World Implementation

2022

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“…In [19], the authors developed an integrated longitudinal and lateral trajectory planning and tracking control algorithm under vehicle-to-vehicle communication. The problem of coordinated lateral and longitudinal vehiclefollowing control for connected and automated vehicles (CAVs) was investigated by considering nonlinear dynamics in [20]. As mentioned above, the accurate trajectory tracking control in AVs requires both the steering wheel control for regulating the lateral position and the throttle/braking control for adjusting its longitudinal dynamics.…”

Section: Background and Motivationmentioning

confidence: 99%

State-feedback Switching Linear Parameter Varying Control for Vehicle Path Following Under Uncertainty and External Disturbances

Shen

Chen

2022

2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC)

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Automated Vehicle Path Following Control (PFC) is an advanced control system that can regulate the vehicle into a collision-free region in the presence of other objects on the road. Common collision avoidance functions, such as forward collision warning and automatic emergency braking, have recently been developed and equipped on production vehicles. However, it is impossible to develop a perfectly precise vehicle model when the vehicle is driving. Most PFCs did not consider uncertainties in the vehicle model, external disturbances, and parameter variations at the same time. To address the issues associated with this important feature and function in autonomous driving, a new vehicle PFC is proposed using a robust model predictive control (MPC) design technique based on matrix inequality and the theoretical approach of the hybrid $\&$ switched system. The proposed methodology requires a combination of continuous and discrete states, e.g. regulating the continuous states of the AV (e.g., velocity and yaw angle) and discrete switching of the control strategy that affects the dynamic behaviors of the AV under different driving speeds. Firstly, considering bounded model uncertainties, and norm-bounded external disturbances, the system states and control matrices are modified. In addition, the vehicle's time-varying longitudinal speed is considered, and a vehicle lateral dynamic model based on Linear Parameter Varying (LPV) is established by utilizing a polytope with finite vertices. Then the Min-Max robust MPC state feedback control law is obtained at every timestamp by solving a set of matrix inequalities which are derived from Lyapunov stability and the minimization of the worst-case in infinite-horizon quadratic objective function. Compared to adaptive MPC, nonlinear MPC, and cascade LPV control, the proposed robust LPV MPC shows improved tracing accuracy along vehicle lateral dynamics. Finally, the state feedback switched LPV control theory with separate Lyapunov functions under both arbitrary switching and average-dwell-time (ADT) switching conditions are studied and applied to cover the path following control in full speed range. Numerical examples, tracking effectiveness, and convergence analysis are provided to demonstrate and ensure the control effectiveness and strong robustness of the proposed algorithms.

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“…So, it can be seen that there is obvious coupling relationship between the lateral and longitudinal movement of the vehicle. 27,28…”

Section: Vehicle Collision Avoidance Control Strategymentioning

confidence: 99%

Longitudinal and lateral collision avoidance control strategy for intelligent vehicles

Zhang

Wang

Zhao

et al. 2021

Proceedings of the Institution of Mechanical Engineers, Part D:

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In order to solve the problems of longitudinal and lateral control coupling, low accuracy and poor real-time of existing control strategy in the process of active collision avoidance, a longitudinal and lateral collision avoidance control strategy of intelligent vehicle based on model predictive control is proposed in this paper. Firstly, the vehicle nonlinear coupling dynamics model is established. Secondly, considering the accuracy and real-time requirements of intelligent vehicle motion control in pedestrian crossing scene, and combining the advantages of centralized control and decentralized control, an integrated unidirectional decoupling compensation motion control strategy is proposed. The proposed strategy uses two pairs of unidirectional decoupling compensation controllers to realize the mutual integration and decoupling in both longitudinal and lateral directions. Compared with centralized control, it simplifies the design of controller, retains the advantages of centralized control, and improves the real-time performance of control. Compared with the decentralized control, it considers the influence of longitudinal and lateral control, retains the advantages of decentralized control, and improves the control accuracy. Finally, the proposed control strategy is simulated and analyzed in six working conditions, and compared with the existing control strategy. The results show that the proposed control strategy is obviously better than the existing control strategy in terms of control accuracy and real-time performance, and can effectively improve vehicle safety and stability.

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Coordinated Lateral and Longitudinal Vehicle-Following Control of Connected and Automated Vehicles Considering Nonlinear Dynamics

Cited by 17 publications

References 24 publications

Leader-Based Trajectory Following in Unstructured Environments—From Concept to Real-World Implementation

Leader-Based Trajectory Following in Unstructured Environments—From Concept to Real-World Implementation

State-feedback Switching Linear Parameter Varying Control for Vehicle Path Following Under Uncertainty and External Disturbances

Longitudinal and lateral collision avoidance control strategy for intelligent vehicles

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