Study on Steering Angle Input during the Automated Lane Change of Electric Vehicle

Li, Hongluo; Luo, Yuanfang

doi:10.4271/2017-01-1962

Cited by 6 publications

(2 citation statements)

References 19 publications

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“…Within this interval, it is essential to filter out trajectories that exhibit excessive lateral acceleration or are unable to complete lane change before reaching the end of the acceleration lane. To address this, a maximum lateral acceleration constraint a y max = 2 m / s 2 is set in this study ( 35 , 39 ). By promising this constraint, the time interval of t 3 − t 2 is further refined to a reduced range, [ 3 . 5 , 7 . 7 ] .…”

Section: Merging Sequence Decision Model For Collision Avoidancementioning

confidence: 99%

Research on Cooperative Control for On-Ramp Merging with Multiple Lanes in Connected and Automated Environments

Wang,

Cao,

Ren

et al. 2023

Transportation Research Record: Journal of the Transportation R

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This paper addresses the issue of on-ramp merging in multi-lane freeways and proposes a cooperative control method based on connected and automated vehicles. Focusing on a two-main-lane freeway scenario, the method consists of two key models: a merging sequence decision model and a motion planning model. The merging sequence decision model prioritizes collision avoidance by predicting the motion state and lane-changing trajectory of vehicles in the merging area. The motion planning model utilizes longitudinal and lateral cooperation to control the main lane vehicles to generate the merging gap through coordinated adjustments in the longitudinal speed or by performing lane changes. The optimal merging trajectory is determined using the entropy weight method, and a fast optimization method based on neural networks is employed. Through simulations considering different traffic density combinations, the proposed method is compared with traditional control schemes. Results demonstrate its superiority in improving traffic flow stability, rapidity, and overall efficiency.

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Section: Merging Sequence Decision Model For Collision Avoidancementioning

confidence: 99%

Research on Cooperative Control for On-Ramp Merging with Multiple Lanes in Connected and Automated Environments

Wang,

Cao,

Ren

et al. 2023

Transportation Research Record: Journal of the Transportation R

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show abstract

“…With the development of online optimization and computer hardware, model predictive control has become an area of increasing interest in vehicle active safety [10,11] and path tracking [12]. Considering the problem of poor tracking of an automated vehicle when it slows down to change lanes in a given vicinity, Li et al [13] proposed a lane change detection algorithm using two controllers and a steering tracking algorithm based on the combination of an MPC controller and a PI controller. Liang et al [14] considered the problem that the saturation range of tire lateral deflection stiffness during cornering reduces the vehicle tracking performance on low-grip roads and proposed model predictive control (MPC).…”

Section: Introductionmentioning

confidence: 99%

Adaptive MPC-Based Lateral Path-Tracking Control for Automatic Vehicles

Yang,

Qian,

et al. 2024

WEVJ

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For continuously changing road conditions and vehicle operating states, the exactitude of vehicle path tracking has not been secured by model predictive control based on linear lateral stiffness. An amended square root cubature Kalman filter method based on the minimization of a new covariance of interest is proposed to calculate the tire lateral deflection force in real time. The ratio of the estimated tire force to the linear tire force was used as a ratio to adjust the lateral deflection stiffness, and an adaptive model predictive controller was built based on the vehicle path-tracking error model to correct the tire lateral deflection stiffness. Finally, an analysis based on the joint CarSim and Simulink simulation platform shows that compared to a conventional model predictive control (MPC) controller, a trajectory-following controller built based on this method can effectively reduce the lateral distance error and heading error of an autonomous vehicle. Especially under low adhesion conditions, the conventional MPC controllers will demonstrate large instability during trajectory tracking due to the deviation of the linear tire force calculation results, whereas the adaptive model predictive control (AMPC) controllers can correct the side deflection stiffness by estimating the tire force and still achieve stable and effective tracking of the target trajectory. This suggests that the proposed algorithm can improve the effectiveness of trajectory tracking control for autonomous vehicles, which is an important reference value for the optimization of autonomous vehicle control systems.

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A Trajectory Planning Method of Automatic Lane Change Based on Dynamic Safety Domain

Wang,

Cao,

2023

Automot. Innov.

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Study on Steering Angle Input during the Automated Lane Change of Electric Vehicle

Cited by 6 publications

References 19 publications

Research on Cooperative Control for On-Ramp Merging with Multiple Lanes in Connected and Automated Environments

Research on Cooperative Control for On-Ramp Merging with Multiple Lanes in Connected and Automated Environments

Adaptive MPC-Based Lateral Path-Tracking Control for Automatic Vehicles

A Trajectory Planning Method of Automatic Lane Change Based on Dynamic Safety Domain

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