Integration of Lane Keeping Assistance with Steering

Nhila, Amine; Williams, Daniel E.; Gupta, Vishi

doi:10.4271/2013-01-2389

Cited by 8 publications

(4 citation statements)

References 4 publications

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“…In this paper, the offset and heading angle are used as the controlled variables of the feedback loop [24]. In the feedback control, the preview time is applied to compensate for the vehicle response lag.…”

Section: Lane‐keeping System Designmentioning

confidence: 99%

Lane‐keeping system design considering driver's nervousness via scene analysis

Бин

Zhang

et al. 2020

IET intell. transp. syst

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Section: Lane‐keeping System Designmentioning

confidence: 99%

Lane‐keeping system design considering driver's nervousness via scene analysis

Бин

Zhang

et al. 2020

IET intell. transp. syst

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“…For lane departure warning (LDW), when the vehicle deviates from the lane, the system can know and remind the driver through sound, touch, and other means to avoid triggering potential lateral collision or other risks after the vehicle crosses the line [ 16 – 18 ]. As regards lane keeping assistance (LKA), when the vehicle deviates from the lane, it will no longer be limited to sending an early warning message to the driver but actively control the steering wheel, correct the lateral position of the vehicle, correct the vehicle deviation back into the lane, and actively avoid lateral collision or other risks [ 19 – 21 ]. Lane centering control (LCC) can assist the driver to control the steering wheel, center the vehicle in the center of the lane, continuously control the vehicle to drive in the center of the lane, and cooperate with adaptive cruise [ 22 – 24 ].…”

Section: Introductionmentioning

confidence: 99%

An End-to-End Lane Detection Model with Attention and Residual Block

Wang

Xiao-ting

2022

Computational Intelligence and Neuroscience

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Lane detection, as one of the most important core functions in the autonomous driving environment, is still an open problem. In particular, pursuing high accuracy in complex scenes, such as no line and multiple lane lines, is an urgent issue to be discussed and solved. In this paper, a novel end-to-end lane detection model combining the advantages of attention mechanism and residual block is proposed to address the problem. A residual block alleviates the possible gradient problem. An attention block can help the proposed model centralize on where to focus in the process of learning feature representation, which can make the model itself more sensitive to the feature representation of lane lines through convolutional operations. Additionally, the U-shaped structure with three downsampling operations preserves the image resolution and the original lane line information in the image to the greatest extent. The U-shaped structure can directly output the prediction results to eliminate many complex or unnecessary calculation processes. The experimental results on two public lane detection datasets show that the lane detection performance of the proposed model can achieve high accuracy, and the corresponding weight sizes are only 2.25 M. Finally, to further explain the effectiveness of the proposed model, the unavoidable troubles encountered in the experiment are discussed.

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“…At present, the researches on lane keeping mainly focus on lane keeping control algorithm, 6,7 road environment perception, 8,9 and driver behavior recognition and human–machine sharing. 10,11…”

Section: Introductionmentioning

confidence: 99%

“…At present, the researches on lane keeping mainly focus on lane keeping control algorithm, 6,7 road environment perception, 8,9 and driver behavior recognition and human-machine sharing. 10,11 The lane keeping control algorithm is very important for the improvement of the overall performance of the lane keeping system.…”

Section: Introductionmentioning

confidence: 99%

Vehicle lane keeping system based on TSK fuzzy extension control

Wang

Cui

Xia

et al. 2019

Proceedings of the Institution of Mechanical Engineers, Part D:

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In the traditional lane keeping system, a single control algorithm is used for global-region control, which often results in poor control effect in some control areas. In view of this problem, a control method combining the extension control and Takagi-Sugeno-Kang fuzzy control is proposed for lane keeping system control. The extension control strategy based on the idea of control with area division divides different control regions according to the states of the system, and adopts different control algorithms in different control areas to address the limitation of a single control algorithm. For the randomness and inaccuracy of artificial experience in control domain division, a method for control domain division based on the density distribution of characteristic plane is proposed in this article. In order to further improve and explore the performance of extension control, Takagi-Sugeno-Kang extension controller is designed with Takagi-Sugeno-Kang fuzzy control theory, and the extension control performance is improved when switching among different control algorithms. The proposed control strategy is simulated and tested on the CarSim/Simulink joint simulation platform and the CarSim/LabVIEW hardware-in-loop test bench, respectively. The results show that the proposed control strategy can effectively keep the vehicle near the center line of the lane, and both the lane keeping control accuracy and vehicle stability are improved, which guarantees driving safety and comfort.

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Integration of Lane Keeping Assistance with Steering

Cited by 8 publications

References 4 publications

Lane‐keeping system design considering driver's nervousness via scene analysis

Lane‐keeping system design considering driver's nervousness via scene analysis

An End-to-End Lane Detection Model with Attention and Residual Block

Vehicle lane keeping system based on TSK fuzzy extension control

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