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

Бин, Ли; Zhang, Jianwei; Zhang, Ce; Pan, Wei; Cai, Shuo; Liu, Yang; Li, He

doi:10.1049/iet-its.2019.0518

Cited by 5 publications

(4 citation statements)

References 24 publications

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“…Li et al proposed a lane keeping algorithm based on scene analysis to ensure the driver's comfort when driving state changes. The research verified that the algorithm made the vehicle intelligent and smooth along the planned path through a variety of typical tests [11] . Wang and others found that when two ships changed their course to avoid collision, the speed ratio was an important factor that must be considered.…”

Section: Related Workmentioning

confidence: 59%

“…To predict the collision risk in a short time as soon as possible, environmental factors EF are introduced. The corresponding values T are calculated for the ships operating under different conditions, as shown in formula (11).…”

Section: ( ) ( )mentioning

confidence: 99%

“…In formula (11), X and Y refer to the ship and the meeting ship respectively. factor evaluation sequence, which is used for environmental factor normalization.…”

Section: ( ) ( )mentioning

confidence: 99%

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Application of Intelligent Obstacle Avoidance Algorithm Combined with Internet of Things Technology in Navigation

Guo

Wang²

2023

Preprint

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With the prosperity and development of the Maritime Silk Road, China's maritime industry has reached a new height. While the maritime transport industry has been vigorously developed, it has also brought great challenges to safe navigation. To realize intelligent navigation, effectively prevent maritime collision accidents, and improve navigation safety, a structural model of intelligent navigation obstacle avoidance platform based on Internet of Things technology is first proposed. Then the research combines the analytic hierarchy process, artificial neural network and BP neural network algorithm, and introduces environmental factors to design an optimized intelligent navigation obstacle avoidance algorithm, so that the algorithm can make real-time intelligent adjustment strategies according to the changes of the actual environment. Finally, the collision risk at the location of the research ship is judged, and the priority list of obstacle avoidance is constructed by the risk value between different ships and the research ship, providing reference for the pilot. The research results show that the prediction accuracy of I-INOA algorithm is 97.83%. In the two obstacle avoidance experiments, the decision-making efficiency of the four ships based on I-IONA algorithm is the highest, which is 1. In practical application, the priority list of obstacle avoidance is P, O and S2. In conclusion, I-INOA algorithm has better performance and practicability, enabling the research ship to respond more intelligently and quickly.

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Section: Related Workmentioning

confidence: 59%

Section: ( ) ( )mentioning

confidence: 99%

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Application of Intelligent Obstacle Avoidance Algorithm Combined with Internet of Things Technology in Navigation

Guo

Wang²

2023

Preprint

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“…The feedback component is used to eliminate the bias in the trajectory tracking while the feedforward component is used to address the disturbances caused by the road curvature 2 . The feedforward component enhances the vehicle's steering adaptation to the road curvature, which significantly enhances the accuracy of the trajectory tracking.…”

Section: /24mentioning

confidence: 99%

Research of the LQR algorithm with constraints based on preview control

Yang

Ouyang

2023

Preprint

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This study proposes a lateral and longitudinal control strategy for intelligent vehicles based on the preview control theory and linear quadratic regulator (LQR) to enhance the trajectory tracking ability and stability. The two-degree-of-freedom dynamic model of the vehicle and the road-vehicle error dynamic model is established, and the future road curvature is incorporated as a disturbance into the LQR state vector using the preview control theory. An augmented LQR problem is solved according to the optimal theory to obtain the analytical solution of the control quantity. This strategy also enhances the adaptive ability of the intelligent vehicle to extreme conditions by taking into account the dynamic constraints. The preview time is optimized using the simulated annealing algorithm to obtain the optimal preview time under different vehicle speeds and road friction coefficients. The stability of the closed-loop control system composed of the new algorithm is analyzed to verify its feasibility. Simulation results on the Carsim/Simulink joint platform demonstrate that it has excellent trajectory tracking ability, stability, and robustness to the vehicle speed. The proposed strategy has the potential to significantly advance the field of intelligent vehicle control and improve the safety and efficiency of transportation systems.

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