Algorithms for vehicle control stability system with 4 WS

Madarás, Juraj; Ferencey, Viktor; Bugár, Martin; Danko, Ján

doi:10.1109/mechatronika.2014.7018281

Cited by 4 publications

(1 citation statement)

References 2 publications

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“…At present, the research on vehicle stability pattern recognition at home and abroad is still in its infancy [6][7][8][9][10][11] . Based on the Hidden Markov Theory, TAKANO et al proposed an intelligent cognitive method for identifying the driver's steering intention; RAKSIN et al proposed a vehicle yaw moment control algorithm based on the identification of the driver's steering intention; Zong Changfu of Jilin University et al Based on driving simulator experiments, a method for predicting driver's steering intention and behavior using Hidden Markov and Neural Network algorithm is studied.…”

Section: Introductionmentioning

confidence: 99%

Research on stability identification algorithm of autonomous vehicle based on data driven

Niu¹,

Liu²,

Lin³

2023

3rd International Conference on Artificial Intelligence, Automation, and High-Performance Computing (AIAHPC 2023)

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Automatic driving is faced with complex and dynamic scenarios, especially under the influence of complex and variable factors such as longitudinal vehicle speed, road terrain, and other complex and variable factors, the vehicle's nonlinear and tire force longitudinal coupling characteristics are significantly enhanced, placing higher requirements on the performance of vehicle stability control systems. Based on this, this paper proposes a vehicle stability identification method which are able to identify vehicle stability status online based on driving style based on data driven and mechanism hybrid models. Firstly, based on vehicle stability evaluation criteria, the feature parameters are processed using unscented Kalman filtering algorithm and factor weighted analysis method to achieve quantitative stability evaluation; Secondly, using the dSPACE simulation platform, a test model for signals such as switch on, brake pedal on, steering wheel angle, and vehicle yaw rate under extreme conditions is established, and a driving stability demand identification model is established using the K-means algorithm; Thirdly, combining the demand identification model and quantitative evaluation training test data, a stability quantitative identification model is obtained; Finally, a collaborative simulation model of MATLAB/Simulink and CarSim was established and tested. The results show that the recognition method can accurately realize the reasonable classification and online recognition of the stability of autonomous vehicle, and to a greater extent, it reflects the design concept of intelligent vehicles that "cars adapt to people, not people adapt to cars".

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Section: Introductionmentioning

confidence: 99%