A fast decision making method for mandatory lane change using kernel extreme learning machine

Cheng, Shuying; Xu, Yang; Zong, Ruixue; Wang, Chuanhai

doi:10.1007/s13042-019-00923-8

Cited by 7 publications

(3 citation statements)

References 17 publications

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“…In addition, Hou 13 modeled lane changes behavior using a Bayesian classifier and a decision tree method, the prediction accuracy of merge events is 79.3%. In Cheng et al ’s work, 14 a K-ELM (kernel extreme learning machine) based decision-making method was proposed for mandatory lane changes, and simulation results show that it can generate the lane change decision with a 92.86% accuracy for merge events. Jin 15 used the continuous hidden Markov model to predict lane change behavior, the predicted accuracy of left and right lane changes are 84% and 88%, respectively.…”

Section: Related Workmentioning

confidence: 99%

Lane change maneuver detection considering real-time vehicle dynamic features via V2X communication

Song

Zhou

Ding

et al. 2022

Proceedings of the Institution of Mechanical Engineers, Part D:

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More than 90% of traffic accidents are caused by driver behavior, with lane change behavior being a major contributor. Recently, driving assistance systems are being introduced on vehicles to reduce traffic accidents, and a reliable vehicle lane change collision detection system is a key component of these systems. Besides, the foundation of the vehicle lane change detection system is the effective vehicle lane change detection model. In this paper, based on the support vector machine, we propose a model for detecting driver lane change maneuvers and take into account the real-time vehicle dynamic features transmitted via Vehicle to X (V2X) Communication. The accuracy is ideal for lane keep and lane change situations, and it is also robust for zigzag driving situations, according to tests conducted using the NGSIM real traffic dataset. The detection accuracy for left and right lane change maneuvers is 97.5% and 99.09%, respectively, while the false alarm rate is 8.56%. Additionally, the average advance detection time is 1.7 s, which is suitable for actual driving application scenarios.

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Section: Related Workmentioning

confidence: 99%

Lane change maneuver detection considering real-time vehicle dynamic features via V2X communication

Song

Zhou

Ding

et al. 2022

Proceedings of the Institution of Mechanical Engineers, Part D:

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“…• Transport. In this field, several studies focus on process improvement, such as speed and accuracy in lane changing maneuvers when driving on highways [97], driving terrestrial vehicles on rural roads [98], robots learning routes through linguistic decision trees [99], and methods used in biped robot walking processes [100,101].…”

Section: Supervised Learningmentioning

confidence: 99%

Decision-Making under Uncertainty for the Deployment of Future Hyperconnected Networks: A Survey

Alzate-Mejía

Santos-Boada

Amazonas

2021

Sensors

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Among the several emerging dimensioning, control and deployment of future communication network paradigms stands out the human-centric characteristic that creates an intricate relationship between telematics and human activities. The hard to model dynamics of user behavior introduces new uncertainties into these systems that give rise to difficult network resource management challenges. According to this context, this work reviews several decision-making computational methods under the influence of uncertainties. This work, by means of a systematic literature review, focuses on sensor-based Internet of Things scenarios such as Smart Spaces and Industry 4.0. According to our conclusions, it is mandatory to establish a means for modeling the human behavior context in order to improve resource assignment and management.

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“…The kernel function can be applied to train the kernel-based ELM model when the hidden layer feature mapping h(x) is unknown. The K-ELM algorithm does not need to consider the number of hidden nodes and only concerns the selection of the kernel function and the input data (Cheng et al, 2019). Let W k denote the output weight, which is expressed as…”

Section: Brief Description Of Elm and K-elmmentioning

confidence: 99%

Archives of Acoustics

Wang

Chen

Yin

2021

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Acoustic source localization using distributed microphone array is a challenging task due to the influences of noise and reverberation. In this paper, acoustic source localization using kernel-based extreme learning machine in distributed microphone array is proposed. Specifically, the space of interest is divided into some labeled positions, and the candidate generalized cross correlation function in each node is treated as the feature mapped into the hidden nodes of extreme learning machine. During the training phase, by the implementation of kernel function, the output weights of the classifier are calculated and do not need to be tuned. After the kernel-based extreme learning machine (K-ELM) is well trained, the measured generalized cross correlation data are fed into the K-ELM classifier, and the output is the estimated acoustic source position. The proposed method needs less human intervention for both training and testing and it does not need to calibrate the node in advance. Simulation and real-world experimental results reveal that the proposed method has extremely fast training and testing speeds, and can obtain better localization performance than steered response power, K-nearest neighbor, and support vector machine methods.

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A fast decision making method for mandatory lane change using kernel extreme learning machine

Cited by 7 publications

References 17 publications

Lane change maneuver detection considering real-time vehicle dynamic features via V2X communication

Lane change maneuver detection considering real-time vehicle dynamic features via V2X communication

Decision-Making under Uncertainty for the Deployment of Future Hyperconnected Networks: A Survey

Archives of Acoustics

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