Intelligent Architecture for Car-following Behaviour Observing Lane-changer: Modeling and Control

Tajdari, Farzam; Toulkani, Naeim Ebrahimi; Nourimand, Maral

doi:10.1109/iccke50421.2020.9303652

Cited by 12 publications

(14 citation statements)

References 21 publications

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“…It should be noted that these plots, correspond to an aggressive driving with f = 0.7 derived from (3) (S P = 14 m, and S MP = 10 m). According to Table V the human-like fuzzy controller accounts for the lowest average error of 2.341 m overall tests, which is 79% lower than that of the controller without t sc , 81% lower than the controller designed based on the model in [4], 75% lower than the controller of [46], and 55% lower than the real driver.…”

Section: A Results Via Ngsim Datamentioning

confidence: 96%

“…Regarding the table, the FV velocity trajectories that were generated by the controller with t sc was 55% smoother than the controller without t sc , 37% smoother than the controller generated based on the model in [4], 27% smoother than the controller in [46], and finally 79% smoother than the real driver. Similarly, the acceleration variances listed in this table also states that the controller with t sc performs relatively better than the other controllers such that it is 20% smoother than the controller designed based on the model in [4], 6% smoother than the controller in [46], and 37% smoother than the real driver in terms of the acceleration variance. However, the acceleration variance for the controller without t sc is considerably lower than the other cases, which might seem to be good to some extent, but it may also lead to slow reaction and the conservative movement and consequently large relative longitudinal distance and traffic queue.…”

Section: A Results Via Ngsim Datamentioning

confidence: 98%

“…We compared performances of the proposed final controller including t sc with the controller excluding t sc , the Controller based on the model in [4], the Controller in [46], the test data subset extracted from NGSim dataset [37] and the sensing device, as the real driver. Briefly, the model in [4] introduces the relative longitudinal acceleration between the FV and its front vehicle as a novel input for their ANFIS model.…”

Section: Resultsmentioning

confidence: 99%

“…However, this input is susceptible to noise in practical applications, and thus the method has solid restrictions. Likewise, the controller proposed in [46] employs the relative longitudinal distance instead of the relative longitudinal acceleration. Although the input is supposed to be less sensitive to the noise comparing to [4], this models also lack considering the human safety and convenience, which we know that as t sc as a human factor in our study.…”

Section: Resultsmentioning

confidence: 99%

“…In addition, the quantitative results of the velocity variance of the controllers with the driver using the NGSim dataset are summarized in Table V. Regarding the table, the FV velocity trajectories that were generated by the controller with t sc was 55% smoother than the controller without t sc , 37% smoother than the controller generated based on the model in [4], 27% smoother than the controller in [46], and finally 79% smoother than the real driver. Similarly, the acceleration variances listed in this table also states that the controller with t sc performs relatively better than the other controllers such that it is 20% smoother than the controller designed based on the model in [4], 6% smoother than the controller in [46], and 37% smoother than the real driver in terms of the acceleration variance.…”

Section: A Results Via Ngsim Datamentioning

confidence: 99%

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Simultaneous Intelligent Anticipation and Control of Follower Vehicle Observing Exiting Lane Changer

Tajdari

Golgouneh

Ghaffari

et al. 2021

IEEE Trans. Veh. Technol.

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Despite the advances related to car-following and lane-changing behaviors, the influence of lane-changing on the car-following models, which results in a complex transient merging behavior, has not comprehensively been investigated. This paper presents a novel fuzzy controller based on a human factor to optimize the Follower Vehicle (FV) behavior subject to safety, comfort, and convenient traveled time in the complex behavior where the Lane Changer (LC) vehicle exits the temporary lane. The factor enables the controller to mimic the current driver behavior in terms of maximum pleasantness of drive. Accordingly, the data of real-life experiments were used to design the human-like fuzzy controller, to build a predictive model to suggest the appropriate acceleration, velocity, and travel distance. At best, the correlation coefficient of 0.93 and the Root Mean Square Error (RMSE) of 0.71 were achieved for modeling using the adaptive Neuro-Fuzzy Inference System (ANFIS) utilizing Gaussian function as a membership function. Furthermore, to evaluate the robustness of the controller to uncertainties and unknown disturbances for real-time driving experiments, a test-bed was fabricated to mount the feedback sensors, including vision, accelerometer, and distance measurement sensors. The results of running the controller in various driving scenarios showed 70% and 38% improvements in safety and ride comfort, respectively. The proposed intelligent controller is intended to be used for vehicle route guidance and on urban highways.

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Section: A Results Via Ngsim Datamentioning

confidence: 96%

Section: A Results Via Ngsim Datamentioning

confidence: 98%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: A Results Via Ngsim Datamentioning

confidence: 99%

See 3 more Smart Citations

Simultaneous Intelligent Anticipation and Control of Follower Vehicle Observing Exiting Lane Changer

Tajdari

Golgouneh

Ghaffari

et al. 2021

IEEE Trans. Veh. Technol.

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Next-generation prognosis framework for pediatric spinal deformities using bio-informed deep learning networks

Tajdari

Shirzadian

et al. 2022

Engineering with Computers

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Predicting pediatric spinal deformity (PSD) from X-ray images collected on the patient's initial visit is a challenging task. This work builds on our previous method and provides a novel bio-informed framework based on a mechanistic machine learning technique with dynamic patient-specific parameters to predict PSD. We provide a geometry-based bone growth model that can be utilized in a range of applications to enhance the bio-informed mechanistic machine learning framework. The proposed technique is utilized to examine and predict spine curvature in PSD cases such as adolescent idiopathic scoliosis. The best fit of a segmented 3D volumetric geometry of the human spine acquired from 2D X-ray images is employed. Using an active contour model based on gradient vector flow snakes, the anteroposterior and lateral views of the X-ray images are segmented to derive the 2D contours surrounding each vertebra. Using minimal user input, the snake parameters are calibrated and automatically computed over the dataset, resulting in fast image segmentation and data collection. The 2D segmented outlines of each vertebra are transformed into a 3D image segmentation result. The Iterative Closest Point mesh registration technique is then used to establish a mesh morphing approach and creates a 3D atlas spine model. Using the comprehensive 3D volumetric model, one can automatically extract spinal geometry data as inputs to the mechanistic machine learning network. Moreover, the proposed bio-informed deep learning network with the modified bone growth model achieves competitive or even superior performance against other state-of-the-art learning-based methods.

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Adaptive traffic control at motorway bottlenecks with time-varying Fundamental Diagram

Tajdari

Roncoli²

2021

IFAC-PapersOnLine

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Intelligent Architecture for Car-following Behaviour Observing Lane-changer: Modeling and Control

Cited by 12 publications

References 21 publications

Simultaneous Intelligent Anticipation and Control of Follower Vehicle Observing Exiting Lane Changer

Simultaneous Intelligent Anticipation and Control of Follower Vehicle Observing Exiting Lane Changer

Next-generation prognosis framework for pediatric spinal deformities using bio-informed deep learning networks

Adaptive traffic control at motorway bottlenecks with time-varying Fundamental Diagram

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