Acceleration and Deceleration Calibration of Operating Speed Prediction Models for Two-Lane Mountain Highways

Jin, Xu; Lin, Wu; Xu, Wenjun; Shao, Yiming

doi:10.1061/jtepbs.0000050

Cited by 32 publications

(24 citation statements)

References 13 publications

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“…This is in contrast to passenger cars, for which the acceleration and deceleration are dominated by driving comfort. Referencing a field research by Xu et al, 21 the 90th acceleration and deceleration for heavy vehicles with a total weight of more than 20 tons are 0.309 and 0.407 m/ s 2 , respectively. Therefore, in the target speed decisionmaking for heavy vehicles, the constraints on the acceleration and deceleration controlled by the longitudinal dynamic performance can be expressed as follows:…”

Section: Limitation In Power Performance For Trucksmentioning

confidence: 93%

“…Therefore, the lateral comfort constraint for a passenger car can be given by (17): The spot speed on selected cross-sections within sharp curves and the continuous speed of passenger cars along six two-lane highways were measured with an inertial measurement unit (IMU) in a study conducted by the author. 21 The measured data of 90th percentile acceleration/deceleration of passenger cars and coaches are listed in Table 4, which can be used as the threshold of longitudinal comfort in travel. Therefore, the longitudinal comfort constraint can be represented as follows:…”

Section: Constraintsmentioning

confidence: 99%

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Modeling drivers’ speed choice on roads with complex shapes based on the curvature of the preview trajectory

2018

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The target speed needs to be determined based on the geometry of the road ahead and current operational state in order to realize automated driving along complex mountain roads. However, existing speed prediction and V 85 models are unable to do so. This paper proposes a speed decision algorithm that is based on the curvature of the preview trajectory. Firstly, the trajectory point in each preview cross-section within the driver's sight window is determined; the curvature of the preview trajectory is calculated and used as input data. Secondly, an objective function is selected to set the speed control pattern: minimum travel time; maximum comfort; minimum deviation from the reference speed; or multiple weighted objectives. Thirdly, the speed value for the preview cross-section is solved by optimizing the selected objective function. As the sight window rolls ahead, the target speed can be determined along the traveling distance. Because the trajectory generated within the boundary of the pavement is used by the driver, parameters such as the change in curve radius, deflection angle, lane width, and spiral length can change the driving boundary, which can change the trajectory characteristics and thus affect the target speed. Therefore, the proposed algorithm is able to determine the speed for roads with complex shapes. The field validation and simulation cases demonstrated that the target speed profile of different driving patterns can be obtained for different combinations of trajectory and speed patterns. Thus, the model can simulate diverse real-world driving behaviors and explain the diversity in driving speeds of different drivers.

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Section: Limitation In Power Performance For Trucksmentioning

confidence: 93%

Section: Constraintsmentioning

confidence: 99%

Modeling drivers’ speed choice on roads with complex shapes based on the curvature of the preview trajectory

2018

Self Cite

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show abstract

“…Model Predictive Control (MPC) has the advantages of excellent e ectiveness and robustness, which can e ectively address the uncertainties and nonlinearity of the control process and address the constraints of various variables in the control process. erefore, from a macroscopic perspective, the congestion problem of a roadway network can be converted to a mathematical model based on the congestion characteristics network, and the model predictive control principle [92][93][94][95][96][97][98][99][100].…”

Section: Study Of Gating Control Methods Based On Model Prediction Conmentioning

confidence: 99%

Evolution Regularity Mining and Gating Control Method of Urban Recurrent Traffic Congestion: A Literature Review

Zhou

et al. 2020

Journal of Advanced Transportation

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To understand the status quo of urban recurrent traffic congestion, the current results of recurrent traffic congestion, and gating control are reviewed from three aspects: traffic congestion identification, evolution trend prediction, and urban road network gating control. Three aspects of current research are highlighted: (a) The majority of current studies are based on statistical analyses of historical data, while congestion identification is performed by acquiring small-scale traffic parameters. Thus, congestion studies on the urban global roadway network are lacking. Situation identification and the failure to effectively warn or even avoid traffic congestion before congestion forms are not addressed; (b) correlation studies on urban roadway network congestion are inadequate, especially regarding deep learning, and considering the space-time correlation for congestion evolution trend prediction; and (c) quantitative research methods, dynamic determination of gating control areas, and effective countermeasures to eliminate traffic congestion are lacking. Regarding the shortcomings of current studies, six research directions that can be further explored in the future are presented.

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“…In general, the average deceleration will be based on experimental data, taking into account different driving styles and scenarios. A general conclusion can be drawn from the experimental statistics under normal driving conditions, the deceleration rate will not exceed -3.5 m/s 2 , refer to [34]. To refine the scenario, traffic environment and acceleration data for different drivers, the deceleration and braking process is represented using a parabolic model from [35] combined with a maximum deceleration table, where the deceleration parameters can be based on different factors (e.g.…”

Section: Deceleration Gradientmentioning

confidence: 99%

Stress Testing Method for Scenario-Based Testing of Automated Driving Systems

Nalic

Eichberger

et al. 2020

IEEE Access

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Acceleration and Deceleration Calibration of Operating Speed Prediction Models for Two-Lane Mountain Highways

Cited by 32 publications

References 13 publications

Modeling drivers’ speed choice on roads with complex shapes based on the curvature of the preview trajectory

Modeling drivers’ speed choice on roads with complex shapes based on the curvature of the preview trajectory

Evolution Regularity Mining and Gating Control Method of Urban Recurrent Traffic Congestion: A Literature Review

Stress Testing Method for Scenario-Based Testing of Automated Driving Systems

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