Naturalistic driving data collection to investigate into the effects of road geometrics on track behaviour

Cerni, Gianluca; Bassani, Marco

doi:10.1016/j.trc.2017.01.012

Cited by 20 publications

(10 citation statements)

References 22 publications

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“…They did not develop models that take into account the distance along the curve. Cerni and Bassani (2017) used naturalistic data from vehicles equipped with high accuracy GPS (frequencies L1, L2) in RTK (real time kinematic) mode to estimate and model the curvatures of vehicles' trajectories on two-lane rural roads termed 'operating trajectory', and compared it to the asbuilt horizontal alignment termed 'designed trajectory'. Based on this comparison the authors evaluated the geometric consistency of the road alignment and the causes that can lead vehicles to diverge from the road alignment.…”

Section: Introductionmentioning

confidence: 99%

How do drivers negotiate horizontal ramp curves in system interchanges in the Netherlands?

2019

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On interchanges there are higher probabilities of risky situations compared to uninterrupted motorway sections due to increased speed variability and higher frequency of lane-changes. In this study, we focus on understanding and modelling drivers' longitudinal speed behavior when negotiating horizontal ramp curves in interchanges in the Netherlands. For this purpose, detailed trajectory data of free-moving vehicles on 29 different curves from 6 different interchanges were collected from video images taken from a hovering helicopter. Only free-moving vehicles were chosen in order to understand how the road geometric design affects the (unhindered) driving speeds. The results of the speed profiles analysis show that for each connection, the speed profiles follow certain patterns, despite the large heterogeneity among drivers. These speed patterns were found to be significantly affected by the distance along a connection, the design characteristics of a connection, vehicle type, and drivers' heterogeneity. The impact of the distance along the connection on the speed was found to be significant and non-linear. This indicates that drivers do not maintain constant speeds, but adapt it along the connections. These models, which describe drivers' speed behavior and adaptation along different connections, are useful for improving current speed behavior models used in different microscopic simulation packages, and provide designers with a tool to estimate the speeds during the design process. The insights from this study, and the identified models, are also useful for enhancing the acceptability of automated vehicles' longitudinal behavior by adapting it to human like behavior.

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Section: Introductionmentioning

confidence: 99%

How do drivers negotiate horizontal ramp curves in system interchanges in the Netherlands?

2019

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“…In addition, this method is faster than other automatic model-selection methods. The main regression results are shown in equation 4to equation (13). The independent variables, speed, the radius of curve, and curve subtended angle, are symbolized as x 1 , x 2 , and x 3 , respectively, the units of which are km/h, m, and rad, respectively.…”

Section: Quantitative Equations Of Feature Distancesmentioning

confidence: 99%

“…Multiple regression is applied to obtain the equation between curve feature distances and speed, the radius of curve as well as curve subtended angle. The multiple regression model applied in this research is a quadratic model as follows The main regression results are shown in equation ( 4) to equation (13). The independent variables, speed, the radius of curve, and curve subtended angle, are symbolized as x 1 , x 2 , and x 3 , respectively, the units of which are km/h, m, and rad, respectively.…”

Section: Quantitative Equations Of Feature Distancesmentioning

confidence: 99%

“…11 In the aspect of analyzing the relationship between radius of curve, curve subtended angle, speed, and the trajectory, the research proposed that the qualitative analysis cannot be used in the quantitative equations. Although the quantitative equations like the trajectory models, 12,13 speed models, 14,15 and speed-trajectory models 16,17 are put forward, the output of these models is not continuous along the whole curve but special speed or lateral offset on specific positions of the curves. It is also significant to analyze the lateral control character of human drivers on curves.…”

Section: Introductionmentioning

confidence: 99%

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Human-like trapezoidal steering angle model on two-lane urban curves

Jiang

Zhou

et al. 2019

International Journal of Advanced Robotic Systems

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With the rapid development of automated vehicles, there is currently a significant amount of automated driving research. Giving automated vehicles capabilities similar to those of experienced drivers will allow them to share the road harmoniously with manned vehicles, especially on two-lane urban curves. To represent the steering behavior of experienced drivers, a series of curve feature distances are proposed, which is determined by multi-regression. These series of curve feature distances are used to generate a trapezoidal steering angle model which imitates the steering behavior of the experienced test drivers. To verify the feasibility and human-likeness of the proposed trapezoidal steering angle model, the model is used with constant vehicle speed to plan a human-like trajectory which is tracked using model predictive control. The simulation results show that the proposed trapezoidal steering angle model is human-like and could be used to give automated vehicles human-like driving capability when driving on two-lane curves.

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“…The model was verified by a set of real vehicle experiment data. Cerni and Bassani [11] considered the road design hypothesis that ''drivers will follow the road alignment with trajectories centered in the lane''. The possible driving errors or unexpected or undesired behaviors will be recognized by comparing the actual 'operating trajectory' with the 'designed trajectory'.…”

Section: Introductionmentioning

confidence: 99%

A Comparative Study of Aggressive Driving Behavior Recognition Algorithms Based on Vehicle Motion Data

Zhang

Chen

et al. 2019

IEEE Access

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Aggressive driving, amongst inappropriate driving behaviors, is largely responsible for leading to traffic accidents, which threatens both the safety and property of human beings. With the objective to reduce traffic accidents and improve road safety, effective and reliable aggressive driving recognition methods, which enables the development of driving behavior analysis and early warning systems, are urgently needed. Most recently, the research focus of aggressive recognition has shifted to the use of vehicle motion data, which has emerged as a new tool for traffic phenomenon explanation. As aggressive driving corresponds to sudden variations in data, they can be recognized based on the recorded vehicle motion data. In this paper, several kinds of anomaly recognition algorithms are studied and compared, using the motion data collected by the accelerometer and gyroscope of a smartphone mounted on the vehicle. Gaussian mixture model (GMM), partial least squares regression (PLSR), wavelet transformation, and support vector regression (SVR) are considered as the representative algorithms of statistical regression, time series analysis, and machine learning, respectively. These algorithms are evaluated by the three widely used validation metrics, including F 1 -score, precision, and recall. The empirical results show that GMM, PLSR, and SVR are promising methods for aggressive driving recognition. GMM and SVR outperform PLSR when only single-source dataset is used. The improvement of F 1 -score is almost 0.1. PLSR performs the best when multi-source datasets are used, and the F 1 -score is 0.77. GMM and SVR are more robust to hyperparameter. In addition, incorporating multi-source datasets helps improve the accuracy of aggressive driving behavior recognition. INDEX TERMSAggressive driving recognition, Gaussian mixture model, partial least squares regression, wavelet transformation, support vector regression, vehicle motion data. YONGFENG MA received the Ph.D. degree in transportation engineering from Southeast University, Nanjing, China, in 2007. He is currently an Associate Professor with the School of Transportation, Southeast University. He has been a Visiting Scholar with the Center for Road Safety, School of Civil Engineering, Purdue University, USA, from 2014 to 2015. He has authored or co-authored over 40 articles in international journals and conferences. His research interests include intelligent transportation systems, traffic safety, driving behavior analysis, and data mining.

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Naturalistic driving data collection to investigate into the effects of road geometrics on track behaviour

Cited by 20 publications

References 22 publications

How do drivers negotiate horizontal ramp curves in system interchanges in the Netherlands?

How do drivers negotiate horizontal ramp curves in system interchanges in the Netherlands?

Human-like trapezoidal steering angle model on two-lane urban curves

A Comparative Study of Aggressive Driving Behavior Recognition Algorithms Based on Vehicle Motion Data

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