Location-based analysis of car-following behavior during braking using naturalistic driving data

2020 ◽  
Vol 47 (5) ◽  
pp. 498-505 ◽  
Author(s):  
Mostafa H. Tawfeek ◽  
Karim El-Basyouny
Keyword(s):  
Driver Assistance Systems ◽  
Behavioral Measures ◽  
Car Following ◽  
Sight Distance ◽  
Influence Area ◽  
Data Source ◽  
Naturalistic Driving ◽  
Stopping Sight Distance ◽  
Microscopic Modeling ◽  
Rich Data

This study investigates the car-following behavior during braking at intersections and segments. Car-following events were extracted from a naturalistic driving dataset, mapped using ArcGIS, and analyzed to differentiate between the intersection- and segment-related events. The intersection-related events were identified according to an intersection influence area, which was estimated based on the stopping sight distance and the speed limit. Five behavioral measures were quantified based on exploring the probability density functions (PDF) for intersection- and segment-related events. The results showed that there were significant differences between the PDFs of the measures for both cases. Moreover, it was indicated that drivers tend to be more aggressive at intersections compared with segments. Thus, it is crucial to consider the driver’s location when investigating driver behavior. The quantified behavioral measures are a rich data source that can be used for car-following microscopic modeling, surrogate safety analysis, and driver assistance systems development.

Evaluating Relationships between Perception-Reaction Times, Emergency Deceleration Rates, and Crash Outcomes using Naturalistic Driving Data

2020 ◽  
pp. 036119812096660
Author(s):  
Jonathan S. Wood ◽  
Shaohu Zhang
Keyword(s):  
Real World ◽  
Predictive Models ◽  
Prediction Models ◽  
Reaction Times ◽  
Deceleration Rate ◽  
Sight Distance ◽  
Naturalistic Driving ◽  
Design Speed ◽  
Stopping Sight Distance ◽  
Roadway Design

Perception-reaction time (PRT) and deceleration rate are two key components in geometric design of highways and streets. Combined with a design speed, they determine the minimum required stopping sight distance (SSD). Current American Association of Highway Transportation Officials (AASHTO) SSD guidance is based on 90th percentile PRT and 10th percentile deceleration rate values from experiments completed in the mid-1990s. These experiments lacked real-world distractions, and so forth. Thus, the values from these experiments may not be applicable in real-world scenarios. This research evaluated (1) differences in PRTs and deceleration rates between crash and near-crash events and (2) developed predictive models for PRT and deceleration rate that could be used for roadway design. This was accomplished using (1) genetic matching (with Rosenbaum’s sensitivity analysis) and (2) quantile regression. These methods were applied to the Strategic Highway Research Program 2 (SHRP2) Naturalistic Driving Study (NDS) data. The analysis results indicated that there were differences in PRT and deceleration rates for crash and near-crash events. The specific estimates were that, on average, drivers involved in crash events took 0.487 s longer to react and decelerated at 0.018 g’s (0.58 ft/s2) slower than drivers in equivalent near-crashes. Prediction models were developed for use in roadway design. These models were used to develop tables comparing existing SSD design criteria with SSD criteria based on the results of the predictive models. These predicted values indicated that minimum design SSD values would increase by 10.5–129.2 ft, dependent on the design speed and SSD model used.

Hidden Markov Model of Lane-Changing-Based Car-Following Behavior on Freeways using Naturalistic Driving Data

2021 ◽  
pp. 036119812199938
Author(s):  
Li Zhao ◽  
Laurence Rilett ◽  
Mm Shakiul Haque
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Hidden Markov ◽  
Gaussian Mixture ◽  
Vehicle Body ◽  
Lane Change ◽  
Lane Changing ◽  
Car Following ◽  
Pilot Model ◽  
Naturalistic Driving

This paper develops a methodology for simultaneously modeling lane-changing and car-following behavior of automated vehicles on freeways. Naturalistic driving data from the Safety Pilot Model Deployment (SPMD) program are used. First, a framework to process the SPMD data is proposed using various data analytics techniques including data fusion, data mining, and machine learning. Second, pairs of automated host vehicle and their corresponding front vehicle are identified along with their lane-change and car-following relationship data. Using these data, a lane-changing-based car-following (LCCF) model, which explicitly considers lane-change and car-following behavior simultaneously, is developed. The LCCF model is based on Gaussian-mixture-based hidden Markov model theory and is disaggregated into two processes: LCCF association and LCCF dissociation. These categories are based on the result of the lane change. The overall goal is to predict a driver’s lane-change intention using the LCCF model. Results show that the model can predict the lane-change event in the order of 0.6 to 1.3 s before the moment of the vehicle body across the lane boundary. In addition, the execution times of lane-change maneuvers average between 0.55 and 0.86 s. The LCCF model allows the intention time and execution time of driver’s lane-change behavior to be forecast, which will help to develop better advanced driver assistance systems for vehicle controls with respect to lane-change and car-following warning functions.

Autonomous Vehicles’ Car-Following Drivability Evaluation Based on Driving Behavior Spectrum Reference Model

2021 ◽  
pp. 036119812199485
Author(s):  
Xiao Qi ◽  
Ying Ni ◽  
Yiming Xu ◽  
Ye Tian ◽  
Junhua Wang ◽  
...  
Keyword(s):  
Autonomous Vehicles ◽  
Reference Model ◽  
Numerical Test ◽  
Safety Evaluation ◽  
Driving Behavior ◽  
Car Following ◽  
Driving Behaviors ◽  
Human Driver ◽  
Normal Human ◽  
Naturalistic Driving

A large portion of the accidents involving autonomous vehicles (AVs) are not caused by the functionality of AV, but rather because of human intervention, since AVs’ driving behavior was not properly understood by human drivers. Such misunderstanding leads to dangerous situations during interaction between AV and human-driven vehicle (HV). However, few researches considered HV-AV interaction safety in AV safety evaluation processes. One of the solutions is to let AV mimic a normal HV’s driving behavior so as to avoid misunderstanding to the most extent. Therefore, to evaluate the differences of driving behaviors between existing AV and HV is necessary. DRIVABILITY is defined in this study to characterize the similarity between AV’s driving behaviors and expected behaviors by human drivers. A driving behavior spectrum reference model built based on human drivers’ behaviors is proposed to evaluate AVs’ car-following drivability. The indicator of the desired reaction time (DRT) is proposed to characterize the car-following drivability. Relative entropy between the DRT distribution of AV and that of the entire human driver population are used to quantify the differences between driving behaviors. A human driver behavior spectrum was configured based on naturalistic driving data by human drivers collected in Shanghai, China. It is observed in the numerical test that amongst all three types of preset AVs in the well-received simulation package VTD, the brisk AV emulates a normal human driver to the most extent (ranking at 55th percentile), while the default AV and the comfortable AV rank at 35th and 8th percentile, respectively.

Sight distance red zones on combined horizontal and sag vertical curves

1998 ◽  
Vol 25 (4) ◽  
pp. 621-630 ◽  
Author(s):  
Yasser Hassan ◽  
Said M Easa
Keyword(s):  
Quantitative Analysis ◽  
Three Dimensional ◽  
The Other ◽  
Two Dimensional ◽  
Horizontal Curve ◽  
Sight Distance ◽  
Stopping Sight Distance ◽  
Three Dimensional Models ◽  
Highway Alignment ◽  
Current Standards

Coordination of highway horizontal and vertical alignments is based on subjective guidelines in current standards. This paper presents a quantitative analysis of coordinating horizontal and sag vertical curves that are designed using two-dimensional standards. The locations where a horizontal curve should not be positioned relative to a sag vertical curve (called red zones) are identified. In the red zone, the available sight distance (computed using three-dimensional models) is less than the required sight distance. Two types of red zones, based on stopping sight distance (SSD) and preview sight distance (PVSD), are examined. The SSD red zone corresponds to the locations where an overlap between a horizontal curve and a sag vertical curve should be avoided because the three-dimensional sight distance will be less than the required SSD. The PVSD red zone corresponds to the locations where a horizontal curve should not start because drivers will not be able to perceive it and safely react to it. The SSD red zones exist for practical highway alignment parameters, and therefore designers should check the alignments for potential SSD red zones. The range of SSD red zones was found to depend on the different alignment parameters, especially the superelevation rate. On the other hand, the results showed that the PVSD red zones exist only for large values of the required PVSD, and therefore this type of red zones is not critical. This paper should be of particular interest to the highway designers and professionals concerned with highway safety.Key words: sight distance, red zone, combined alignment.

Textbooks as Data for the Study of the History of Economics: Lowly Beast or Fruitful Vineyard?

2012 ◽  
pp. 193-207
Author(s):  
Steven G. Medema
Keyword(s):  
Coase Theorem ◽  
Journal Articles ◽  
History Of Economics ◽  
History Of ◽  
New Ideas ◽  
Data Source ◽  
Rich Data

Historians of economics have paid minimal attention to the diffusion of economic ideas in the textbook literature. Given the low esteem in which textbooks are held as embodiments of scholarship and the propensity of historians of economics - and intellectual historians generally - to focus on the production of scholarship through more lofty venues such as journal articles and scholarly books, this lack of attention to the textbook literature is in some ways understandable. This article argues that the textbook literature constitutes an incredibly rich data source for the historian of economics. In doing so, it offers illustrations from the treatment of the Coase theorem in the textbooks, with a view both to showing how the textbook literature enhances our understanding of the diffusion of economic ideas and how attempts by authors to grapple with new ideas in the context of the textbook literature can result in divergences between how these ideas are treated in the scholarly and textbook literatures.

scholarly journals An Initial Investigation of the Effects of a Fully Automated Vehicle Fleet on Geometric Design

2019 ◽  
Vol 2019 ◽  
pp. 1-10 ◽  
Author(s):  
John Khoury ◽  
Kamar Amine ◽  
Rima Abi Saad
Keyword(s):  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Real Life ◽  
Geometric Design ◽  
Design Elements ◽  
Sight Distance ◽  
Human Driver ◽  
Projected Change ◽  
Vehicle Fleet ◽  
Stopping Sight Distance

This paper investigates the potential changes in the geometric design elements in response to a fully autonomous vehicle fleet. When autonomous vehicles completely replace conventional vehicles, the human driver will no longer be a concern. Currently, and for safety reasons, the human driver plays an inherent role in designing highway elements, which depend on the driver’s perception-reaction time, driver’s eye height, and other driver related parameters. This study focuses on the geometric design elements that will directly be affected by the replacement of the human driver with fully autonomous vehicles. Stopping sight distance, decision sight distance, and length of sag and crest vertical curves are geometric design elements directly affected by the projected change. Revised values for these design elements are presented and their effects are quantified using a real-life scenario. An existing roadway designed using current AASHTO standards has been redesigned with the revised values. Compared with the existing design, the proposed design shows significant economic and environmental improvements, given the elimination of the human driver.

scholarly journals Three-Dimensional Stopping Sight Distance Calculation Method under High Slope Restraint

2020 ◽  
Vol 10 (20) ◽  
pp. 7118
Author(s):  
Yonghong Yang ◽  
Jiecong Wang ◽  
Yuanbo Xia ◽  
Lan Huang
Keyword(s):  
Calculation Method ◽  
Three Dimensional ◽  
Highway Safety ◽  
Small Radius ◽  
Safety Engineering ◽  
Design Data ◽  
Important Indicator ◽  
High Slope ◽  
Sight Distance ◽  
Stopping Sight Distance

Sight distance is an important indicator to ensure the safety of drivers, and is also an indispensable evaluation basis in highway safety engineering. In mountainous highways, high slopes and small radius often lead to poor visibility and traffic accidents. Through the combined calculation of horizontal and vertical sections, this paper comprehensively considers the specific sizes of roadside clearance, high slope, as well as the position and height of the driver’s view point and other factors, and it analyzes the limited visibility of the driver in the process of driving right turn. An effective and simplified calculation method based on design data for three dimensional (3D) stopping sight distance (S.S.D.) in high fill sections is proposed. Finally, the S.S.D. inspection of the actual highway, based on design speed and operating speed, is carried out, and the sight distance of the calculated point is judged by comparing the value with the normal value and the calculation result of the horizontal sightline offset. The results show that the method proposed in this paper is consistent with the sight distance results obtained by the horizontal sightline offset method, which indicates the calculation method is accurate and provides a technical reference for S.S.D. evaluation in highway safety engineering.

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