Empirical Verification of Car-Following Parameters Using Naturalistic Driving Data on Freeway Segments

2022 ◽  
Vol 148 (2) ◽  
Author(s):  
Yirong Zhou ◽  
Juan C. Medina ◽  
Jeffrey Taylor ◽  
Xiaoyue Cathy Liu
Keyword(s):  
Car Following ◽  
Empirical Verification ◽  
Naturalistic Driving

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.

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.

Analysis of Headway and Speed Based on Driver Characteristics and Work Zone Configurations Using Naturalistic Driving Study Data

2021 ◽  
pp. 036119812110152
Author(s):  
Dan Xu ◽  
Chennan Xue ◽  
Huaguo Zhou
Keyword(s):  
Risk Perceptions ◽  
Time Series Data ◽  
Change Point Detection ◽  
Work Zone ◽  
Series Data ◽  
Car Following ◽  
Time Headway ◽  
Speed Distribution ◽  
Naturalistic Driving Study ◽  
Naturalistic Driving

The objective of this paper is to analyze headway and speed distribution based on driver characteristics and work zone (WZ) configurations by utilizing Naturalistic Driving Study (NDS) data. The NDS database provides a unique opportunity to study car-following behaviors for different driver types in various WZ configurations, which cannot be achieved from traditional field data collection. The complete NDS WZ trip data of 200 traversals and 103 individuals, including time-series data, forward-view videos, radar data, and driver characteristics, was collected at four WZ configurations, which encompasses nearly 1,100 vehicle miles traveled, 19 vehicle hours driven, and over 675,000 data points at 0.1 s intervals. First, the time headway selections were analyzed with driver characteristics such as the driver’s gender, age group, and risk perceptions to develop the headway selection table. Further, the speed profiles for different WZ configurations were established to explore the speed distribution and speed change. The best-fitted curves of time headway and speed distributions were estimated by the generalized additive model (GAM). The change point detection method was used to identify where significant changes in mean and variance of speeds occur. The results concluded that NDS data can be used to improve car-following models at WZs that have been implemented in current WZ planning and simulation tools by considering different headway distributions based on driver characteristics and their speed profiles while traversing the entire WZ.

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