Near Crashes as Crash Surrogate for Naturalistic Driving Studies

2010 ◽  
Vol 2147 (1) ◽  
pp. 66-74 ◽  
Author(s):  
Feng Guo ◽  
Sheila G. Klauer ◽  
Jonathan M. Hankey ◽  
Thomas A. Dingus
Keyword(s):  
Naturalistic Driving ◽  
Crash Surrogate

Analysis of Naturalistic Driving Data

2008 ◽  
Vol 2061 (1) ◽  
pp. 1-8 ◽  
Author(s):  
Venky Shankar ◽  
Paul P. Jovanis ◽  
Jonathan Aguero-Valverde ◽  
Frank Gross
Keyword(s):  
Statistical Models ◽  
Formal Definition ◽  
Model Formulation ◽  
Naturalistic Driving ◽  
Definition Of ◽  
Crash Surrogate ◽  
Safety Knowledge ◽  
Model Structures

Recently completed naturalistic (i.e., unobtrusive) driving studies provide safety researchers with an unprecedented opportunity to study and analyze the occurrence of crashes and a range of near-crash events. Rather than focus on the details of the events immediately before the crash, this study seeks to identify methodological paradigms that can be used to answer questions long of interest to safety researchers. In particular, an attempt is made to shed some light on the four important components of methodological paradigms for naturalistic driving analysis: surrogates, evaluative aspects related to model structures, interpretation of driving context, and assessment of risk and associated sampling issues. The methodological paradigms are founded on a formal definition of the attributes of a valid crash surrogate that can be used in model formulation and testing. After a brief summary of the type of data collected in the studies, an overall framework for the analysis and a range of specific models to test hypotheses of interest are presented. A summary is given of how the systematic analyses with statistical models can extend safety knowledge beyond an assessment of “causes” of individual crashes.

The influence of street environments on fuel efficiency: insights from naturalistic driving

2014 ◽  
Vol 11 (8) ◽  
pp. 2291-2306 ◽  
Author(s):  
X. Wang ◽  
C. Liu ◽  
L. Kostyniuk ◽  
Q. Shen ◽  
S. Bao
Keyword(s):  
Fuel Efficiency ◽  
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.

Built environment, driving errors and violations, and crashes in naturalistic driving environment

2021 ◽  
Vol 157 ◽  
pp. 106158
Author(s):  
Numan Ahmad ◽  
Behram Wali ◽  
Asad J. Khattak ◽  
Eric Dumbaugh
Keyword(s):  
Built Environment ◽  
Naturalistic Driving

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.

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