Comparing the Importance of the Factors on Drivers’ Response Time to Lead Vehicle’s Braking

2020 ◽  
Vol 64 (1) ◽  
pp. 1905-1908
Author(s):  
Brian T. W. Lin ◽  
Dillon S. Funkhouser ◽  
James R. Sayer ◽  
Rini Sherony
Keyword(s):  
Response Time ◽  
Current Situation ◽  
Relative Speed ◽  
Time To Collision ◽  
Car Following ◽  
Speed Change ◽  
Naturalistic Driving ◽  
The Absolute ◽  
Gap Time

During car-following, drivers respond to the braking or deceleration of a leading vehicle based on their perceived threshold for gap, relative speed, change of the gap, time-to-collision, etc. These measures are widely used to estimate drivers’ response time to the braking of a vehicle ahead. However, it is not clear if their response is driven only by absolute thresholds or also through a comparison of the current situation with any baseline situation to which they have just been exposed. This research explored drivers’ braking response to a lead vehicle’s braking using naturalistic driving data. Two hundred and ninety-six braking events were analyzed. It was found that measures adjusted from a baseline (when the lead vehicle’s brake lights were illuminated) were more important for estimating drivers’ response time than the measures on the absolute thresholds. Predictors adjusted according to the baselines are suggested for better prediction of drivers’ response time.

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.

Car-following theory of steady-state traffic flow using time-to-collision

2011 ◽  
Vol 12 (8) ◽  
pp. 645-654 ◽  
Author(s):  
Sheng Jin ◽  
Zhi-yi Huang ◽  
Peng-fei Tao ◽  
Dian-hai Wang
Keyword(s):  
Steady State ◽  
Traffic Flow ◽  
Time To Collision ◽  
Car Following

scholarly journals A NEW CAR-FOLLOWING MODEL CONSIDERING ACCELERATION OF LEAD VEHICLE

Transport ◽  
2014 ◽  
Vol 31 (1) ◽  
pp. 1-10 ◽  
Author(s):  
Bingrong Sun ◽  
Na Wu ◽  
Ying-En Ge ◽  
Taewan Kim ◽  
Hongjun Michael Zhang
Keyword(s):  
General Motors ◽  
Weighted Sum ◽  
Relative Speed ◽  
New Model ◽  
Car Following ◽  
Stimulus Response ◽  
The Road ◽  
Car Following Model ◽  
On The Road ◽  
Lead Vehicle

For decades, the general motors (gm) car following model has received a great deal of attention and provided a basic framework to describe the interactions between vehicles on the road. It is based on the stimulus-response assumption that the following vehicle responds to the relative speed between the lead vehicle and itself. However, some of the empirical findings show that the assumption of gm model is not always true and need some modification. For example, the acceleration of the following vehicle is very sensitive to the sign of the relative speed and because of no term in the model that directly represents the leader’s acceleration, the follower’s response to the leader’s acceleration can be retarded. This paper offers a new car-following model that can be considered as a variant of the gm model that can better capture car following behavior. The new model treats the follower’s acceleration as a proportion of a weighted sum of the leader’s acceleration and the relative speed between the lead and following vehicles. This paper compares the new model with the original gm model numerically and the characteristics of the new parameters in the model are investigated. It is also shown that the new model overcomes the shortcomings of the original gm model identified in this paper and gives us more instruments to capture the real-world car-following behavior.

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.

Findings on the Approach Process Between Vehicles: Implications for Collision Warning

2000 ◽  
Vol 1724 (1) ◽  
pp. 21-28 ◽  
Author(s):  
Mark A. Brackstone ◽  
Beshr Sultan ◽  
Michael McDonald
Keyword(s):  
Cruise Control ◽  
Collision Warning ◽  
Time To Collision ◽  
Car Following ◽  
Brief Assessment ◽  
Road Design ◽  
Equal Importance ◽  
Instrumented Vehicle ◽  
Transport Telematics ◽  
Few Data

Over the past 10 years there has been a growing body of research into modeling and describing driving behavior, particularly for situations that occur on motorways. This interest has arisen from the need to assess safety and capacity benefits that could be produced by changes to road design, operation, signage, and in-vehicle advanced transport telematics, such as collision warning (CW) or autonomous cruise control. For the most part these investigations have focused on “close” or “car” following, which describes the maintenance of a time- or distance-based following headway. However, often overlooked, and of equal importance, is the “approach” process, describing how a driver decelerates when approaching a slower vehicle. There are several competing theories of the behavioral basis underlying this process, including, for example, those based on time-to-collision or optic flow. There are, however, very few data against which such models can be assessed and systems designed. Presented are the results from an exploratory, instrumented vehicle study designed to assess approach mechanisms. The two key features of the process are explored: the circumstances under which driver deceleration is instigated, and the process governing the control of the deceleration itself. Finally, there is a brief assessment of the implications of these findings for the design of CW systems, in which realistic warnings may prove vital to their acceptance by the driving public.

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