scholarly journals Predicting Driver Behavior Using Field Experiment Data and Driving Simulator Experiment Data: Assessing Impact of Elimination of Stop Regulation at Railway Crossings

2013 ◽  
Vol 2013 ◽  
pp. 1-9 ◽  
Author(s):  
Toshihisa Sato ◽  
Motoyuki Akamatsu ◽  
Toru Shibata ◽  
Shingo Matsumoto ◽  
Naoki Hatakeyama ◽  
...  
Keyword(s):  
Field Experiments ◽  
Driving Simulator ◽  
Driver Behavior ◽  
Assessment Method ◽  
Driving Behavior ◽  
Experiment Data ◽  
Driving Speed ◽  
Railway Crossing ◽  
The Impact ◽  
Lead Vehicle

We investigated the impact of deregulating the presence of stop signs at railway crossings on car driver behavior. We estimated the probability that a driver would stop inside the crossing, thereby obstructing the tracks, when a lead vehicle suddenly stopped after the crossing and a stop regulation was eliminated. We proposed a new assessment method of the driving behavior as follows: first, collecting driving behavior data in a driving simulator and in a real road environment; then, predicting the probability based on the collected data. In the simulator experiment, we measured the distances between a lead vehicle and the driver’s vehicle and the driver’s response time to the deceleration of the leading vehicle when entering the railway crossing. We investigated the influence of the presence of two leading vehicles on the driver’s vehicle movements. The deceleration data were recorded in the field experiments. Slower driving speed led to a higher probability of stopping inside the railway crossing. The probability was higher when the vehicle in front of the leading vehicle did not slow down than when both the lead vehicle and the vehicle in front of it slowed down. Finally, advantages of our new assessment method were discussed.

scholarly journals Investigating the impact of Innovative Treatments on Driving Behavior at School Bus Stops: A Driving Simulator Study

2021 ◽  
Author(s):  
Mustafa Suhail Almallah ◽  
Shabna Sayed Mohammed ◽  
Qinaat Hussain ◽  
Wael K. M. Alhajyaseen
Keyword(s):  
Driving Simulator ◽  
Driving Behavior ◽  
School Bus ◽  
School Buses ◽  
Bus Stop ◽  
Typical Condition ◽  
Simulator Study ◽  
And Control ◽  
The Impact ◽  
Traveling Speed

The illegal overtaking/crossing of stopped school buses has been identified as one of the leading causes of students’ injuries and fatalities. The likelihood of students in getting involved in a school bus-related crash increases during loading/unloading. The main objective of this driving simulator study was to study the effectiveness of different treatments in improving students’ safety by reducing the illegal overtaking/crossing of stopped school buses. Treatments used in this research are LED, Road Narrowing and Red Pavement. All proposed treatments were compared with the control condition (i.e., typical condition in the State of Qatar). Seventy-two subjects with valid Qatari driving license were invited to participate in this study. Each subject was exposed to three situations (i.e., Situation 1: the school bus is stopped in the same traveling direction, Situation 2: the school bus is stopped in the opposite traveling direction, Situation 3: the school bus is not present at the bus stop). Results showed that LED and Road Narrowing treatments were effective in reducing the illegal overtaking/crossing of stopped school buses. Moreover, the stopping behavior for drivers in LED and Road Narrowing was more consistent compared to the Red Pavement and control conditions. Finally, LED and Road Narrowing treatments motivated drivers to reduce their traveling speed by 5.16 km/h and 5.11 km/h, respectively, even with the absence of the school bus. Taking into account the results from this study, we recommend the proposed LED and Road Narrowing as potentially effective treatments to improve students’ safety at school bus stop locations.

Effects of Human–Machine Interface Design for Intelligent Speed Adaptation on Driving Behavior and Acceptance

2005 ◽  
Vol 1937 (1) ◽  
pp. 79-86
Author(s):  
Arno M. Rook ◽  
Jeroen H. Hogema
Keyword(s):  
Interface Design ◽  
Driving Simulator ◽  
Driving Behavior ◽  
Human Machine Interface ◽  
Speed Limit ◽  
Compensatory Behavior ◽  
Driving Speed ◽  
Moving Base ◽  
Machine Interface ◽  
Intelligent Speed Adaptation

The effects of human–machine interface (HMI) design for intelligent speed adaptation (ISA) on driving behavior and acceptance were measured in a moving-base research driving simulator. Sixty-four experienced drivers participated in two simulator experiments (32 in each). During the simulated runs with ISA, the speed limit was communicated through the ISA system. The ISA system consisted of an indication of the speed limit on the speedometer and a gas pedal that could be used either as a haptic or tactile pedal or as a dead throttle. Two versions of the haptic gas pedal were examined in Experiment I: a low-force ISA (easy to overrule, informative in nature) and a high-force ISA (stronger counterforce, more compulsory in nature). Two other configurations were tested in Experiment II: a tactile pedal (a vibration on the gas pedal, informative in nature) and a dead throttle (completely restraining the driver from exceeding the speed limit). It was hypothesized that the closer the ISA is to an informative type, the higher the acceptance and the smaller the effects on driving behavior would be. This hypothesis appeared to be valid, although for both driving behavior and acceptance, not all four HMIs could be ranked unambiguously on the scale from no ISA to full ISA. In sharp curves, drivers appeared to choose a driving speed below the speed limit, irrespective of ISA. The specific road environment scenarios that were inserted to examine presupposed compensatory behavior for experienced delay indicated no signs of compensatory driving behavior.

Car-Following Model Calibration Based on Driving Simulator Data to Study Driver Characteristics and to Investigate Model Validity in Extreme Traffic Situations

2021 ◽  
pp. 036119812110326
Author(s):  
Moritz Berghaus ◽  
Eszter Kallo ◽  
Markus Oeser
Keyword(s):  
Driving Simulator ◽  
Driver Behavior ◽  
Field Studies ◽  
Driving Behavior ◽  
Model Parameters ◽  
Prediction Errors ◽  
Trajectory Data ◽  
Car Following ◽  
Model Validity ◽  
Simulator Study

In this paper we use traffic data from a driving simulator study to calibrate four different car-following models. We also present two applications for which the calibration results can be used. The first application relied on the advantage that driving simulator data also contain information on driver characteristics, for example, age, gender, or the self-assessment of driver behavior. By calibrating the models for each driver individually, the resulting model parameters could be used to analyze the influence of driver characteristics on driver behavior. The analysis revealed that certain characteristics, for example, self-identification as an aggressive driver, were reflected in the model parameters. The second application was based on the capability to simulate dangerous situations that require extreme driving behavior, which is often not included in datasets from real traffic and cannot be provoked in field studies. The model validity in these situations was analyzed by comparing the prediction errors of normal and extreme driving behavior. The results showed that all four car-following models underestimated the deceleration in an emergency braking scenario in which the drivers were momentarily shocked. The driving simulator study was validated by comparing the calibration results with those obtained from real trajectory data. We concluded that driving simulator data were suitable for the two proposed applications, although the validity of driving simulator studies must always be regarded.

Reliability-Based Design for Passing Maneuvers Based on Observational Data

2020 ◽  
Vol 2674 (7) ◽  
pp. 127-134
Author(s):  
Udai Hassein
Keyword(s):  
Field Experiments ◽  
Driving Simulator ◽  
Driver Behavior ◽  
Real Life ◽  
Estimation Method ◽  
Random Variables ◽  
Minimum Length ◽  
Reliability Model ◽  
Reliability Based Design ◽  
Proposed Model

Two-lane roadways constitute the largest proportion of road networks. Their operational characteristics are significantly different from other road classifications. Allowing passing maneuvers is considered as one of the effective measures to improve mobility levels along two-lane highways, while crash records show that head-on collisions, which usually are attributed to passing maneuvers, are among the most common and most severe types of crashes on two-lane roadways. Therefore, rational and realistic estimation of the needed passing sight distance (PSD) considering driver behavior is essential for the safe design of passing zones along two-lane highways. Several random variables help to determine the minimum length required for safe passing maneuvers. Current PSD models are based on single deterministic values of the input variables to determine PSD values. This paper presents a reliability model PSD that accounts for the variability of the input random variables to offer a better representation of real-life conditions. The objectives of this paper are: (1) to design driving simulator and field experiments for data collection, (2) to develop a PSD model using the mechanics of passing maneuvers, (3) to develop a reliability model based on the first-order second-moment (FOSM) method, and (4) to validate the model using Monte Carlo simulation. In this study, driving simulator experiments were conducted to determine the passing behavior of drivers, and field data were used to validate the proposed PSD model. The proposed model accounts for the variability in the parameters by using the mean and standard deviation in a closed form estimation method. The analysis was performed for a design speed of 80 km/h, and the corresponding PSD distribution was established. A comparison of the results of the proposed model, which reflects driver behavior, and those of existing models was presented. Using the reliability-based design method, transportation engineers can adjust the PSD to fulfill a desired probability of non-compliance.

scholarly journals Increased visual and cognitive demands emphasize the importance of meeting visual needs at all distances while driving

2020 ◽  
Author(s):  
Amigale Patoine ◽  
Laura Mikula ◽  
Sergio Mejía-Romero ◽  
Jesse Michaels ◽  
Océane Keruzore ◽  
...  
Keyword(s):  
Visual Acuity ◽  
Cognitive Load ◽  
Driving Simulator ◽  
Negative Impact ◽  
Visual Quality ◽  
Driving Behavior ◽  
Driving Safety ◽  
Quality Degradation ◽  
Driving Scenario ◽  
The Impact

ABSTRACTHaving an optimal quality of vision as well as adequate cognitive capacities is known to be essential for driving safety. However, the interaction between vision and cognitive mechanisms while driving remains unclear. We hypothesized that, in a context of high cognitive load, reduced visual acuity would have a negative impact on driving behavior, even when the acuity corresponds to the legal threshold for obtaining a driving license in Canada, and that the impact observed on driving performance would be greater with the increase in the threshold of degradation of visual acuity. In order to investigate this relationship, we examined driving behavior in a driving simulator under optimal and reduced vision conditions through two scenarios involving different levels of cognitive demand. These were: 1. a simple rural driving scenario with some pre-programmed events and 2. a highway driving scenario accompanied by a concurrent task involving the use of a navigation device. Two groups of visual quality degradation (lower/ higher) were evaluated according to their driving behavior. The results support the hypothesis: Driving behavior was less stable under reduced visual quality in the context of a high cognitive load and this effect was exacerbated when visual quality was more severely altered. These results support the idea that visual quality degradation impacts driving behavior when combined with a high mental workload driving environment while specifying that this impact is not present in the context of low cognitive load driving condition.

scholarly journals Car Following and Microscopic Traffic Simulation Under Distracted Driving

2021 ◽  
pp. 036119812110003
Author(s):  
Sunbola Zatmeh-Kanj ◽  
Tomer Toledo
Keyword(s):  
Traffic Flow ◽  
Coefficient Of Variation ◽  
Driving Simulator ◽  
Traffic Simulation ◽  
Driving Behavior ◽  
Transportation Systems ◽  
Microscopic Simulation ◽  
Microscopic Traffic Simulation ◽  
Car Following ◽  
The Impact

Microscopic simulation models have been widely used as tools to investigate the operation of traffic systems and different intelligent transportation systems applications. The fidelity of microscopic simulation tools depends on the driving behavior models that they implement. However, current models commonly do not consider human-related factors, such as distraction. The potential for distraction while driving has increased rapidly with the availability of smartphones and other connected and infotainment devices. Thus, an understanding of the impact of distraction on driving behavior is essential to improve the realism of microscopic traffic tools and support safety and other applications that are sensitive to it. This study focuses on car-following behavior in the context of distracting activities. The parameters of the well-known GM and intelligent driver models are estimated under various distraction scenarios using data collected with an experiment conducted in a driving simulator. The estimation results show that drivers are less sensitive to their leaders while talking on the phone and especially while texting. The estimated models are implemented in a microscopic traffic simulation model. The average speed, coefficient of variation of speed, acceleration noise and acceleration and deceleration time fractions were used as measures of performance indicating traffic flow and safety implications. The simulation results show deterioration of traffic flow with texting and to some extent talking on the phone: average speeds are lower and the coefficient of variation of speeds are higher. Further experimentation with varying fractions of texting drivers showed similar trends.

scholarly journals The impact of the connected environment on driving behavior and safety: A driving simulator study

2020 ◽  
Vol 144 ◽  
pp. 105643 ◽  
Author(s):  
Yasir Ali ◽  
Anshuman Sharma ◽  
Md. Mazharul Haque ◽  
Zuduo Zheng ◽  
Mohammad Saifuzzaman
Keyword(s):  
Driving Simulator ◽  
Driving Behavior ◽  
Simulator Study ◽  
The Impact

scholarly journals Increased visual and cognitive demands emphasize the importance of meeting visual needs at all distances while driving

PLoS ONE ◽  
2021 ◽  
Vol 16 (3) ◽  
pp. e0247254
Author(s):  
Amigale Patoine ◽  
Laura Mikula ◽  
Sergio Mejía-Romero ◽  
Jesse Michaels ◽  
Océane Keruzoré ◽  
...  
Keyword(s):  
Visual Acuity ◽  
Cognitive Load ◽  
Dual Task ◽  
Driving Simulator ◽  
Negative Impact ◽  
Visual Quality ◽  
Driving Behavior ◽  
Quality Degradation ◽  
Driving Scenario ◽  
The Impact

Having an optimal quality of vision as well as adequate cognitive capacities is known to be essential for driving safety. However, the interaction between vision and cognitive mechanisms while driving remains unclear. We hypothesized that, in a context of high cognitive load, reduced visual acuity would have a negative impact on driving behavior, even when the acuity corresponds to the legal threshold for obtaining a driving license in Canada, and that the impact observed on driving performance would be greater with the increase in the threshold of degradation of visual acuity. In order to investigate this relationship, we examined driving behavior in a driving simulator under optimal and reduced vision conditions through two scenarios involving different levels of cognitive demand. These were: 1. a simple rural driving scenario with some pre-programmed events and 2. a highway driving scenario accompanied by a concurrent task involving the use of a navigation device. Two groups of visual quality degradation (lower/ higher) were evaluated according to their driving behavior. The results support the hypothesis: A dual task effect was indeed observed provoking less stable driving behavior, but in addition to this, by statistically controlling the impact of cognitive load, the effect of visual load emerged in this dual task context. These results support the idea that visual quality degradation impacts driving behavior when combined with a high mental workload driving environment while specifying that this impact is not present in the context of low cognitive load driving condition.

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