Revista Argentina de Clínica Psicológica 2020, Vol. XXIX, N°1, 304-309
DOI: 10.24205/03276716.2020.40 304
S
IMULATION
A
NALYSIS ON THE
I
NFLUENCE OF
T
RAFFIC
L
IGHT
C
HANGES OVER
D
RIVERS
’
E
MOTIONS
B
ASED ON
V
IRTUAL
R
EALITY
P
LATFORM
Xiaoxia Pan*
Abstract
The changes in traffic lights at intersections may cause discomfort to drivers, and even lead to traffic accidents. In this paper, a virtual reality (VR) simulation driving platform is adopted to simulate the actual driving environment. Several drivers were selected to drive in the simulated environment. The physiological data of the drivers were collected by the data acquisition system Mindware, and their psychological behavior was recorded synchronously on BIOLAB. Then, the psychological and emotional changes of the drivers at traffic light changes were analyzed in details. The results show that, when the traffic light changes, the drivers may exhibit three kinds of behaviors, including dashing, forestalling, and greenlight-sprinting; meanwhile, the drivers experience tense, hesitant psychological changes, especially for the greenlight-sprinting behavior. The research results enrich the theories and results on how traffic light changes
affect drivers’ psychology, and help to improve road traffic safety.
Key words: Virtual Reality (VR) Platform, Traffic Signal Change, Driver Psychology, Driver Emotion, Simulation Experiment.
Received: 19-03-19 | Accepted: 15-09-19
INTRODUCTION
In recent years, with the continuous improvement of China's economic level, road transportation has developed rapidly, and the car parc of residents has been increasing continuously. Although the safety facilities on the roads and the safety of the vehicles have been greatly improved (Ben & David, 2018), road traffic accidents are still on the rise year by year. In order to guarantee route safety, as the throat of traffic flow conversion, most intersections are equipped with traffic lights, but still, they are the high-risk areas for traffic accidents, accounting for 25%-50% of the total traffic accidents (Krishnappa Babu & Lahiri, 2016). People, vehicles and roads are the three basic elements
Design and Art college, Changsha University of Science & Technology, Changsha 410076, China.
E-Mail: panxiaoxia_445@126.com
constitute road traffic, and people (drivers) are the primary influencing factor in traffic accidents (Rizzo, Schultheis, Kerns et al., 2004). Some researchers have found that, for more than 75% of traffic accidents, the drivers need to take full responsibility or primary responsibility of the accidents (Felnhofer, Kothgassner, Schmidt et al., 2015). Therefore, it’s of great significance to study the influence of traffic signal change on the driver's psychological emotions and behaviors so as to improve the safety of road traffic.
The purpose of the traffic signal setting is to better control the traffic and avoid chaos and accidents (Stradling, Meadows, & Beatty, 2000). The specifications of stop at red light, go at green light, and wait at yellow light are quite simple. In recent years, in order to facilitate the drivers to understand the start and stop status of the signal lights, most of the signal lights have been equipped a countdown display screen. However, due to the complicated
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traffic environment and the different driving skills of the drivers, there’re still phenomena such as red light running, which has resulted serious traffic safety hazard (Valmaggia, Latif, Kempton et al., 2016). As early as 1960, Gazis had studied the yellow signal light in order to eliminate the driver's dilemma status during the yellow light (Mühlberger, Wieser, & Pauli, 2008). In 2006, K. M et al. from Singapore found that the green light countdown did not reduce the red light running, but only helped encourage car stop (Lal & Craig, 2002). From the perspective of subjective psychology, Zhang Jie investigated and researched the relationship between the signal change at the intersections and the drivers’ behaviors and preferences. Wu Jing studied the decision-making behavior of the drivers at intersections equipped with light signals and countdown display screen (Figueroa, Bischof, Boulanger et al., 2005). In addition, targeting on the two aspects of driving suitability and road linearity, many scholars studied the drivers’ physiological indicators from the physiological point of view, and scholar Mao Zhe collected the physiological indicators of drivers under different mental states while driving on the driving simulator, and conducted analysis (Suzuki, Roseboom, Schwartzman et al., 2017). With the continuous development and maturity of VR technology, at home and abroad, there are more and more researches on the application of VR in vehicle driving behavior simulation, through which the vehicle driving training could be achieved, and the active safety analysis of the people-vehicle-environment could be conducted.
Based on the above analysis, this paper introduces VR technology and uses MINDWARE data acquisition system to collect the electrodermal test data of the drivers when they drive on the VR simulation driving experiment platform, meanwhile, it also adopts the BIOLAB, a driver psychological
behavior synchronous recording system, to record the electrodermal test data of the drivers obtained during traffic signal change, at last, this paper analyzes the three behaviors of the drivers and their corresponding psychological and emotional changes during traffic signal change.
VR SIMULATION DRIVING EXPERIMENT
PLATFORM
VR Technology
Virtual Reality is often referred to as VR for short, which means the virtual reality technology (Knodler, Noyce, Kacir et al., 2005). It uses computer technology to simulate the real environment, through electronic technology and sensors, VR enables users to truly perceive the vision, sound and touches in a 3D environment, as well as use and interact with the entities in the real world. Multi-perceptive, immersive, autonomy and interactive are the four most basic and important features of VR. According to the user's immersion form and participation degree, VR can be divided into distributed VR system, enhanced VR system, immersive VR system and desktop VR system.
VR simulation driving experiment platform
The vehicle driving simulators are divided into two types: development type and training type, both can provide users with the driving feeling that is close to the real vehicle, and they can simulate the real vehicle running conditions, which is especially suitable for the research of the people-vehicle-road closed-loop system. A traditional vehicle driving simulation system consists of three parts: the somatosensory simulation motion system, the scene simulation system and the vehicle dynamics simulation model. The first two provide drivers with a real driving feeling through visual and motion somatosensory simulation, however, due to the
Figure 1
.
VR simulation driving system
Vehicle dynamics model immersive environmentVirtual reality Visual feedback
Acoustic feedback Motion sensory
feedback Haptic feedback Operational
SIMULATION ANALYSIS ON THE INFLUENCE OF TRAFFIC LIGHT CHANGES OVER DRIVERS’ EMOTIONS BASED ON VIRTUAL REALITY PLATFORM 306
rapid development of VR technology, the current VR immersive environment can cover these two functions (Brookings, Wilson, & Swain, 1996). Figure 1 show a VR simulation driving system. In order to better simulate the real driving environment, the VR simulation driving experiment platform should provide a good immersive experience, the accuracy of the vehicle dynamics model should meet the requirements, and have good interaction with the visual display system.
SIMULATION EXPERIMENT OF INFLUENCE OF
TRAFFIC SIGNAL CHANGE ON DRIVER'S
PSYCHOLOGICAL EMOTIONS BASED ON VR PLATFORM
Simulation experiment design and
implementation
The skin conductance can reflect the corresponding nerve excitation and the changes in drivers’ cerebral cortex when their mood is fluctuating, therefore, this paper adopted electrodermal tests to reflect the physiological and psychological indicators of the drivers during the traffic signal change simulation experiment. This paper introduced real driving scenarios into the VR simulation driving experiment platform, and selected 6 healthy drivers (3 male and 3 female) to conduct the experiment. Before the experiment, the electrodermal test device was put on the subjects’ body correctly. In order to familiarize the subjects with the VR simulation driving experiment platform, prior to the experiment, each subject carried out a 5-10-minute training, after the experiment started, each driver drove for 30 minutes using the VR simulation driving experiment platform, with a 10-minute break in the middle. Each subject took the experiment for 4 times, and during the experiment, the MINDWARE data acquisition system was used to collect the drivers’ electrodermal test data. In the meantime, the BIOLAB, a driver psychological behavior synchronous recording system, was applied to record and analyze the data.
Analysis of simulation results
Driver behavior statistics results when traffic signal changes
Through the analysis of simulation experiment data, the drivers’ behaviors when the traffic signal changes were divided into the following three types: (1) Greenlight-dashing behavior. The greenlight-dashing behavior is that, when the green light is about to end and the red light is about to start, in order to pass through the parking line, the driver
accelerates instead of slowing down. Figure 2 shows the driving behavior test results of the drivers at different greenlight remaining time. It can be seen from the figure that the watershed of driver's behavior choice is when the remaining greenlight time is 1s and 2s, when the remaining time of the greenlight is longer or equal to 2s, most drivers (more than 98%) would choose to pass through the intersection, while if the remaining time of the greenlight is only 1s or the yellow light turns on, most drivers would choose to stop the vehicle and wait for the next greenlight to pass.
Figure 2
.
Greenlight-dashing behavior
statistics
0 20 40 60 80 100
Red light Yellow light 1s
2s 3s
Remaining green time
Perc
ent
age
Choose to pass Choose to stop
4s
(2) Greenlight-forestalling behavior. The greenlight-forestalling behavior means the driver starts the vehicle when the red light is not over and the green light has not turned on yet. Figure 3 shows the statistical results of the driver's greenlight-forestalling behavior. It can be seen from the figure that 1% and 15% of the drivers would start the vehicle 1s and 2s before the greenlight turns on, while most drivers (84%) choose to start the vehicle when the greenlight turns on.
Figure 3
.
Statistical results of the driver's
greenlight-forestalling behavior
1% 15% 84%
Green light starts to drive Start with the remaining 1s Start with the remaining 2s
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(3) Greenlight-sprinting behavior. The greenlight-sprinting behavior means that, when the red light is about to go out and the driver is coming from a distance, and at this time, it happens that there’s no vehicle waiting at the intersection, then the driver would go straight into the intersection area without slowing down. Figure 4 shows the statistical results of the driver's greenlight-sprinting behavior. It can be seen from the figure that the watershed of the driver's behavior choice is when the remaining red light time is 2s and 3s, when the remaining time of the red light is longer or equal to 3s, to avoid running at red light, most drivers would choose to slow down and stop, and when the remaining time of the red light is less than 2s, most drivers would choose to slow down and pass through the intersection.
Figure 4
.
Statistics results of the driver's
greenlight-sprinting behavior
0 20 40 60 80 100
1s 2s
3s
Remaining red light time
Perc
ent
age
Slow down, stop Slow down, pass
4s
Analysis of the influence of traffic signal change on the driver's psychological emotions
(1) Driver basic signal analysis (BSA) under the three behaviors. In this paper, the ZO and dz/dt diagrams related to driver electrodermal BSA were selected to analyze the psychological emotions of the drivers before, during and after the dashing, forestalling and greenlight-sprinting behaviors.
Figure 5 shows the BSA of the drivers before, during and after the greenlight-dashing behavior. It can be seen from the figure that the signal fluctuates significantly when the greenlight-dashing behavior occurs, indicating that the driver’s greenlight-dashing behavior is closely related to the EDA (Electrodermal Activity). Before, during and after the greenlight-dashing behavior, the driver has undergone the psychological emotions from relaxed to tense and to relaxed again.
Figure 6 shows the BSA of the drivers before, during and after the greenlight-forestalling behavior. The driver's physiological indicators fluctuate greatly when the greenlight-forestalling occurs, indicating that the driver is in a tense psychological state when the behavior occurs.
Figure 7 shows the BSA of the drivers before, during and after the greenlight-sprinting behavior. It can be seen from the figure that the drivers’ physiological data fluctuates largely before the greenlight-sprinting behavior occurs, the data decreases slightly during the behavior, while after the behavior occurs, the data is relatively stable, indicating that when the greenlight-sprinting behavior occurs, the driver's psychological state changes from a hesitant and tense state to a stable state.
Figure 5
.
Driver BSA before, during and after greenlight-dashing behavior
0.0 0.2 0.4 0.6 0.8 1.0
AC DC RMS Min Max Mode Median Mean
Before the green light In the green light After catching the green light
-2.5 -2.0 -1.5 -1.0 -0.5 0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5
AC DC RMS Min Max Mode Median
Before the green light In the green light After catching the green light
Mean
SIMULATION ANALYSIS ON THE INFLUENCE OF TRAFFIC LIGHT CHANGES OVER DRIVERS’ EMOTIONS BASED ON VIRTUAL REALITY PLATFORM 308
Figure 6
.
Driver BSA before, during and after greenlight-forestalling behavior
0.0 0.2 0.4 0.6 0.8 1.0
AC DC RMS Min Max Mode Median Mean
Before grabbing the green light Grab the green light After grabbing the green light
-4 -3 -2 -1 0 1 2 3 4
AC DC RMS Min Max Mode Median
Before grabbing the green light Grab the green light After grabbing the green light
Mean
(a) ZO map (b) dz/dt
Figure 7.
Driver BSA before, during and after greenlight-sprinting behavior
0.0 0.2 0.4 0.6 0.8 1.0
AC DC RMS Min Max Mode Median Mean
Sprint before the green light Sprint in the green light After sprinting green light
-4.5 -4.0 -3.5 -3.0 -2.5 -2.0 -1.5 -1.0 -0.5 0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5
AC DC RMS Min Max Mode Median
Sprint before the green light Sprint in the green light After sprinting green light
Mean
(a) ZO map (b) dz/dt
Combining with the above data analysis we can know that, when the traffic signal changes, drivers would exhibit tense, hesitant psychological changes and perform behaviors such as greenlight-dashing, greenlight-forestalling and greenlight-sprinting, wherein the physiological data of the greenlight-sprinting behavior changes most obviously, indicating that at this time the driver is most tense.
CONCLUSION
(1) Based on VR technology, this paper introduced the main structural functions of the VR simulation driving experiment platform, and designed and implemented the simulation experiment of the influence of traffic signal changes on the driver's psychological emotions.
(2) Through the reflection of drivers when they were driving on the VR simulation experiment platform, the driver’s behaviors during traffic signal changes were divided into three types: dashing, forestalling, and
greenlight-sprinting.
(3) Through the statistical analysis of the ZO and dz/dt data related to the BSA of driver EDA, it’s found that the when the traffic signal changes, the driver would exhibit tense, hesitant psychological changes, wherein the physiological data of the greenlight-sprinting behavior changes most obviously, indicating that at this time the driver is most tense.
Acknowledgement
This study was supported by Phased Achievements of the 2018 Hunan Provincial Department of Education Project "Training Simulation Experience of Tower Car Based on Virtual Reality (VR) Platform" (Project No. 18C0182).
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