Traffic Safety and Intelligent Transportation Systems - Research Article
Experimental research on car movement characteristics under the condition of different driving emotions
Advances in Mechanical Engineering 2018, Vol. 10(12) 1–13 Ó The Author(s) 2018 DOI: 10.1177/1687814018815369 journals.sagepub.com/home/ade
Xiao-yuan Wang1,2 , Ya-qi Liu1,3, Li-ping Liu3, Fang Wang3, Dong Kong3 and Yun-yun Wang3
Abstract Emotion is the external way to express human’s inner thoughts, which has a significant influence on human behaviors. It is an important prerequisite for studying the intrinsic affect mechanism of emotions on behaviors certain. In this article, drivers’ emotional induction experiment, actual and virtual driving experiments are designed to obtain the multi-source dynamic data of human–vehicle–environment under the condition of different emotions. The influences of emotions’ changes on car movement characteristics of different types of drivers are explored. Changing law of car movement characteristics under the condition of different emotions can be obtained finally. The research can provide theoretical basis for the future research of driver assistance system, which is of great significance to realize active vehicle safety warning and unmanned driving in the future. Keywords Driver, emotion, car movement characteristics, active vehicle safety warning
Date received: 5 July 2018; accepted: 25 October 2018 Handling Editor: Marianna Imprialou
Introduction With the development of transportation industry, the number of vehicles has been increasing day by day. The conflicts among human, vehicle, and environment in the road traffic system have become increasingly prominent and traffic accidents occur frequently. Statistics and analysis found that more than 90% of the occurred traffic accidents were caused by human, in which, more than 70% was caused by drivers.1 Therefore, controlling driver’s behavior is an important means to improve traffic safety. Emotion is an important factor that affects driver’s behavior and decision-making. Some research results on neuroscience, cognitive science, and psychology show that emotion plays a key role in attention distribution, behavior, inference, and decisionmaking.2 Hence, it can be seen that driver’s emotion plays a very important role in driver’s behavior and decision-making. With the rapid development of the
Internet of Things and artificial intelligence, human emotions have gradually become the focus of scholars’ research. A great deal of research on the behavioral mechanisms of drivers under the influence of emotion has been studied. Emotional states of drivers and the impact on speed, acceleration, and traffic violations were explored with a simulator by E Roidl et al.3 1
College of Electromechanical Engineering, Qingdao University of Science & Technology, Qingdao, China 2 The Joint Laboratory for Internet of Vehicles, Ministry of Education— China Mobile Communications Corporation, Tsinghua University, Beijing, China 3 School of Transportation and Vehicle Engineering, Shandong University of Technology, Zibo, China Corresponding author: Xiao-yuan Wang, College of Electromechanical Engineering, Qingdao University of Science & Technology, Qingdao 266000, China. Email:
[email protected]
Creative Commons CC BY: This article is distributed under the terms of the Creative Commons Attribution 4.0 License (http://www.creativecommons.org/licenses/by/4.0/) which permits any use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access pages (https://us.sagepub.com/en-us/nam/ open-access-at-sage).
2 Research result indicated that anger led to stronger acceleration and higher speeds. Anxiety and contempt yielded similar but weaker effects, yet showed the same negative and dangerous driving pattern as anger. Fright correlated with stronger braking momentum and lower speeds directly after the critical event. The expression of driver’s anger on the road was studied by MJM Sullman.4 Research result showed that verbal aggressive expression and use of a vehicle to express anger were significantly related to potentially crash-related conditions. The study also confirmed that those drivers who expressed their anger in an aggressive manner are more likely to be male and younger. Drivers’ physical indicators and subjective and behavioral differences under the condition of extreme anger and mild anger were analyzed by D Herrero-Ferna´ndez.5 Research result showed that high-anger drivers drove in general faster than low-anger drivers, had more accidents, a higher physiological arousal. Anger emotion on driving performance and driver’s visual attention were studied with driving simulation experiment by T Zhang et al.6 Research result showed that the driver’s behavior under the condition of anger emotion was more aggressive, and the visual field was narrower, which increased the potential danger. Anger emotion was taken as an example by Y Zhu et al.7 to study the role of music rhythm and personal familiarity with music in releasing negative emotions and alleviating negative influences. Research result showed that familiar music with a clear sense of rhythm can better reduce drivers’ anger and improve driving behavior. Y Jia et al.8 studied the correlation of driver’s anger emotion with driving style and offensive driving behavior. Research result showed that the driver’s anger and driving style were affected by factors such as gender, driving age, vehicle type, and whether he was a professional driver. The higher the driver’s score on the driving style scale, the stronger the driver’s anger expression and the more aggressive driving behavior during driving. The influence of anger emotion on driving behavior was studied by L Precht et al.9 based on natural driving data. Research result showed that although anger had not caused an increase in the frequency of driving mistakes, it has led to more frequent acts of aggression. Compared with mild anger, violent anger was often accompanied by more aggressive behavior. The effects of sad, neutral, and pleasure emotions on driving safety were studied by T Zimasa et al.10 using hazard perception video and eye tracking. Research result showed that sad emotions had the most significant influence on the driver. At this time, the driver’s reaction time and fixation duration were the longest, and the influence of pleasure emotion on the driver was not clear. In summary, previous experts and scholars have conducted certain research on the behavioral mechanism of drivers under the influence of emotions, which have promoted the development of driving
Advances in Mechanical Engineering emotions and behaviors, and laid a foundation for further studying the influence of emotions on driver behaviors. However, previous studies mainly focused on the analysis of the impact of driving emotions on driving strategy and driving behavior from the macroscopic perspective of traffic safety. Rarely did the study of the characteristics of car movement from the microscopic point of view and did not consider influence of the driver’s psychological factors on car movement characteristics, which affect the accuracy of the result to some extent. Therefore, eight kinds of common and typical driving emotions such as anger, surprise, fear, anxiety, helplessness, contempt, relief, and pleasure are selected to study the car movement characteristics under the condition of different driving emotions in this article. Emotional induction experiments, actual and virtual driving experiments are designed to collect the multisource dynamic data of human–vehicle–environment. And the drivers are classified according to their psychological characteristics. Then the car movement characteristics of different types of drivers under the condition of different emotions are analyzed. And the differences in car movement characteristics under the condition of different driving emotions are explored in more detail and from a microscopic perspective.
Method Purpose In order to obtain the car movement characteristics under the condition of different driver emotions, emotional induction experiment is designed to stimulate different emotional states of the driver. The actual and virtual driving experiments under the condition of the emotion are carried out. Multi-source dynamic data of human–vehicle–environment under the condition of different driving emotions were obtained. To cover more types of drivers, experiments subjects with wide ranges of age, driving mileage, and driving distance were selected. Human emotions are complex and changeable, and there is no one method can accurately assess the result of emotion inducement when it was used alone. Considering the above factor, multiple methods were used together to improve the accuracy of assessing emotion inducement result. In order to ensure the objectivity of experimental result, we chose the urban road which contains expressway and main road as experimental route. The experimental roads contain a variety of traffic scenes and moderate traffic density.
Subjects The sample capacity of the experiment is 56, including 34 males and 22 females. The age ranges from 18 to 50 years and the average age is 26.8 years. The driving
Wang et al.
3
Figure 1. Part of emotional visual stimulus materials.
mileage ranges from 0.1 to 200,000 km and the average driving distance is 12,000 km.
Experimental condition Emotional induction material. Emotional induction material is the basis to induce emotions. The emotional induction material used in this experiment mainly includes International Affective Picture System (IAPS) and Chinese Affective Picture System (CAPS). IAPS is authoritative emotional stimulus material in the international. CAPS is the emotional stimulus material that adapts to Chinese unique social and cultural background. According to different sensory channels of the material, emotional induction materials used in this experiment include visual stimulus materials (words, pictures), auditory stimulus materials (emotional music), and multi-channel stimulating materials (video). Part of the emotional induction materials is shown in Figure 1. Actual driving experimental equipment. The actual driving experimental equipment is mainly comprehensive experiment vehicle for road traffic, which is equipped with 32-line laser radar, BTM300-905-200 laser ranging sensor, GPS high-precision positioning system, SG299GPS non-contact multi-function speedometer, CTM-8A non-contact multi-function speedometer, X5000 vehicle recorder, PsyLAB human factors engineering experimental system, WTC-1 pedal force manipulator, high-definition camera, notebook computer, and so on. Part of the experimental equipment is shown in Figure 2. The road sections of Zhangzhou Road between Yuanshan Avenue and Jiangmeng Road in Zhangdian, Zibo (as shown in Figure 3) are selected as the experimental route to organize actual
driving experiment in the periods of off-peak with good weather and road conditions. The length of the line is about 4.5 km and the speed limit of the road is 70 km/h. Virtual driving experiment platform. Virtual driving experiment is mainly based on the simulation platform of human–vehicle–environment and driving experimental platform with multi-drivers and multi-simulators. Three-dimensional virtual road systems of the two platforms are built with the Road Builder and UCwin/Road software according to the basic properties of the actual driving road parameters such as properties and traffic volume. On these bases, virtual driving experiment was carried out and drivers were videotaped throughout the experiment. The experimental platforms and road editing interfaces are shown in Figures 4–7.
Experimental content Driving tendency survey. Driving tendency is the attitude of the driver to the objective and realistic traffic conditions and the adaptive decision-making and psychological characteristics he displays. It is divided into three types: conservative, common, and radical.11 Even in the same emotional state, the behavioral characteristics of drivers with different driving tendencies are often different. Therefore, drivers’ driving tendencies were investigated first and be taken as the classification basis of drivers. Driving tendency determination method refers to the literature.11 The 56 drivers were investigated and the result showed that the number of conservative, common, and radical drivers were 19, 21, and 16, respectively.
4
Advances in Mechanical Engineering
Figure 2. Actual driving experimental equipment.
Figure 3. Experiment route and driving scene of actual driving.
2. Driving experiment. Natural driving refers to driving that is not restricted by rigorous experimental design. Natural Driving Research (NDS) is usually a system of collecting video, audio, vehicle telemetry data, and
other sensor data, which can capture all aspects of driving for a long time. In these studies, the data were obtained in close agreement with the natural conditions of drivers who usually drive in the wild. Normally, the
Wang et al.
5
Figure 4. Simulation platform of human–vehicle–environment.
driver’s own vehicle is equipped with instruments (as inconspicuous as possible), and each driver is asked to continue using their vehicle as usual, and the data are collected throughout the vehicle’s use. In addition, driving activities are not subject to any structured experimental design. The aim is to provide natural behavior records that are not affected by the measurement process as far as possible. This contrasts with road tests carried out in similar instrumented vehicles. Common road tests require drivers to perform specific tasks on specific roads at specific times using specific technical systems in the vehicle. We think the natural driving data are the most efficient for driving emotion study. However, eight emotions were studied in this article, to capture all the data without any interference in the driving were difficult to enforce. So, driving experiments are divided into actual and virtual driving experiments. The data obtained from actual driving experiments are more similar to natural driving data, but the experimental organization is more difficult and the cycle is longer. So it is difficult to obtain large amounts of data. Virtual driving experiments are relatively simple, and the cycle is short, and the experimental organization and data collection process are relatively easy. Therefore, the virtual driving experimental data can effectively supplement actual driving experimental data. The specific process of the experiment is as follows: 1.
Preparation: Before the experiment, the experiment organizer first collected the most intense events of the subjects’ emotional experience
2.
through interviews or open questionnaires and sorted these experiences into sound and text materials with roughly the same length, which were used to induce subjects’ emotions. Before driving, install and connect the experimental equipment, and debug them to ensure normal operation and smooth progress of the experiment. Convey the precautions and reward and punishment system in the experimental process to the subjects. The precautions include the experimental process is kept secret so as to avoid interference with the emergence of emotions, try to maintain the natural driving state while driving, do not be too nervous, and do not suppress your emotions. The reward and punishment system is there will be some compensation for assisting in the successful completion of the experiment and part of the reward will be deducted if the experiment cannot be carried out normally due to subjects’ own reason. Launch the on-board experimental equipment and guide the participants to start the experimental car. Then play soothing music for the subjects to create a quiet atmosphere and keep their emotions calm. At the same time, let the subjects take about 10 min of adaptive driving. Emotional induction and driving experiments: After the preparation, an offline emotional induction experiment was conducted. Immediately after the subject’s emotions were stimulated, the driving experiment was conducted along the experimental route. During
6
Advances in Mechanical Engineering
Figure 5. Road builder interface and driving scene.
driving, corresponding online emotional induction methods were used. The activation degree of the subject’s emotion, which was divided into three levels from low to high: low–medium– high, should also be asked every 1 min. At the same time, data were collected and recorded synchronously using the above experimental equipment. After driving, subject’s expected speeds during the experiment were collected. The offline and online induction methods for different emotions are shown in Table 1.
3.
Evaluation of emotional induction effect: Emotional induction effect directly affects the accuracy and reliability of the study. In this article, natural speech questioning, self-report of emotional experience, physiological signal measurement, analysis of facial expressions and behaviors during the experiment are comprehensively used to evaluate the emotional induction effect of the subjects. The form of the emotional experience self-report is a 9-point Likert-type scale.12 Participants are asked to rank the feelings of a certain emotion from 0 (not at all) to 8
Wang et al.
Figure 6. Driving experimental platform with multi-drivers and multi-simulators.
(very strong). The higher the score, the higher the emotional intensity the participants experienced. Physiological signals are dominated by the human autonomic nervous system and endocrine system. They are not easily controlled by subjective consciousness and can objectively reflect the emotional state. Therefore, physiological measurement is a hotspot in the current emotional induction evaluation research.13 In this article, emotional induction effect was evaluated according to the signals of
Figure 7. Road editing interface of UC-win/road.
7 electrocardiogram (ECG), electromyography (EMG), respiration (RESP), and electrodermal activity (EDA) collected by human factors equipment. After driving, the subject is required to watch the video playback during his driving, fill in emotional experience self-report, and report his emotional experience at different times in the video to the experimental organizers. According to the result of emotional experience of self-report and physiological measurement, and combined with the statistical analysis result of the facial expressions and behaviors of the subjects recorded using the video detection system in the experiment process, the effectiveness of the subject’s emotional induction was finally determined. According to the above experimental steps, 56 subjects were organized to carry out actual and virtual driving experiments. Driving experiments were carried out after inducing subjects’ specific emotions. Eight different emotional driving experiments were conducted for each subject driver; a total of 448 sets of experimental data were obtained finally.
Data preprocessing The car driving data that the experiment can collect are shown in Table 2.
8
Advances in Mechanical Engineering
Table 1. Induction methods of different emotions. Offline emotional induction methods
Online emotional induction methods
First, display the pictures in the IAPS and CAPS which can induce different emotions to the subject, so that the subject can generate associations and imaginations. On this basis, let the subject watch the videos in the CAPS that can induce different emotions. And then present with the text materials that were collected before the experiment which can produce different emotional feelings to recall his feelings when the incident happened. As the induced experiment is carried out, the emotions of the subject will gradually be stimulated. In addition, tell about some strange things such as the novelty Guinness Book of Records to the subject when stimulating surprise emotion. Display the pictures or videos of severe traffic accident scene which can induce fear emotion to the subject when stimulating fear emotion In virtual driving experiment, when stimulating contempt emotion, the subject is required to complete a scenario experiment before driving. The driving scene is constructed with virtual driving platform. In the experiment, the subject is required to complete various driving tasks and ensure he can always win or exceed the task through background control. Then praise his driving skills and operation skills so as to create a strong sense of superiority and produce contempt emotion
Present with the sound materials that were collected before the experiment which can produce different emotional feelings to recall his feelings when the incident happened, so that the emotion is fully stimulated. In addition, when stimulating anxiety emotion, require subject to complete the driving task in a relatively short period of time, and if he fails to complete the task, his corresponding rewards will be reduced In virtual driving experiment, when stimulating anger emotion, other disturbed vehicles can be used to block the target car and provoke the subject to further stimulate the anger emotion. When stimulating surprise emotion, novelty banners or signs can be set up on the roadside to stimulate surprise emotion. When stimulating anxiety emotion, set the intersection for a long-time waiting for traffic scenes to induce anxiety emotion
IAPS: International Affective Picture System; CAPS: Chinese Affective Picture System.
Considering the factors such as the representativeness, dependability, and statistical analysis requirements of the test data, the original data are processed. A total of 52 sets of experimental data involving incomplete data, unstable data, abnormal data, and invalid emotional induction data were excluded. Finally, 396 sets of experimental data were determined as the final data for statistical analysis.
Result The 396 sets of valid data obtained from data preprocessing were analyzed. Considering the consistency of sets of data under different driving emotions and the sets of valid data collected in experiment under each of driving emotions, we eventually selected 43 sets of each emotion and a total of 344 sets of data for analysis. Among them, there are 120, 136, and 88 sets of data respectively corresponding to conservative, common, and radical drivers. Car movement characteristic parameters such as expected speed, acceleration frequency, braking force, time headway, and acceleration interference were analyzed, and the box plot is used to display the changing law of each characteristic parameter.
Expected speed Driver’s expected speed refers to the highest ‘‘safe’’ driving speed desired by the driver in the condition that
the vehicle runs without being restrained or substantially restrained by other vehicles.14 Obviously, different types of drivers have different expected speeds under the condition of different emotions. The expected speeds of different driving tendency drivers under the condition of different emotions are counted, and the expected speed box plot is shown in Figure 8. Statistics show that the average drivers’ expected speeds in descending order under the condition of different emotions are contempt (58.5 km/h) . anger (56.3 km/h) . anxiety (55.7 km/h) . surprise (53.1 km/ h) . pleasure (50.2 km/h) . relief (46.7 km/h) . helplessness (43.5 km/h) . fear (39.7 km/h). It can be seen that drivers’ expected speeds under the condition of contempt, anger, anxiety, surprise, and pleasure emotions are relatively high and are lower under the condition of helplessness and fear emotions. Comparing the expected speeds of different driving tendency drivers under the condition of same emotion, it can be seen that the expected speeds of the conservative drivers are the smallest, that of the common drivers are the second, and that of the radical drivers are the largest.
Acceleration frequency Acceleration frequency is the number of times the driver steps on the accelerator pedal per unit time. The acceleration frequencies of different driving tendency drivers under the condition of different emotions are
F
Expected speed (km/h)
v
Target car acceleration (m/s2)
a
Target car speed (km/h)
v
Parameters
Code
f
Braking force (N)
Dv3 Dv2 Dv1
Acceleration frequency (beats/min)
Dv6 Dv5
counted, and the acceleration frequency box plot is shown in Figure 9. Statistics show that the average acceleration frequencies in descending order under the condition of different emotions are anxiety (19.2 beats/min) . surprise (18.1 beats/min) . contempt (15.3 beats/min) . anger (15.2 beats/min) . pleasure (12.5 beats/min) . relief (9.7 beats/min) . helplessness (7.4 beats/min) . fear (5.1 beats/min). It can be seen that drivers’ acceleration frequencies under the condition of relief emotion in accordance with the general rules of driving. The acceleration frequencies in anxiety, surprise, contempt, anger, and pleasure emotion are higher and lower in helplessness and fear emotion. Comparing the acceleration frequencies of different driving tendency drivers under the condition of same emotion, it can be seen that the acceleration frequencies of the radical drivers are the highest, that of the common drivers are the second, and that of the conservative drivers are the lowest.
Braking force
Code
Dv4
Right-rear vehicle Right-front vehicle Rear vehicle Left-front vehicle
Relative speed between target car and surrounding vehicles (m/s) Parameters
Front vehicle
Dd6 Dd5 Dd4 Dd3 Dd2 Dd1 Code
Left-rear vehicle
Right-rear vehicle Rear vehicle Front vehicle Left-rear vehicle Left-front vehicle
Relative distance between target car and surrounding vehicles (m) Parameters
Table 2. Available car driving data in the experiment.
9
Right-front vehicle
Wang et al.
Braking force, which is the fore when of driver steps on the brake pedal, can be used to characterize the urgency of the driver’s braking. The braking forces of different driving tendency drivers under the condition of different emotions are counted, and the braking force box plot is shown in Figure 10. Statistics show that the average braking forces in descending order under the condition of different emotions are fear (40.8 N) . anger (38.3 N) . contempt (34.1 N) . surprise (33.5 N) . anxiety (32.8 N) . pleasure (25.4 N) . relief (19.9 N) . helplessness (19.3 N). It can be seen that drivers’ braking force under the condition of fear emotion is the highest, followed by anger, contempt, surprise, and anxiety. It is smaller under the condition of pleasure and relief emotion, and lowest under the condition of helplessness emotion. Comparing the braking forces of different driving tendency drivers with the same emotion, it can be seen that the braking forces of the radical drivers are the largest, that of the common drivers are the second, and that of the conservative drivers are the smallest.
Time headway Time headway is the time interval between the front car and the target car passing through a certain section continuously. The time headways of different driving tendency drivers under the condition of different emotions are calculated and counted, and the time headway box plot is shown in Figure 11. Statistics show that the average time headways in descending order under the condition of different
10
Advances in Mechanical Engineering
Figure 8. Expected speed box plot of different driving tendency drivers under the condition of different emotions.
Figure 9. Acceleration frequency box plot of different driving tendency drivers under the condition of different emotions.
Figure 10. Braking force box plot of different driving propensity drivers under the condition of different emotions.
emotions are fear (2.73 s) . helplessness (2.65 s) . relief (2.57 s) . pleasure (2.34 s) . surprise (2.06 s) . contempt (1.92 s) . anxiety (1.81 s) . anger (1.76 s). It can be seen that drivers’ time headways under the condition of relief emotion in accordance with the general rules of driving. The time headways are relatively long under the condition of drivers’ fear and helplessness emotions and short under the condition of drivers’ pleasure, surprise, contempt, anxiety, and anger emotions. Comparing the time headways of different driving
tendency drivers with the same emotion, it can be seen that the time headways of the conservative drivers are the longest, that of the common drivers are the second, and that of the radical drivers are the shortest.
Acceleration interference When the driver is driving on the road, he will not always maintain a constant speed, but change or swing within a certain speed range. The acceleration
Wang et al.
11
Figure 11. Time headway box plot of different driving propensity drivers under the condition of different emotions.
Figure 12. Acceleration interference box plot of different driving propensity drivers under the condition of different emotions.
interference is the description of the vehicle speed swing and was once a quantitative evaluation index of ride comfort.15 The acceleration interferences of different driving tendency drivers under the condition of different emotions are calculated and counted, and the acceleration interference box plot is shown in Figure 12. Statistics show that the average acceleration interferences in descending order under the condition of different driving emotions are anger (0.83 m/s2) . anxiety (0.79 m/s2) . contempt (0.76 m/s2) . surprise (0.58 m/ s2) . fear (0.51 m/s2) . helplessness (0.47 m/s2) . pleasure (0.35 m/s2) . relief (0.21 m/s2). It can be seen that cars’ acceleration interferences under the condition of relief and pleasure emotions are small, which indicate that the ride comfort is good. The acceleration interferences under the condition of anger, anxiety, contempt, surprise, fear, helpless emotions have increased to some extent, which indicate that the ride comfort is worse. There is no significant difference in the acceleration interference of different driving propensity drivers under the condition of different emotions.
Discussion Emotion is an important factor affecting the driver’s behavior. There are obvious differences in car
movement characteristics under the condition of different emotions. In this article, drivers’ emotional induction experiments, actual and virtual driving experiments are designed to obtain the multi-source dynamic data of human–vehicle–environment under the condition of different driving emotions. Car movement characteristics under the condition of anger, surprise, fear, anxiety, helplessness, contempt, relief, and pleasure emotions are analyzed. And the differences in car movement characteristics under the condition of different driving emotions are explored in more detail and from a microscopic perspective. Car movement characteristics such as expected speed, acceleration frequency, braking force, time headway, and acceleration interference under the condition of different driving emotions are explored in this article. Different types of drivers have different car movement characteristics in different emotions. It is analyzed that drivers in anger emotion are prone to vent or retaliate, have biased estimates toward outside things, are more prone to risky behaviors, and often behave more aggressively. Therefore, expected speed and acceleration interference are high and time headway is low. Drivers in surprise emotion are curious about the surroundings. They have a trait of searching for changeable, complex and strong sensory experience, tend to pursue new things, and are
12 more likely to adopt relatively aggressive driving behaviors. Therefore, acceleration frequency is high. The nervous centers of drivers under fear emotion are in a state of high tension and they are more cautious about the surroundings. So they prefer to choose a cautious and conservative strategy to drive. Therefore, expected speed and acceleration frequency are low; braking force and time headway are high. Drivers with anxiety emotions are prone to emerge impatient or uneasy feelings. They want to get rid of the current driving environment as quickly as possible and vent their emotions through frequent operations or aggressive driving. Therefore, acceleration frequency and acceleration interference are high. The nervous centers of drivers under helplessness emotion are in a state of depression, and their response is slower, and cannot respond quickly to the current driving environment. Therefore, their behavior is relatively conservative. Therefore, expected speed, time headway, acceleration frequency, and braking force are low. Drivers in contempt emotion tend to have a pride and self-satisfaction mentality. They will overestimate their abilities, and they will unable to correctly understand and judge the traffic environment they are in. Thus, they are easier to adopt radical driving strategies. Therefore, high expected speed is high. Drivers in relief emotion have strong perception of the outside world, have better behavioral performance. Their car movement characteristics are more and their driving conditions are also more stable. Therefore, acceleration interference is low. The nervous centers of drivers under pleasure emotion are in a state of excitement and have strong perceptions to the world outside. They will choose active driving strategies as a whole. Therefore, acceleration interference is low, but the behavioral indicators are relatively aggressive compared to relief emotion.16–18 In addition, different types of drivers tend to have different car movement characteristics even in the same emotion. The driving strategies and behaviors of radical drivers are often more risky, so the characteristics of car movement characteristics will be more intense. Conservative drivers tend to adopt relatively conservative and conservative driving strategies, and car movement characteristics will be relatively stable. It can be seen that there are significant differences in the car movement characteristics of different types of drivers under the condition of different emotions. In this article, the significant difference in drivers’ physiological, psychological, and car movement characteristics under different driving emotions was found. Driver emotions are dynamic, changeable, and complex psychological processes. Due to the difficulty in data collecting, the methods of experiment and data acquisition have some limitation, as listed: (1) Only eight common emotions during driving are studied. (2) The consideration of dynamic change in driver’s psychological characteristics is insufficient. (3) The classification
Advances in Mechanical Engineering criterion of drivers is only the driving tendency, and only representative car movement characteristics are analyzed. (4) Some external factors such as driving environment, the selection of experimental methods, and experimental subjects will bring a certain amount of error for the experiments. Aiming at eliminate the above deficiency, the focus of follow-up research topic are as follows: (1) the choice and definition of driver emotions, (2) comprehensive consideration of the dynamic evolution of driver emotions, (3) the exploration about more detailed drivers’ classification and more car movement characteristics, and (4) the reeducation of impact of external factors on the experimental result.
Conclusion Car movement characteristics under the condition of different driving emotions are explored in this article, which makes up for the lack of micro-study in the field of traffic safety, and provides a basis for the study of driver emotions and intentions. This study will lay the foundation for further exploring the influence of emotions on the intrinsic mechanism of drivers’ behaviors, and the result of this study will provide new ideas for driver behavior research in the area of active traffic safety and a theoretical basis for realizing the active vehicle safety warning. Declaration of conflicting interests The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
Funding The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This study was supported by the Joint Laboratory for Internet of Vehicles, Ministry of Education—China Mobile Communications Corporation under the project NO. ICVKF2018-03, Natural Science Foundation of Shandong Province (grant nos ZR2014FM027, ZR2016EL19), Social Science Planning Project of Shandong Province (grant no. 14CGLJ27), Project of Shandong Province Higher Educational Science and Technology Program (grant no. J15LB07), and the National Natural Science Foundation of China (grant nos 61074140, 61573009, 1508315, 51608313).
ORCID iD Xiao-yuan Wang
https://orcid.org/0000-0003-2418-7394
References 1. Yu F, Wang B, Wu X, et al. Expressway operation management. Beijing, China: China Communications Press, 2000.
Wang et al. 2. Chen WI. Research on the modeling of emotional agents’ cognitive behavior. Changsha, China: National University of Defense Technology, 2011. 3. Roidl E, Frehse B and Ho¨ger R. Emotional states of drivers and the impact on speed, acceleration and traffic violations—a simulator study. Accid Anal Prev 2014; 70: 282–292. 4. Sullman MJM. The expression of anger on the road. Safe Sci 2015; 72: 153–159. 5. Herrero-Ferna´ndez D. Psychophysiological, subjective and behavioral differences between high and low anger drivers in a simulation task. Transp Res Part F Traffic Psychol Behav 2016; 42: 365–375. 6. Zhang T, Chan AHS, Ba Y, et al. Situational driving anger, driving performance and allocation of visual attention. Transp Res Part F Traffic Psychol Behav 2016; 42: 376–388. 7. Zhu Y, Wang Y, Li G, et al. Recognizing and releasing drivers’ negative emotions by using music: evidence from driver anger. In: Proceedings of the adjunct 8th international conference on automotive user interfaces and interactive vehicular applications, Ann Arbor, MI, 24–26 October 2016, pp.173–178. New York: ACM. 8. Jia Y, Zhang L, Duan Y, et al. Analysis of drivers road rage affecting driving style and attack behavior. Chin Publ Health 2016; 32: 1373–1377. 9. Precht L, Keinath A and Krems JF. Effects of driving anger on driver behavior: results from naturalistic driving data. Transp Res Part F Traffic Psychol Behav 2017; 45: 75–92.
13 10. Zimasa T, Jamson S and Henson B. Are happy drivers safer drivers? Evidence from hazard response times and eye tracking data. Transp Res Part F Traffic Psychol Behav 2017; 46: 14–23. 11. Wang X, Zhang J and Ban X. Vehicle driving tendency identification based on dynamic human vehicle environment collaborative deduction. Beijing, China: Science Press, 2013. 12. Rottenberg J, Ray RD and Gross JJ. Emotion elicitation using films. In: Coan JA and Allen JJB (eds) The handbook of emotion elicitation and assessment. US: Oxford University Press, 2007, pp. 9–28. 13. Jiang X, Zhang J, Chen F, et al. Emotion recognition and result analysis based on J48 decision tree classifier. Comput Eng Des 2017; 38: 761–767. 14. Zheng A. The significance of expected vehicle speed and analysis of its influencing factors. J Wuhan Univ Sci Technol Nat Sci Ed 2005; 28: 61–64. 15. Herman R, Montroll EW, Potts RB, et al. Traffic dynamics: analysis of stability in car following. Oper Res 1959; 7: 86–106. 16. Ma C, Ma C, Ye Q, et al. An improved genetic algorithm for the large-scale rural highway network layout. Math Probl Eng 2014; 9: 1–6. 17. Ma C, Hao W, Pan F, et al. Road screening and distribution route multi-objective robust optimization for hazardous materials based on neural network and genetic algorithm. PLoS ONE 2018; 13: 1–22. 18. Ma C, He R and Zhang W. Path optimization of taxi carpooling. PLoS ONE 2018; 13: 1–15.