A meta-analysis of the association between anger and aggressive driving

Bogdan, Smaranda R.; Măirean, Cornelia; Havârneanu, Corneliu-Eugen

doi:10.1016/j.trf.2016.05.009

Cited by 86 publications

(28 citation statements)

References 86 publications

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“…Additionally, the degree of the above changes decreased with the increase in

. Many previous researchers have proven that the drivers in the anger emotion prefer aggressive driving behavior [ 10 , 67 , 68 ]. The results in this study showed that drivers were more inclined to accelerate when they were in an angry state—i.e., preferred to seek a higher driving speed and a shorter following distance.…”

Section: Discussionmentioning

confidence: 99%

“…However, due to the complexity of the human behavior mechanism, there is still a lack of driving behavior prediction algorithms that can be applied to vehicle intelligence systems. In recent years, researchers argued that driver’s affective factors have close associations with road accidents [ 10 , 11 ]. Psychologists now widely accept that it is impossible for people to think or perform an action without engaging their emotional system, at least unconsciously [ 12 , 13 ].…”

Section: Introductionmentioning

confidence: 99%

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Differences in Driving Intention Transitions Caused by Driver’s Emotion Evolutions

Liu

Wang

2020

IJERPH

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Joining worldwide efforts to understand the relationship between driving emotion and behavior, the current study aimed at examining the influence of emotions on driving intention transition. In Study 1, taking a car-following scene as an example, we designed the driving experiments to obtain the driving data in drivers’ natural states, and a driving intention prediction model was constructed based on the HMM. Then, we analyzed the probability distribution and transition probability of driving intentions. In Study 2, we designed a series of emotion-induction experiments for eight typical driving emotions, and the drivers with induced emotion participated in the driving experiments similar to Study 1. Then, we obtained the driving data of the drivers in eight typical emotional states, and the driving intention prediction models adapted to the driver’s different emotional states were constructed based on the HMM severally. Finally, we analyzed the probabilistic differences of driving intention in divers’ natural states and different emotional states, and the findings showed the changing law of driving intention probability distribution and transfer probability caused by emotion evolution. The findings of this study can promote the development of driving behavior prediction technology and an active safety early warning system.

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“…Additionally, the degree of the above changes decreased with the increase in

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Differences in Driving Intention Transitions Caused by Driver’s Emotion Evolutions

Liu

Wang

2020

IJERPH

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“…Aside from trait, personality, and other personal factors [28][29][30][31], traffic and environmental situations that contain certain appraisal factors (e.g., whether another driver was accountable) can lead to a driver's development and experience of emotions [32]. Several representative examples, although not exhaustive, are outlined in Table 1.…”

Section: Previous Work Relating Affect To Driver Behaviors/physiologimentioning

confidence: 99%

A Review on Measuring Affect with Practical Sensors to Monitor Driver Behavior

Welch

Harnett

Lee

2019

Safety

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Using sensors to monitor signals produced by drivers is a way to help better understand how emotions contribute to unsafe driving habits. The need for intuitive machines that can interpret intentional and unintentional signals is imperative for our modern world. However, in complex human-machine work environments, many sensors will not work due to compatibility issues, noise, or practical constraints. This review focuses on practical sensors that have the potential to provide reliable monitoring and meaningful feedback to vehicle operators-such as drivers, train operators, pilots, astronauts-as well as being feasible for implementation and integration with existing work infrastructure. Such an affect-sensitive intelligent vehicle might sound an alarm if signals indicate the driver has become angry or stressed, take control of the vehicle if needed, and collaborate with other vehicles to build a stress map that improves roadway safety. Toward such vehicles, this paper provides a review of emerging sensor technologies for driver monitoring. In our research, we look at sensors used in affect detection. This insight is especially helpful for anyone challenged with accurately understanding affective information, like the autistic population. This paper also includes material on sensors and feedback for drivers from populations that may have special needs.Safety 2019, 5, 72 2 of 18 would be challenging to apply in vehicles because of noise sensitivity and data-processing requirements. Beyond engineering considerations, it would be unrealistic to expect daily drivers to apply the EEG sensor before each trip because it would be a new behavior and a time-consuming departure from the driver's routine in a world where it is already difficult to get people to wear seatbelts. Wearable sensing devices such as watches and eyeglasses that fit into a driver's established routine are more practical. Video cameras, reviewed in 2016 by Fernández et al. [1], are practical in the sense that users do not need to activate or touch them, but cameras and microphones also introduce privacy concerns and produce high bandwidth data that requires processing. New soft and textile-embedded sensor formats are promising because they can be fitted to vehicle interiors, and because they can detect safety-relevant activity using body contact data that is not as personally identifiable as video and audio streams. Figure 1 illustrates a pressure sensor for tracking a driver's grip pressure in (a) a body-worn format, and (b) a vehicle surface format. Wearable sensors such as the glove in (a) are more practical for daily use than, for example, blood sampling to measure glucose [2] or cortisol levels, or neural implants to detect brain activity in animal studies. Such invasive sampling can validate conclusions drawn from proxy signals available at the body surface, but a sensor glove is better for daily wear from the user's viewpoint. However, for this grip-tracking application, the driver would have to modify their behavior to put gloves on, would need ...

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“…Previous research has shown that the human factor was the primary cause for about 40% of road accidents and it was indirectly involved in another 50% of accidents. Thus, approximately 90% of road accidents are directly or indirectly related to the human factors (Bogdan, Mairean, & Havarneanu, 2016;Suhr, 2016;Kovacsova, Lajunen, & Roskova, 2016;Bumgarner, Webb, & Dula, 2016). Environmental factors (roadways conditions, weather conditions) are the possible reasons for about 34% of accidents and vehicle factors as causes of accidents are mentioned only in 13% of cases (Dewar & Olson, 2002).…”

Section: Relevancementioning

confidence: 99%

Prediction for Driving Behavior in Connection With Socio - Demographic Characteristics, Individual Anomia and Individual Value System

Lazdins¹,

Mārtinsone²

2017

Proceedings of the Iii International Scientific and Practical Conference

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The aim of the research was to examine characteristics of individual value system and individual anomia prediction for driving behavior. A total of 108 respondents participated in the study. The results showed that the value system integrity / disintegrity indicator predicts distracted driving, explaining 18% of variation and being statistically significant. Internal vacuum and age are statistically significant and negatively predict risky driving in 17% of variation. Age as a statistically significant factor is associated with a safe and courteous driving, this explains 12% of variation. Value system integrity / disintegrity indicator and gender are significantly negatively associated with the summary indicator of dangerous driving, this explains 22% of variation. Age is significantly negatively associated with distracted driving, explaining 30% of variation. An individual deviation from prescribed rules or customs and gender are significantly associated with aggressive behavior against other drivers, explaining 28% of variation. Age is significantly associated with risky driving; this model explains 13% of variation. Age is significantly negatively associated with the summary indicator of dangerous driving, explaining 37% of variation. The results can serve as the basis to create new measures for driving behaviour regulation and they might also be applicable to psychologists' professional activity.

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A meta-analysis of the association between anger and aggressive driving

Cited by 86 publications

References 86 publications

Differences in Driving Intention Transitions Caused by Driver’s Emotion Evolutions

Differences in Driving Intention Transitions Caused by Driver’s Emotion Evolutions

A Review on Measuring Affect with Practical Sensors to Monitor Driver Behavior

Prediction for Driving Behavior in Connection With Socio - Demographic Characteristics, Individual Anomia and Individual Value System

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