Psychometric properties of driver self-image inventory for Chinese drivers and its associations with personality and driving style

This study aimed to adapt the Driver Self-image Inventory (DSII, Taubman-Ben-Ari, 2008) to Chinese drivers and examine its relationship with personality traits and driving style. Six hundred forty drivers aged 18–55 years agreed to participate in this study. Measurements included the DSII, a personality scale and a validated Chinese version of the Multidimensional Driving Style Inventory (MDSI). The results of exploratory factor analysis (n = 302) and confirmatory factor analysis (n = 305) yielded a three-factor scale with satisfactory reliability. Significant gender differences were found on the DSII factors, with male drivers scoring higher on the impulsive driver factor and lower on the cautious driver factor than female drivers. The validity of the DSII was supported by significant associations between the DSII factors and personality traits, driving style and number of traffic violations and accidents in the previous 12 months. Moreover, drivers with traffic accidents scored significantly lower on the cautious driver factor and higher on the impulsive driver factor than those without traffic accidents. These findings indicate that the reliability and validity of the Chinese version of the DSII are acceptable.     

Investigating sex,masculinity and femininity in relation to impulsive driving and driving anger expression

Human factors constitute a class of prominent road safety related factors. In the present study, human factors of driving were studied by investigating sex differences and gender roles in relation to impulsive driving and driving anger expression. A total of 425 drivers between the ages of 18 and 56 (M = 25.46, SD = 7.58) participated to the study and completed a series of questionnaires including a demographic information form, the Bem Sex Roles Inventory, the Impulsive Driver Behaviour Scale and the Driving Anger Expression Inventory. According to the ANCOVA results, male drivers showed higher functional impulsivity, lack of premeditation and use of the vehicle to express anger than female drivers. Additionally, hierarchical regression analyses showed that masculinity was positively associated with functional impulsivity, urgency and the dimensions of aggressive anger expression. However, femininity was positively associated with functional impulsivity and adaptive/constructive anger expression, but negatively associated with the dimensions of dysfunctional impulsivity and aggressive anger expression. Overall, the results showed the significant solo effects of masculinity and femininity on impulsive driver behaviours and driving anger expression, over and above the effects of sex, and the interaction between sex and gender roles. In the present study, previously reported findings indicating the relationships between sex and gender roles and driving anger expression were supported and extended by providing the literature with the contribution of answering the question how sex and gender roles are related to impulsive driver behaviours. The findings of the two related concepts of impulsive driving and driving anger expression were discussed in light of the current literature. Contributions, implications and future research directions concerning road safety practices were presented.     

The role of drivers’ social interactions in their driving behavior: Empirical evidence and implications for car-following and traffic flow

Models for describing the microscopic driving behavior rarely consider the “social effects” on drivers’ driving decisions. However, social effect can be generated due to interactions with surrounding vehicles and affect drivers’ driving behavior, e.g., the interactions result in imitating the behavior of peer drivers. Therefore, social environment and peer influence can impact the drivers’ instantaneous behavior and shift the individuals’ driving state. This study aims to explore empirical evidence for existence of a social effect, i.e., when a fast-moving vehicle passes a subject vehicle, does the driver mimic the behavior of passing vehicle? High-resolution Basic Safety Message data set (N = 151,380,578) from the Safety Pilot Model Deployment program in Ann Arbor, Michigan, is used to explore the issue. The data relates to positions, speeds, and accelerations of 63 host vehicles traveling in connected vehicles with detailed information on surrounding environment at a frequency of 10 Hz. Rigorous random parameter logit models are estimated to capture the heterogeneity among the observations and to explore if the correlates of social effect can vary both positively and negatively. Results show that subject drivers do mimic the behavior of passing vehicles –in 16 percent of passing events (N = 18,099 total passings occurred in freeways), subject vehicle drivers are observed to follow the passing vehicles accelerating. We found that only 1.2 percent of drivers normally sped up (10 km/hr in 10 s) during their trips, when they were not passed by other vehicles. However, if passed by a high speed vehicle the percentage of drivers who sped up is 16.0 percent. The speed change of at least 10 km/hr within 10 s duration is considered as accelerating threshold. Furthermore, the acceleration of subject vehicle is more likely if the speed of subject driver is higher and more surrounding vehicles are present. Interestingly, if the difference with passing vehicle speed is high, the likelihood of subject driver’s acceleration is lower, consistent with expectation that if such differences are too high, the subject driver may be minimally affected. The study provides new evidence that drivers’ social interactions can change traffic flow and implications of the study results are discussed.     

Associations between personality and driving behavior are mediated by mind-wandering tendency: A cross-national comparison of Australian and Italian drivers

ObjectiveThis study examined the associations between personality traits (i.e., neuroticism, extraversion, openness, agreeableness, and conscientiousness) and aberrant driving behaviors in a sample of Australian and Italian drivers by investigating the mediation effect of mind-wandering (MW) tendency.BackgroundAlthough unsafe driving behaviors are influenced by both a driver’s MW tendency and personality traits, the potential interaction between these variables and their association with aberrant driving behaviors has not been previously investigated.MethodNine-hundred and four active drivers (n = 452 Australians, n = 452 Italians) completed an online survey related to their self-reported personality traits, driving behaviors, and MW tendency.ResultsA multi-group path analysis showed that MW tendency significantly mediated the effects of neuroticism, extraversion, and conscientiousness on aberrant driving behavior with invariances across nationality groups.ConclusionThese results suggest that the association between personality traits and aberrant driving behaviors is partially explained by a driver’s MW tendency while driving. Further research is needed to understand these relationships using objective measures of MW while driving (e.g., the probe-caught method). The findings of this study suggest that the assessment of personality traits may have important implications for inattentive and distracted driving and fitness-to-drive evaluation purposes.     

Driving on familiar roads: Automaticity and inattention blindness

This paper describes our research into the processes that govern driver attention and behavior in familiar, well-practiced situations. The experiment examined the effects of extended practice on inattention blindness and detection of changes to the driving environment in a high-fidelity driving simulator. Participants were paid to drive a simulated road regularly over 3 months of testing. A range of measures, including detection task performance and driving performance, were collected over the course of 20 sessions. Performance from a yoked Control Group who experienced the same road scenarios in a single session was also measured. The data showed changes in what drivers reported noticing indicative of inattention blindness, and declining ratings of mental demand suggesting that many participants were “driving without awareness”. Extended practice also resulted in increased sensitivity for detecting changes to road features associated with vehicle guidance and improved performance on an embedded vehicle detection task (detection of a specific vehicle type). The data provide new light on a “tandem model” of driver behavior that includes both explicit and implicit processes involved in driving performance. The findings also suggest reasons drivers are most likely to crash at locations very near their homes.     

Physiological indicators of driver workload during car-following scenarios and takeovers in highly automated driving

This driving simulator study, conducted as a part of Horizon2020-funded L3Pilot project, investigated how different car-following situations affected driver workload, within the context of vehicle automation. Electrocardiogram (ECG) and electrodermal activity (EDA)-based physiological metrics were used as objective indicators of workload, along with self-reported workload ratings. A total of 32 drivers were divided into two equal groups, based on whether they engaged in a non-driving related task (NDRT) during automation (SAE Level 3) or monitored the drive (SAE Level 2). Drivers in both groups were exposed to two counterbalanced experimental drives, lasting ∼ 18 min each, of Short (0.5 s) and Long (1.5 s) Time Headway conditions during automated car-following (ACF), which was followed by a takeover that happened with or without a lead vehicle. Results showed that driver workload due to the NDRT was significantly higher than both monitoring the drive during ACF and manual car-following (MCF). Furthermore, the results indicated that a lead vehicle maintain a shorter THW can significantly increase driver workload during takeover scenarios, potentially affecting driver safety. This warrants further research into understanding safe time headway thresholds to be maintained by automated vehicles, without placing additional cognitive or attentional demands on the driver. Our results indicated that ECG and EDA signals are sensitive to variations in workload, which warrants further investigation on the value of combining these two signals to assess driver workload in real-time, to help future driver monitoring systems respond appropriately to the limitations of the driver, and predict their performance in the driving task, if and when they have to resume manual control of the vehicle after a period of automated driving.     

Driving in dangerous territory: Complexity and road-characteristics influence attentional demand

Car driving related attentional demands fluctuate according to route complexity and are found to be highly associated with motor-vehicle collisions (MVCs). The purpose of the current study was to explore the inherent attentional demands of scenarios that approximate common crash configurations. Sixty drivers completed a series of 20 simulated driving scenarios incorporating either rear-end or crossing path situations. For each scenario, the complexity of the driving environment was systematically manipulated in terms of vehicle handling and information processing elements. The attentional demands of each scenario were assessed by means of a peripheral detection task (PDT) as well as through a subjective measure of overall difficulty. Our results showed a reduction in PDT performance at intersections where information processing is increased as well as when handling maneuvers behind a lead vehicle were required. The results point to the appropriateness of the PDT as a sensitive measure of cognitive workload. The implications of these findings for future research and safety initiatives are discussed.     

Aging and driving in a complex world: Exploring age differences in attentional demand while driving

We investigated the relationship between attention and road complexity in a convenience sample of older drivers. The study sought to examine the impact of age-associated changes in attention in response to situations with an elevated risk of crash. Scenarios were manipulated in terms of handling and information processing complexity. Twenty-six older drivers and 30 mid-aged drivers completed a series of 20 simulated driving scenarios incorporating either rear-end or crossing path situations. For each scenario, the complexity of the driving environment was systematically manipulated in terms of vehicle handling and information processing elements. The attentional demands of half of the scenarios were assessed by means of a peripheral detection task (PDT) as well as through a subjective measure of overall difficulty. The results indicated that when information processing demands were increased, through the addition of traffic, and buildings, all participants exhibited greater workload regardless of age. While no main effects of age were observed, older drivers did exhibit significantly longer PDT responses in the low vehicle handling condition of the crossing path scenario. The results confirm the impact of environmental complexity on attention but suggest that the PDT may not be the most appropriate means of assessing attentional demands among older drivers, particularly when the driving complexity is elevated.     

Transition patterns of driving style from a traditional driving environment to a connected vehicle environment: A case of an extra-long tunnel road

To provide a better understanding of individual driver’s driving style classification in a traditional and a CV environment, spatiotemporal characteristics of vehicle trajectories on a road tunnel were extracted through a driving simulator-based experiment. Speed, acceleration, and rate of acceleration changes are selected as clustering indexes. The dynamic time warping and k-means clustering were adopted to classify participants into different risk level groups. To assess the driver behavior benefits in a CV environment, an indicator BI (behavior indicator, BI) was defined based on the standard deviation of speed, the standard deviation of acceleration, and the standard deviation of the rate of acceleration change. Then, the index BI of each driver was calculated. Furthermore, this paper explored driving style classification, not in terms of traditional driving environment, but rather the transition patterns from a traditional driving environment to a CV environment. The results revealed that inside a long tunnel, 80 % of drivers benefited from a CV environment. Moreover, drivers might need training before using a CV system, especially female drivers who have low driving mileage. In addition, the results showed that the driving style of 69 % of the drivers’ transferred from a high risk-level to a low risk-level when driving in a CV environment. The study results can be expected to improve driving training education programs and also to provide a valuable reference for developing individual in-vehicle human-machine interface projects and other proactive safety countermeasures.     

Self-concept as a risky driver: Mediating the relationship between racing video games and on-road driving violations in a community-based sample

PurposeThe mediating relationship of self-conceptions as a risky driver on self-reported driving violations was examined for players of “drive’em up” and “circuit” racing video games using an Internet survey of automobile and racing club members. Structural equation modelling (SEM) tested Fischer et al. (2012) extended socio-cognitive model on the effects of risk-glorifying media on cognitions and actions.MethodAn Internet questionnaire was developed and relied upon validated instruments or questions derived from previous surveys. Driver club members were asked about: (1) their frequency of video game playing, (2) self-perceptions as a risky driver and (3) self-reported driving violations. SEM was performed to examine mediating effects of racing video game playing on self-reported driving violations.ResultsPlaying “drive‘em up” video games positively predicted risky self-concept (β = .15, t = 2.26), which in turn, positively predicted driving violations (β = .73, t = 8.63), while playing “circuit racing” games did not predict risky self-concept, although risky self-concept did predict driving violations (β = .72, t = 8.67).ConclusionsSelf-concept as a risky driver mediated the relationship between racing video game playing and self-reported driving violations for “drive’em up”, but not for “circuit racing” video games. These findings are congruent with Fischer and colleagues’ experimental model that self-concept as a reckless driver mediated the relationship between racing video game playing for “drive’em up”, but not for “circuit racing” games and risk-taking behavior in a video of road traffic scenarios.     

Effects of personality on driving behaviors among professional truck drivers: The mediating effect of safety climate

Introduction: The number of traffic accidents involving truck drivers remains high, and strategies to eliminate the probability of such accidents have been proposed, among which enhancing the safety climate has attracted much interest. The main purpose of the current study was to validate the Chinese version of the safety climate scale for truck drivers and apply it to investigate the mediating effect of safety climate between truck driver personality and driving behavior. Method: A total of 389 male truck drivers completed the Big Five Inventory, the Chinese version of the trucking safety climate scale, the driver behavior questionnaire and the positive driver behavior scale. Results: The reliability and validity of the Chinese version of the organization-level safety climate scale and the group-level safety climate scale were confirmed through factor analysis. More importantly, a path analysis revealed that the organization-level safety climate mediated the effects of agreeableness and neuroticism on aggressive violations, ordinary violations and lapses, while the group-level safety climate mediated the influences of agreeableness, neuroticism and openness on positive behavior and all four kinds of aberrant driving behaviors. Conclusions: This study introduced the trucking safety climate scale into China and stressed the significance of improving both the organizational and the group levels of safety climate to reduce accidents involving professional truck drivers. Practical Applications: First, the adapted safety climate scale for Chinese truck drivers contributes to further investigating the role that safety climate plays in the safety problem of truck drivers in China. Moreover, the critical impacts of both levels of the trucking safety climate serve as reminders for relevant companies to not only pay attention to establishing an organization-level safety climate but also invest more effort into promoting the group-level safety climate.     

Assessment of driver’s attentional resource allocation to visual,cognitive, and action processing by brain and eye signals

Driving a vehicle is comprised of multiple tasks (e.g., monitoring the environment around the vehicle, planning the trajectory, and controlling the vehicle), and requires the allocation of capacity-limited attentional resources to visual, cognitive, and action processing; otherwise, the quality of task performance will deteriorate, increasing the risk of near-accidents or crashes. The present study proposes that variations in the total amount as well as the individual amounts of attentional resources allocated to visual, cognitive, and action processing depending on the driving situations could be objectively estimated by the combined use of three physiological measures: (1) the duration of eye blinks during driving, (2) the size of eye-fixation-related potentials (EFRPs), i.e., event-related potentials (ERPs) that are time-locked to the offset of saccadic eye-movements during driving, and (3) the size of auditory-evoked potentials (AEPs), i.e., ERPs time-locked to the onset of task-unrelated auditory stimuli discretely presented during driving. We implemented these measures when participants (N = 16) drove a vehicle on a slalom course under four driving conditions defined by a combination of two levels of speed requirement (fast and slow) and two levels of path width (narrow and wide). The findings suggested that, (1) when driving at fast compared to slow speeds, the total amount of resources allocated to overall processing increased, which consisted of an increase in the amount of resources allocated to cognitive (and possibly action) processing and a decrease in the amount of resources allocated to visual processing, and (2) when driving on narrow compared to wide paths, the total amount of resources allocated to overall processing remained almost the same (due to complementary speed reduction), which consisted of an increase in the amount of resources allocated to visual processing, a decrease in the amount of resources allocated to cognitive processing, and almost the same amount of resources allocated to action processing. The driver’s resource management strategies indicated by these results as well as the utility and limitations of the proposed method are discussed.     

Attention all ‘drivers’: You could be to blame,no matter your behaviour or the level of vehicle automation

Vehicle crashes are one of the leading causes of human deaths worldwide, with crashes predominately attributed to failures of human drivers. Whilst increasing vehicle automation is argued to reduce road crashes via decreased driver involvement, automation also raises concerns around driver blame and stakeholder responsibility. This study examines blame for crash scenarios across four different forms of driver distraction behaviours (phone, sleep, work and driving under the influence), and across four levels of vehicle automation (no automation [manual], partially automated, highly automated, fully automated), using a mixed (qualitative and quantitative) methods approach. Participants (n = 205) were randomised into one of the four levels of vehicle automation and were presented with vignette crash scenarios involving a pedestrian being hit by a vehicle. Results revealed that scenarios varying driver behavior at the time of the crash, had no significant impact on participants’ blame attribution or selected course of action. The qualitative analysis revealed that despite semantic distinction between some driver behaviours, drivers were deemed responsible for the crash. As automation increased, attribution of blame towards the driver decreased, but did not disappear. Blame simultaneously increased towards other stakeholders including the manufacturer and the government, as level of automation increased. These findings mirror that of previous research and further highlight the need for legal frameworks for crashes with automated vehicles, irrespective of driver behaviours.     

Safety at first sight? – Manual drivers’ experience and driving behavior at first contact with Level 3 vehicles in mixed traffic on the highway

Soon, manual drivers will interact with conditionally automated vehicles (CAVs; SAE Level 3) in a mixed traffic on highways. As of yet, it is largely unclear how manual drivers will perceive and react to this new type of vehicle. In a driving simulator study with N = 51 participants aged 20 to 71 years (22 female), we examined the experience and driving behavior of manual drivers at first contact with Level 3 vehicles in four realistic driving scenarios (highway entry, overtaking, merging, introduction of a speed limit) that Level 3 vehicles may handle alone once their operational domain extends beyond driving in congested traffic. We also investigated the effect of an external marking via a visual external human–machine interface (eHMI), with participants being randomly assigned to one of three experimental groups (none, correct, incorrect marking). Participants experienced each driving scenario four times, twice with a human-driven vehicle (HDV), and twice with a CAV. After each interaction, participants rated perceived driving mode of the target vehicle as well as perceived safety and comfort. Minimum time headways between participants and target vehicles served as an indicator of safety criticality in the interactions. Results showed manual driver can distinguish CAVs from HDVs based on behavioral differences. In all driving scenarios, participants rated interactions with CAVs at least as safe as interactions with HDVs. The driving data analysis showed that manual driver interactions with CAVs were largely uncritical. However, the CAVs’ strict rule-compliance led to short time headways of following manual drivers in some cases. The eHMI used in this study neither affected the subjective ratings of the manual drivers nor their driving behavior in mixed traffic. Thus, the results do not support the use of eHMIs on the highway, at least not for the eHMI design used in this study.     

Effects of environmental,vehicle and human factors on comfort in partially automated driving: A scenario-based study

Although it is key to improving acceptability, there is sparse scientific literature on the experience of humans as passengers in partially automated cars. The present study therefore investigated the influence of road type, weather conditions, traffic congestion level, vehicle speed, and human factors (e.g., trust in automated cars) on passenger comfort in an automated car classified as Level 3 according to the Society of Automotive Engineers (SAE). Participants were exposed to scenarios in which a character is driven by an SAE Level 3 automated car in different combinations of conditions (e.g., highway × heavy rain × very congested traffic × vehicle following prescribed speed). They were asked to rate their perceived comfort as if they were the protagonist. Results showed that comfort was negatively affected by driving in downtown (vs. highway), heavy rain, and congested traffic. Interaction analyses showed that reducing the speed of the vehicle improved comfort in these two last conditions, considered either individually or in combination. Cluster analysis revealed four profiles: trusting in automation, averse to speed reduction, risk averse, and mistrusting automation. These profiles were all influenced differently by the driving conditions, and corresponded to varying levels of trust in automated cars. This study suggests that optimizing comfort in automated cars should take account of both driving conditions and human profiles.     

Psychometric adaptation of the driving cost and benefit scale in Chinese drivers

The present study aimed to adapt the Driving Cost and Benefit Scale (DCBS, Taubman-Ben- Ari, 2008) to Chinese drivers and examine its relationships with driving style and traffic violations. Nine hundred drivers aged 18 to 60 years were asked to complete the DCBS and the Multidimensional Driving Style Inventory. The results of exploratory factor analysis (n = 429) and confirmatory factor analysis (n = 429) yielded a 36-item scale with satisfactory reliability. The Chinese version of the DCBS contains seven factors, including three driving cost factors (damage to self-esteem, life endangerment and distress) and four driving benefit factors (impression management, sense of control, thrill and pleasure). Significant associations between the DCBS-C factors and driving styles and traffic violations show that the discriminant validity of the scale is acceptable. Moreover, the driving cost factor of damage to self-esteem and the driving benefit factor of thrill both contributed to drivers’ traffic violations and crashes. The findings show that the reliability and validity of the Chinese version of the DCBS are acceptable, and it can be used as a tool to measure driving motivation in China.     

Influence of following vehicle’s tailway and classification on subject driver’s response to the circular yellow indication

Traffic crashes at signalized intersections are frequently linked to driver behavior at the onset of the circular yellow (CY) indication. To better understand behavioral factors that influence a driver’s decision to stop or go at an intersection, this study analyzed the behavior of the driver of a subject vehicle at the onset of the CY indication. Driver performance data from 53 participants were collected in the Oregon State University Driving Simulator, simulating scenarios of driving through high-speed intersections under various conditions. Data included interactions where the driver stopped at the stop line (n = 644) or proceeded through the intersection (n = 628) in response to a CY indication. Data were analyzed as panel data while considering 12 indicator variables related to the driver’s stop/go decision. These indicator variables included time to stop line (TTSL), tailway time, following vehicle type, vehicle speed at the onset of the CY indication, and demographics (age, gender, driving experience, level of education, personal vehicle type, number of times driving per week, number of miles driving last year, participation in previous simulation studies. A random-parameter binary logit model was used to determine contributing factors for driver decision making at the onset of CY indication while accounting for unobserved heterogeneity. Four indicator variables were significantly related to the driver’s stop/go decision, but three factors varied across observations. Findings showed that a driver’s stop/go decision in response to a CY indication was associated with the time to the stop line (TTSL), tailway time to the following vehicle, subject vehicle speed at the onset of the CY indication, and driver’s age (20–36 years), but was not significantly associated with classification of the following vehicle. Also, the findings indicated that a shorter tailway increased a subject driver’s red-light running frequency. These findings provide insights into variables that affect driver decisions in a vehicle-following situation at the onset of the CY indication. This information can help make better decisions in smart traffic control systems such as to extend/decrease the green interval slightly to avoid decisions that are more difficult.     

Evidence for the role of personality in the cognitive performance of older male drivers

ObjectivesThis study aimed to examine whether in older male drivers the level of information processing under increasing cognitive workload varies with the level of personality traits.MethodThe study involved 60 male, active drivers aged between 65 and 81 (M = 71, SD = 4.4). During passive driving they performed central (a test in the n-back scheme, in three cognitive workload levels) and peripheral (memorizing objects characteristic for road traffic) tasks presented simultaneously in the central and peripheral visual fields. A wide-angle high-fidelity collimated visual system was used to display visual stimuli. In the central task, the reaction time and its correctness were measured, while the peripheral task measured the number of memorized objects. The EPQ-R questionnaire was used to assess personality traits, and the memory functions were assessed using the MMSE test.ResultsAs the central task-induced cognitive workload increases, the performance of both central and peripheral tasks deteriorates. These tasks were performed less accurately by drivers with a higher level of psychoticism than those with a lower level of this trait. This dependency related to all levels of cognitive workload. In contrast, the drivers with a higher level of neuroticism showed a lower level of performance of the peripheral task than those with a low level of this trait. This dependency concerned only the central task with a medium cognitive workload.ConclusionThe level of personality traits, especially the ones related to the emotional sphere, contributed to the decrease in the effectiveness of action (longer reaction time, increase in the number of errors, decrease in the number of memorized objects) in the conditions of increasing cognitive workload. A higher level of neuroticism and psychoticism can be conducive to making errors on the road. This applies especially to situations that pose high demands on the driver’s cognitive system.     

Driver brake vs. steer response to sudden forward collision scenario in manual and automated driving modes

In autonomous vehicle operation, situations may arise when the driver is required to re-engage in manual control of the vehicle. Whether the control handoff from vehicle to human is done in a structured or unstructured manner, the process may be affected by the driver’s state, i.e. distracted or not. The study reported here was designed to measure a non-distracted driver’s response to a sudden forward collision (FC) event, in which the driver would assume manual control of the autonomous vehicle. Three driving scenarios were investigated: autonomous vehicle driven with full collision avoidance support, autonomous vehicle driven without collision avoidance support, and vehicle driven in manual mode.Forty-eight volunteers participated in a simulator study conducted in VIRTTEX. It was found that, at handoff, (1) drivers in manual mode tended to use evasive steering, rather than braking, compared to drivers in both the autonomous modes, (2) between subjects variations in speed were higher for the automation with collision support condition than for the other two scenarios, (3) for both autonomous driving scenarios, drivers reaction times were longer than for manual driving. In some cases the driver response was so late and the distance remaining so reduced that crash avoidance might be unfeasible. At a minimum, results of this study suggest that drivers may benefit from appropriate driver assistance technologies when a crash imminent situation is suddenly encountered.     

How does emotional intelligence predict driving behaviors among non-commercial drivers?

Dangerous driving behaviors have been found to be a leading contributor to vehicle crashes and fatalities, with more than 2.7 million people injured and 36,560 people killed in the United States in 2018 (NHTSA, 2020). Drivers’ emotions have been found to be among the leading contributors to dangerous driving behaviors. Emotions can be measured and understood through one’s emotional intelligence (EI). Previous research has confirmed the relationship between EI and dangerous driving behaviors among general driving populations in limited scope. This study analyzed dangerous driving behaviors (e.g., aggressive driving) among non-commercial US drivers. 615 US drivers ages 18 to 65 (M = 31.14, SD = 11.15) with valid US driver’s licenses (non-commercial) participated in this study. Participants completed an online survey through Qualtrics that included the Trait Emotional Intelligence Questionnaire (TEIQue-SF) to measure different dimensions of EI and the Dula Dangerous Driving Index (DDDI) and the Driving Behavior Questionnaire (DBQ) to measure dangerous driving behaviors. Furthermore, participants reported their demographic information, including age, sex, and location. Correlation analysis revealed that significant associations exist between dangerous driving behaviors and EI. The emotionality component of EI was found to be the strongest predictor of dangerous driving behaviors. The findings concluded that participants with higher EI scores engaged in less dangerous driving behavior, resulting in fewer crashes and fatalities. Thus, promoting and improving EI may be useful in preventing risky driving among non-commercial drivers. Incorporating emotional intelligence education in driver’s education, workplace training, and licensing procedures can be helpful to develop safer drivers. Further research is needed to investigate commercial drivers’ behaviors in relation to EI.