Frequency, determinants, and consequences of different drivers’ emotions: An on-the-road study using self-reports, (observed) behaviour, and physiology

In the present study, the frequency, determinants and consequences of three relevant emotions in traffic were investigated. Based on appraisal theory, it was predicted that the combination of three appraisal components (goal congruence, blame and threat) affects the occurrence of anger, anxiety and happiness. Participants (n = 44) filled in a questionnaire containing background and personality variables, and performed a test drive in an instrumented car. During the drive, speed and heart rate were registered and the traffic environment was recorded on video. Participants verbally reported scores for emotions and perceived risk. The most frequently occurring emotion was anxiety, followed by anger and happiness. Emotions while driving were related to emotional traits. Emotions while driving were also related to traffic events: anger and anxiety were both associated with goal incongruent events, and happiness with goal congruent events. Anger was mostly associated with other-blame and anxiety with situation-blame. Anger was mostly associated with events affecting impeded progress, and anxiety with events affecting safety. Anxiety, but not anger or happiness, was associated with increased perceived risk and with increased heart rate. Participants who reported anger drove faster and exceeded the speed limit more often on a 100 km/road section than participants who did not report anger. These and other results are discussed in terms of appraisal theory and state-trait differences in emotion.     

危险类型对公交驾驶员危险知觉的影响

结合反应时方法和多维度驾驶风格量表,以140名公交驾驶员为研究对象,采用2（危险类型：隐藏危险、明显危险）×2（驾驶员分组：有无交通违规/事故记录）的混合实验设计考察危险类型对危险知觉的影响。结果发现：驾驶员对明显危险的反应时比隐藏危险短,无交通违规/事故记录驾驶员对明显和隐藏危险的反应时均比有交通违规/事故记录驾驶员短。控制驾驶风格后,危险类型主效应不再显著。结果表明,公交驾驶员危险知觉的特点随着危险类型不同而变化。     

Investigating explanations in conditional and highly automated driving: The effects of situation awareness and modality

With the level of automation increases in vehicles, such as conditional and highly automated vehicles (AVs), drivers are becoming increasingly out of the control loop, especially in unexpected driving scenarios. Although it might be not necessary to require the drivers to intervene on most occasions, it is still important to improve drivers’ situation awareness (SA) in unexpected driving scenarios to improve their trust in and acceptance of AVs. In this study, we conceptualized SA at the levels of perception (SA L1), comprehension (SA L2), and projection (SA L3), and proposed an SA level-based explanation framework based on explainable AI. Then, we examined the effects of these explanations and their modalities on drivers’ situational trust, cognitive workload, as well as explanation satisfaction. A three (SA levels: SA L1, SA L2 and SA L3) by two (explanation modalities: visual, visual + audio) between-subjects experiment was conducted with 340 participants recruited from Amazon Mechanical Turk. The results indicated that by designing the explanations using the proposed SA-based framework, participants could redirect their attention to the important objects in the traffic and understand their meaning for the AV system. This improved their SA and filled the gap of understanding the correspondence of AV’s behavior in the particular situations which also increased their situational trust in AV. The results showed that participants reported the highest trust with SA L2 explanations, although the mental workload was assessed higher in this level. The results also provided insights into the relationship between the amount of information in explanations and modalities, showing that participants were more satisfied with visual-only explanations in the SA L1 and SA L2 conditions and were more satisfied with visual and auditory explanations in the SA L3 condition. Finally, we found that the cognitive workload was also higher in SA L2, possibly because the participants were actively interpreting the results, consistent with a higher level of situational trust. These findings demonstrated that properly designed explanations, based on our proposed SA-based framework, had significant implications for explaining AV behavior in conditional and highly automated driving.     

Social Perception of Rape Victims in Dating and Married Relationships: The Role of Perpetrator’s Benevolent Sexism

Two hypothetical scenario studies examined how situational, perpetrator, and observer factors affect blame towards rape victims. In Study 1, Spanish high school students (N?=?206) read about a rape committed by a boyfriend or husband who was described as benevolently sexist or not. Study 2 (N?=?201 British college students) replicated and extended Study 1 by adding a condition in which the rapist was described as a hostile sexist. In both studies, participants’ benevolent sexism scores predicted more victim blame when the rapist was described as a husband (but not a boyfriend) who held benevolently sexist attitudes. Study 2 showed that participants’ hostile sexism scores predicted more victim blame when the rapist was described as a hostile sexist.     

The influence of attention allocation and age on intersection accidents

Many severe accidents occur in urban areas. As part of the research project UR:BAN, this study investigated the causes of driver errors (e.g., inadequate attention allocation) in urban areas when turning left at intersections. As intersection accidents are especially difficult for older drivers, differences between older and younger drivers were examined as well. In a first step, accident protocols of left turn crashes with pedestrians and bicyclists were analysed in detail, since they are the most dangerous ones. Characteristics of the oncoming traffic and the location of crossing bicyclists and pedestrians were identified as possible causes. Accordingly, critical scenarios were implemented in a static driving simulator, varying the characteristics of the oncoming traffic, the direction and location of crossing vulnerable road users. These factors were examined in a within-subject design, with two different aged groups of participants (12 aged 20–35 y, 12 aged 65+ y; between-subjects factor).The results revealed that the presence of the oncoming traffic, which was assumed to capture the drivers’ attention, did not lead to more accidents with vulnerable road users. However, this may be because many drivers waited until the oncoming traffic had passed. Unexpectedly, older drivers had fewer accidents. This may be explained by the more cautious behaviour of older drivers, who drove significantly slower and waited significantly longer at the stop line before turning. Further analyses showed that a more cautious behaviour, independently of the age, predicted accident avoidance better than attention allocation. From these results, warning systems for younger and older drivers, especially for those not driving cautious, need to be developed. This idea will be tested in future studies introducing different warning concepts.     

Instruction-prompted objective behaviors as proxy for subjective measures in a driving simulator

Interactions with other road users and interpretations of traffic situations are important aspects of driving safety. Self-reports are often used to study drivers’ perceptions and attitudes but self-reports can be inaccurate and biased because of socially desirable responding. Driving simulators offer objective measures of driver behaviors but have limited ability to elicit natural behaviors. To address this issue, we tested a driving simulator-based approach that combined realistic driving scenarios including potentially frustrating forward obstacles and delays in travel time with two different types of instructions. Participants' vehicle control behaviors and subjective perception of traffic delays were compared. Results demonstrated that behaviors collected following instructions to drive safely did not have significant associations with participants’ perceptions of the traffic delays while participants following instructions to drive quickly demonstrated behaviors that were predictive of their subjective perceptions of the traffic delays. The findings suggest that vehicle control behaviors can be used as a proxy for subjective perceptions of traffic delays. We conclude that driving simulator methodology combining instructions, realistic traffic scenarios, and adaptive analytical methods is appropriate for studying drivers’ behaviors and interactions with other road users and can minimize the need to rely on subjective self-reports.     

Investigation of drivers’ thresholds of a subjectively accepted driving performance with a focus on automated driving

Lately, the development and implementation of automated driving moved to the center of interest in the automotive industry. In this context, one of the central issues – the configuration of adequate trajectories – is mainly tackled using a technical approach. However, it appears that a technically ideal driving performance does not necessarily coincide with the drivers’ subjective preferences. This study strives to determine thresholds of a subjectively accepted driving performance regarding lateral vehicle control. A second objective is to analyze the influence of selected personal and situational factors on these thresholds. An empirical online survey with 161 participants rating video sequences of driving performances was conducted. The video sequences differed not only with regard to the lateral offset of the ego-vehicle but also concerning the weather (sun/rain) and traffic conditions (existence/driving behavior of oncoming traffic). Additionally, the participants’ driving experience and sensation seeking were considered in the data evaluation. To analyze the data, binary logistic regression analyses were calculated. They revealed that the subjective evaluation of driving performances varies primarily depending on the lateral offset of both the ego-vehicle and the oncoming traffic. The results indicate that regarding the lateral offset certain thresholds of subjectively accepted driving performances do exist. Regarding the development of automated driving systems, two issues need to be considered in order to ultimately guarantee user acceptance. First, the subjective thresholds need to be integrated into the systems’ trajectory planning. Second, the oncoming traffic’s driving behavior has to be considered.     

Examination of driver detection of roadside traffic signs and advertisements using eye tracking

Research shows that driver factors, particularly driver distraction, are the most common cause of traffic accidents. Among various visual distractions, objects such as advertisements that are commonly prevailing elements at the roadside, represent an important external distractor that may affect driving performance. Research findings on the influence that roadside signs or advertisements have on driver’s attention focus are not consistent. Therefore, with the application of eye-tracking technology, this research was designed to test several assumptions regarding drivers’ detection and perception of roadside elements. Seventeen volunteer participants, 19–76 years old, performed ten kilometres of urban driving, in which they were visually challenged with 56 traffic signs and 31 advertisements. It was found that drivers’ age is not associated with the number of roadside objects detected. Those drivers who detected more traffic signs were also more attentive to visual advertisements. Furthermore, a positive and large effect size between the number of detected street-level and raised-level advertisements was also found.     

Using physiological and behavioral measurements in a picture-based road hazard perception experiment to classify risky and safe drivers

Every year, a considerable number of people got injured or even lost their lives in road traffic accidents. To decrease the number of fatalities and injuries, researchers are seeking methods to identify and restrain drivers before the happening of actual traffic accidents, who possess dangerous driving behaviors and may cause road traffic accidents. Such methods are usually exploited to decide drivers’ fitness to drive—an indicator to describe whether they are fit for driving. The aim of this study is to measure drivers’ physiological and behavioral responses to road hazards and to extract features from measurements for further classification of risky and safe drivers. 42 drivers participated in a picture-based road hazard perception experiment, where electroencephalography (EEG), electrodermal activity (EDA), behavioral responses to road hazards, multidimensional driving style inventory (MDSI) questionnaire, and demographic information were recorded. Results indicated that 5 specific physiological features regarding to road hazard perception showed significant differences between risky and safe drivers. Subsequently, participants were classified into risky or safe drivers group by applying only the 5 features. 81.82% and 77.78% accuracy of classification were attained for risky and safe drivers, respectively. It was evidenced that using physiological and behavioral responses to evaluate drivers’ road hazard perception might be utilized as a tool to measure drivers’ fitness to drive. For further studies, improvements to future experiment design were discussed.     

A fuzzy-based driver assistance system using human cognitive parameters and driving style information

Reducing the number of traffic accidents due to human errors is an urgent need in several countries around the world. In this scenario, the use of human-robot interaction (HRI) strategies has recently shown to be a feasible solution to compensate human limitations while driving. In this work we propose a HRI system which uses the driver’s cognitive factors and driving style information to improve safety. To achieve this, deep neural networks based approaches are used to detect human cognitive parameters such as sleepiness, driver’s age and head posture. Additionally, driving style information is also obtained through speed analysis and external traffic information. Finally, a fuzzy-based decision-making stage is proposed to manage both human cognitive information and driving style, and then limit the maximum allowed speed of a vehicle. The results showed that we were able to detect human cognitive parameters such as sleepiness –63% to 88% accuracy–, driver’s age –80% accuracy– and head posture –90.42% to 97.86% accuracy– as well as driving style –87.8% average accuracy. Based on such results, the fuzzy-based architecture was able to limit the maximum allowed speed for different scenarios, reducing it from 50 km/h to 17 km/h. Moreover, the fuzzy-based method showed to be more sensitive with respect to inputs changes than a previous published weighted-based inference method.     

Taking Blame for Other People's Misconduct

Taking blame for another person's misconduct may occur at relatively high rates for less serious crimes. The authors examined individual differences and situational factors related to this phenomenon by surveying college students (n = 213) and men enrolled in substance abuse treatment programs (n = 42). Among college students, conscientiousness and delinquency predicted their likelihood of being in a situation in which it was possible to take the blame for another person's misconduct. Situational factors, including the relationship with the perpetrator, the seriousness of the offense, feelings of responsibility for the offense, and differential consequences between the offender and the blame taker, were associated with college students’ decisions to take the blame. Among substance abuse treatment participants, individuals who took the blame for another person's misconduct were more extraverted, reported feeling more loyalty toward the true perpetrator, and indicated more incentives to take the blame than individuals who did not take the blame. Links between theories of helping behavior and situational factors that predict blame taking are discussed. Copyright © 2015 John Wiley & Sons, Ltd.     

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.     

Situational Influences on Condom Purchasing

Despite the well-documented role of condoms in safe sex, people frequently fail to use them. Embarrassment and other negative emotions that occur while acquiring condoms may make it less likely that people will consistently purchase condoms, subsequently decreasing condom use. It is important to examine how situational factors affect emotions related to condom purchasing behaviors. Two experiments examined the influence of different condom purchasing situations on emotions felt and willingness to purchase condoms. Participants imagined one of three condom purchasing scenarios: anonymously at a self-checkout counter, non-anonymously at a regular checkout counter, or asking for condoms from a locked display. Results revealed that participants’ self-reported willingness to purchase condoms increased as levels of anonymity increased. Similarly, participants reported more positive emotions as anonymity increased. A negative correlation between negative emotion and willingness to purchase condoms also emerged. Overall, results suggest a need for businesses to help provide anonymity to customers, perhaps through incorporating self-checkouts into their stores to potentially decrease negative emotion felt and increase condom sales and potentially consistent condom use.     

An Examination of the Actor-Observer Effect in Young Drivers' Attributions for Their Own and Their Friends' Risky Driving

The attributions of 70 young drivers for their own and their friends' risky driving were examined using open-ended questions to determine if there were self-other differences consistent with the actor-observer effect. Six response categories were created, 4 of which were rated as more dispositional than situational by a subsample of the participants and 2 of which were rated as more situational than dispositional. While the largely dispositional category "Showing off, acting cool" was used significantly more for friends than for self, and the largely situational "In a hurry, late" was used significantly more for self than for friends, there was only limited support for the actor-observer effect overall. The participants also rated their friends as taking more risks than themselves. The actor-observer differences are suggested to be influenced primarily by motivational factors and the context in which young people observe their friends' driving. New approaches to traffic safety interventions are suggested.     

Situational influencing factors on understanding cooperative actions in automated driving

Cooperative interacting vehicles are a promising approach in the context of automated driving. To ensure understanding and acceptance of such systems, the underlying mechanisms of human cooperation in the context of traffic must be understood. In a driving simulator study, we investigated how situational factors influence cooperative behaviour in a lane change situation on a two-lane German highway during automated driving in the left lane. When another car in the right lane was approaching a slower truck, participants (N = 32) were asked by an automated system to either accelerate, decelerate or maintain speed. The driver’s scope of action, the situation’s criticality for the lane-changing vehicle and the display of intention to change lanes were manipulated. A hierarchical multinomial logistic regression revealed that a wider scope of action, a higher situation’s criticality and signalling the intention to change lanes positively influenced cooperative behaviour by accelerating and decelerating. These results might be applied to design user-centred automated cooperatively interacting vehicles.     

The benefits of collective responsibility: How ingroup reputation concern motivates prosociality in intergroup contexts

Collective responsibility processes have been investigated from the perspectives of the outgroup (e.g., collective blame) and the ingroup (e.g., collective guilt). This article extends theory and research on collective responsibility with a third perspective, namely that of the individual actor whose behavior triggers the attribution of collective blame. Four experiments (n = 78, 118, 208 and 77, respectively) tested the hypotheses that collective responsibility processes influence the individual actors' appraisals, emotions and behavior. The possibility of collective blame for their individual action prompted more prosocial behavior among participants (Experiment 1). Participants also experienced more ingroup reputation concern and in turn more negative emotions (Experiment 2–4) for a past wrongdoing if it could reflect negatively on the ingroup in the eyes of outgroups. The increased negative emotions then motivated participants to improve the ingroup's image (Experiment 4). The effects were moderated by perceived ingroup entitativity, in that activating collective blame increased ingroup reputation concern and negative emotions only for ingroups perceived as highly entitative (Experiment 3).     

Gaze tracking patterns on different types of traffic risk in early-stage Alzheimer’s disease

Many fatal accidents are caused by the driver’s inability, upon the occurrence of sudden events, to detect hazards in time, an ability that is based on the driver’s eye movement patterns, the detection of involved persons, events, objects, as well as the length of time spent on noticing same. This study intends to compare risk perception and decision-making in high-risk traffic situations between individuals with AD and controls by exploring gaze tracking and hazard detection patterns. The study included 16 individuals with early-stage AD and 26 controls. During the experiment, participants underwent several vision and cognitive tests and watched twelve videos of high-risk traffic situations while their eye movement patterns were being recorded by the eye tracking device, in order to understand how cognitive abilities affect eye movement patterns during high-risk traffic situations. The results revealed poorer cognitive abilities and diminished risk perception in individuals with AD compared with the controls and thus an inability to predict potential accidents. In terms of high-risk traffic situations involving pedestrians, traffic violations by others and view-blocking events, a significant difference in gaze patterns was found between individuals with AD and healthy seniors. The contribution of this study is in elaborating the effect of different traffic event type and specific cognitive abilities on traffic risk-detection in individuals with AD. The results of this study may aid in the formulation of driver licensing policies and the design of traffic scenarios; they also elucidate the driving behaviors of individuals with AD.     

感激情绪的认知评估体系

本文从情绪认知理论的角度出发, 通过3个研究探究与感激情绪有关的认知评价维度, 进而建立感激情绪的认知评价体系。研究1使用回忆不同类型的情绪性事件法初步探究了感激情绪的认知评估体系; 研究2使用操作认知评估维度法探究了认知评价的变化对感激体验的影响; 研究3使用故事情境法分别从个体特质和情境因素的角度, 探究个体特质与感激情绪之间的关系如何受到个体认知评价的影响, 进而为感激情绪的评价体系提供更多的证据支持。研究1发现感激情绪体验与受关怀感、符合道德规范和他人负责性3个维度之间存在正相关关系。研究2发现3个认知评估维度体验的有无均会导致个体在感激情绪体验上出现显著差异。研究3表明感激特质可以预测个体的认知评价和感激情绪体验; 同时, 受关怀感、道德规范、他人负责3个认知评价维度在感激特质与感激情绪关系间起到中介作用。结论：3个研究结果表明, 受关怀感、道德规范、他人负责性3个认知评价维度与感激密切相关, 并在区分开感激与自豪、亏欠感等情绪中起重要作用。因此, 这3个认识评估维度共同组成了感激情绪认知评价体系。