Secondary task engagement and disengagement in the context of highly automated driving

During highly automated driving (level 3 automation according to SAE International, 2014) people are likely to increase the frequency of secondary task interactions. However, the driver must still be able to take over control within a reasonable amount of time. Previous studies mainly investigated take-over behavior by forcing participants to engage in secondary tasks prior to take over, and barely addressed how drivers voluntarily schedule secondary task processing according to the availability and predictability of automated driving modes. In the current simulator study 20 participants completed a test drive with alternating sections of manual and highly automated driving. One group had a preview on the availability of the automated driving system in upcoming sections of the track (predictive HMI), while the other drivers served as a control group. A texting task was offered during both driving modes and also prior to take-over situations. Participants were free to accept or reject a given task, taking the situational demands into account. Drivers accepted more tasks during highly automated driving. Furthermore, tasks were rejected more often prior to take-over situations in the predictive HMI group. This was accompanied by safer take-over performance. However, once engaged in a task, drivers tended to continue texting even in take-over situations. The results indicate the need to discriminate different aspects of task handling regarding self-regulation: task engagement and disengagement.     

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.     

The impact of a dedicated lane for connected and automated vehicles on the behaviour of drivers of manual vehicles

Connected and automated vehicles (CAVs) are expected to enhance traffic efficiency by driving at shorter time headways, and traffic safety by shorter reaction times. However, one of the main concerns regarding their deployment is the mixed traffic situation, in which CAVs and manually driven vehicles (MVs) share the same road.This study investigates the behavioural adaptation of MV drivers in car-following and lane changing behaviour when they drive next to a dedicated lane (DL) for CAVs and compares that to a mixed traffic situation. The expectation is that in a mixed traffic situation, the behavioural adaptation of MV drivers is negligible due to lower exposure time and scarce platoons, while concentrating the CAVs on one dedicated lane may cause significant behavioural adaptation of MV drivers due to a higher exposure time and conspicuity of CAV platoons.Fifty-one participants were asked to drive an MV on a 3-lane motorway in three different traffic scenarios, in a fixed-base driving simulator: (1) Base, only MVs were present in traffic, (2) Mixed, platoons of 2–3 CAVs driving on any lane and mixed with MVs, (3) DL, platoons of 2–3 CAVs driving only on a DL. The DL was recognizable by road signs and a buffer demarcation which separated the DL from the other lanes. A moderate penetration rate of 43% was assumed for CAVs.During the drives, the car following headways and the accepted merging gaps by participants were collected and used for comparisons of driving behaviour in different scenarios.Based on the results, we conclude that there is no significant difference in the driving behaviour between Base and Mixed scenarios at tested penetration rate, confirming our research expectation. However, in DL scenario, MV drivers drove closer to their leaders specially when driving on the middle lane next to the platoons and accepted shorter gaps (up to 12.7% shorter at on-ramps) in lane changing manoeuvres. Dedicating a lane to CAVs increases the density of CAV platoons on one lane and consequently their conspicuity becomes higher. As a result, MV drivers are influenced by CAV platoons on a DL and imitate their behaviour.The literature suggests that dedicating a lane to CAVs improves the traffic efficiency by providing more possibilities for platooning. This study shows that implementing such a solution will affect the driving behaviour of human drivers. This should be taken into consideration when evaluating the impacts of dedicated lanes on traffic efficiency and traffic safety.     

基于认知机制的有条件自动驾驶接管中的人因问题

自动驾驶能在很大程度上缓解现代交通问题并提升驾驶舒适度。有条件自动驾驶下, 驾驶员可执行非驾驶相关任务但需要在系统无法处理的状况下接管车辆。在这一关键过程中, 驾驶员需要进行注意转换并获得情境意识以成功接管。已有研究表明, 接管请求、非驾驶相关任务、驾驶情景及驾驶员因素是影响接管过程重要因素。未来可从认知机制角度研究各因素对接管过程产生的影响, 以及探究接管过程中各因素之间可能存在的交互作用。     

Urgent and non-urgent takeovers during conditional automated driving on public roads: The impact of different training programmes

Supplying training to drivers that teaches them about automated driving and requests to intervene may help them to build and maintain a mental representation of how automation works and thereby improve takeover performance. We aimed to investigate the effect of different types of training programmes about the functioning of automated driving on drivers’ takeover performance during real driving. Fifty-two participants were split into three groups for training sessions: paper (short notice), video (3-minute tutorial) and practice (short drive). After the training, participants experienced automated driving and both urgent and non-urgent requests to intervene in a Wizard-of-Oz vehicle on public roads. Participants’ takeover time, visual behaviour, mental workload, and flow levels during the requests to intervene were assessed. Our results indicated that in urgent circumstances, participants’ takeover response times were faster in the practice training condition compared to the other training conditions. Nevertheless, the practice training session did not present any other positive effect on drivers’ visual behaviour. This could indicate that prior training, particularly when reinforcing drivers' motor skills, improved their takeover response time at the latest motor stages rather than in the early sensory states. In addition, the analysis of in-vehicle videos revealed that participants’ attention was captured in the first place by the in-vehicle human-machine interface during the urgent request to intervene. This highlights the importance for designers to display information on the HMI in an appropriate way to optimise drivers’ situation awareness in critical situations.     

The influence of age on the take-over of vehicle control in highly automated driving

The growing proportion of older drivers in the population plays an increasingly relevant role in road traffic that is currently awaiting the introduction of automated vehicles. In this study, it was investigated how older drivers (⩾60 years) compared to younger drivers (⩽28 years) perform in a critical traffic event when driving highly automated. Conditions of the take-over situation were manipulated by adding a verbal non-driving task (20 questions task) and by variation of traffic density. Two age groups consisting of 36 younger and 36 older drivers drove either with or without a non-driving task on a six-lane highway. They encountered three situations with either no, medium or high traffic density where they had to regain vehicle control and evade an obstacle on the road. Older drivers reacted as fast as younger drivers, however, they differed in their modus operandi as they braked more often and more strongly and maintained a higher time-to-collision (TTC). Deterioration of take-over time and quality caused by increased traffic density and engagement in a non-driving task was on the same level for both age groups. Independent of the traffic density, there was a learning effect for both younger and older drivers in a way that the take-over time decreased, minimum TTC increased and maximum lateral acceleration decreased between the first and the last situation of the experiment. Results highlight that older drivers are able to solve critical traffic events as well as younger drivers, yet their modus operandi differs. Nevertheless, both age groups adapt to the experience of take-over situations in the same way.     

On the way to autonomous driving: How age influences the acceptance of driver assistance systems

Driver assistance systems have the potential to create the basis for future mobility solutions. They enable older generations in need of assistance to maintain their mobility even in the case of physical deficits. Therefore, we conducted a literature review and identified factors that may influence the acceptance of driver assistance systems. Based on the technology acceptance model and other influencing factors, we developed a research model that was validated with the help of a study (n = 227). The results show that, depending on age, there are differences in the acceptance of driver assistance systems and their influencing factors among old and young people. The five determinants of acceptance, perceived usefulness, perceived ease of use and trust in technology are viewed as equally important for all age groups. Differences emerge within the group of older vehicle drivers with varying weighting. For the 50–59 age group, perceived ease of use and social norm are most important, while for the 60–69 age group, perceived usefulness and trust in technology are very important. Among 70–90-year olds, trust in technology is emphasized as most important. Our results show how important it is to increase the confidence of older drivers in age-appropriate driver assistance systems, using target-group-specific advertising and public relations work.     

Effects of mode distinction,user visibility,and vehicle appearance on mode confusion when interacting with highly automated vehicles

Automated vehicles are expected to communicate with pedestrians at least during the introductory phase, for example, via LED strips, displays, or loudspeakers. While these are added to minimize confusion and increase trust, the human passenger within the vehicle could perform motions that a pedestrian could misinterpret as opposing the vehicle’s communication. To evaluate potential solutions to this problem, we conducted an online video-based within-subjects experiment (N = 59). The solutions under evaluation were mode distinction, vehicle appearance, and the visibility of the passenger via a tintable windshield. Our results show that especially the mode distinction and the conspicuous sensor attached to the automated vehicle showed positive effects. A tintable windshield, however, was negatively assessed. Thus, our work helps to design eHMI concepts to introduce automated vehicles safely by informing about feasible methods to avoid mode confusion.     

Take-over requests in highly automated driving: A crowdsourcing survey on auditory,vibrotactile, and visual displays

An important research question in the domain of highly automated driving is how to aid drivers in transitions between manual and automated control. Until highly automated cars are available, knowledge on this topic has to be obtained via simulators and self-report questionnaires. Using crowdsourcing, we surveyed 1692 people on auditory, visual, and vibrotactile take-over requests (TORs) in highly automated driving. The survey presented recordings of auditory messages and illustrations of visual and vibrational messages in traffic scenarios of various urgency levels. Multimodal TORs were the most preferred option in high-urgency scenarios. Auditory TORs were the most preferred option in low-urgency scenarios and as a confirmation message that the system is ready to switch from manual to automated mode. For low-urgency scenarios, visual-only TORs were more preferred than vibration-only TORs. Beeps with shorter interpulse intervals were perceived as more urgent, with Stevens’ power law yielding an accurate fit to the data. Spoken messages were more accepted than abstract sounds, and the female voice was more preferred than the male voice. Preferences and perceived urgency ratings were similar in middle- and high-income countries. In summary, this international survey showed that people’s preferences for TOR types in highly automated driving depend on the urgency of the situation.     

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.     

The role of organizational politics,contextual resources,and formal communication on change recipients' commitment to change: A multilevel study

Over the past decade there has been an increased interest in studying the factors that affect people's commitment to change. Drawing from the Job Demands–Resources model, in this enquiry we explored the moderating role of two contextual resources (i.e., trust in top management, history of change) and formal communication in the relationship between perceived organizational politics and commitment to change. Data were collected from 2543 employees of 84 companies representing a wide variety of industry sectors. In a first survey we collected data about the work context. Two weeks after the first survey, in a second survey we captured data on people's commitment to change. Hierarchical Linear Modeling (HLM) was used to analyse the multilevel character of the data. Consonant with our hypotheses, the findings indicate that the negative relationship between perceived organizational politics and commitment to change is moderated by “trust in top management”, “history of change”, and “formal communication”. As a group the Level 2 predictors account for 18%, 2.5%, and 10%, respectively, of the between-unit variance in continuance, normative, and affective commitment for change.     

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.     

Driving speed choice: The role of conscious monitoring and control (reinvestment) when driving

This study aimed to examine the role of reinvestment - the propensity to consciously monitor and control actions (movement specific reinvestment) and to consciously monitor and evaluate decision making processes (Decision specific reinvestment) while driving in everyday risky scenarios. The study also aimed to evaluate the association between reinvestment and previously validated driver attitude measures. Fifty one participants completed a series of questionnaires (Driving Self-Efficacy Scale, Driver Attitude Questionnaire, Movement Specific Reinvestment Scale, Decision Specific Reinvestment Scale) after which they completed a test phase in a driving simulator. In the test phase, driving scenarios included roads with different markings (i.e., double yellow, wide centrelines, wire rope barriers, Audio Tactile Profiled markings) and alerting scenarios (i.e., police car present, high crash risk area sign, reduced speed zone). Results revealed that on risky roads (wide centrelines), participants with a high propensity for decision specific reinvestment drove slower than those with a low propensity. Driver experience, attitudes towards speeding and scores on the Decision Reinvestment subscale of the Decision Specific Reinvestment Scale significantly predicted speed choice. More experienced participants with higher scores on the Decision Reinvestment subscale were more likely to drive slower and participants with worse attitudes towards speeding were likely to drive faster. Participants with a low propensity for movement specific reinvestment (specifically, Movement Self-Consciousness) reduced their speed to a greater extent than those with a high propensity when driving in the police car scenario. There was some evidence to suggest that high decision specific and movement specific reinvesters were more likely to be involved in crashes and receive driving infringements. The current study is the first to demonstrate a significant relationship between reinvestment and driving. The implications of these findings for road safety are discussed.     

Effects of adaptive cruise control and highly automated driving on workload and situation awareness: A review of the empirical evidence

Adaptive cruise control (ACC), a driver assistance system that controls longitudinal motion, has been introduced in consumer cars in 1995. A next milestone is highly automated driving (HAD), a system that automates both longitudinal and lateral motion. We investigated the effects of ACC and HAD on drivers’ workload and situation awareness through a meta-analysis and narrative review of simulator and on-road studies. Based on a total of 32 studies, the unweighted mean self-reported workload was 43.5% for manual driving, 38.6% for ACC driving, and 22.7% for HAD (0% = minimum, 100 = maximum on the NASA Task Load Index or Rating Scale Mental Effort). Based on 12 studies, the number of tasks completed on an in-vehicle display relative to manual driving (100%) was 112% for ACC and 261% for HAD. Drivers of a highly automated car, and to a lesser extent ACC drivers, are likely to pick up tasks that are unrelated to driving. Both ACC and HAD can result in improved situation awareness compared to manual driving if drivers are motivated or instructed to detect objects in the environment. However, if drivers are engaged in non-driving tasks, situation awareness deteriorates for ACC and HAD compared to manual driving. The results of this review are consistent with the hypothesis that, from a Human Factors perspective, HAD is markedly different from ACC driving, because the driver of a highly automated car has the possibility, for better or worse, to divert attention to secondary tasks, whereas an ACC driver still has to attend to the roadway.     

Goal priming,public transportation habit and travel mode selection: The moderating role of trait mindfulness

Habits in travel mode occur in a stable context and create strong links between travel goals and travel modes in memory. As a consequence, priming a travel goal (i.e., going to a place) in memory increases the accessibility of the associated travel mode among habitual users of this transportation mode. We posit that individual differences in attention to present moment and experience (dispositional mindfulness construct; Brown & Ryan, 2003) could moderate the interaction between travel goal priming and habit strength. In this study, habitual vs. non-habitual users of a subway line were nonconsciously primed with travel goals that were strongly associated with the use of a subway line. In the second part of the study, participants chose, among four travel modes, the one(s) that allowed reaching a specific place. Priming and habit strength contributed non-additively to decision times, but among low-mindfulness participants only. Among non-habitual users, priming speeded decisions at high levels of mindfulness, but slowed decisions at low levels of mindfulness. From a fundamental and applied point of view, discussion focuses on processes crossing habits, non-deliberated responses to environmental cues, and mindfulness.     

Building commitment to change: The role of perceived supervisor support and competence

Commitment to change is considered the glue that binds people and change goals. Still, few studies have explored how employees develop their commitment to organizational change. The present study examined the relationship between supervisor competence and support, and employee's commitment to change. Participants were 210 full-time employees from two public organizations that had recently faced major change interventions resulting from governmental directives. As predicted, perceived supervisor support fully mediated the positive relationship between competence and affective and normative commitment to change. On the other hand, supervisor competence was negatively related to continuance commitment to change. These findings illustrate how supervisors can shape employees' reactions to change.     

The impact of feedback from job and task autonomy in the relationship between dispositional resistance to change and innovative work behaviour

Building on an interactionist approach, the present study investigated the moderating role of two task design characteristics, namely task autonomy and feedback from job, in the relationship between dispositional resistance to change and innovative work behaviour. Consistent with a trait activation perspective, it was specifically hypothesized that dispositional resistance to change would have a stronger, positive association with innovative performance when autonomy and feedback were high than when they were low. In a sample of 270 employees from the public sector, task autonomy was found to significantly interact with both composite resistance to change and with three of the four dimensions (routine seeking, short-term thinking, and emotional reaction). Simple slope analyses specifically revealed that individuals high in short-term thinking and emotional reaction exhibited positive relationships with innovative behaviour only in the case of high task autonomy, whereas in the case of low autonomy the relationship was nonsignificant. Furthermore, feedback from job was found to moderate the relationship between overarching dispositional resistance to change, short-term thinking, and emotional reaction, on one hand, and innovative performance, on the other, such that a positive and significant association emerged only in the case of high feedback.     

The Challenge of Being a Challenger: Social Dominance Orientation Shapes the Impact of “Challenger vs. Leader” Comparisons

The current research investigates when and how comparative advertising effectiveness is shaped by social dominance orientation (SDO), that is, the degree to which one desires to maintain the status quo in social hierarchies. We examine these issues with regard to “challenger vs. leader” comparisons that seek to change the relative standing of competitors in a product category by documenting the superiority of challenger brands over market leaders. Findings demonstrate that the resistance to change characterizing high (vs. low) SDO individuals makes them respond (1) less favorably to “challenger vs. leader” comparisons that seek to alter the existing category hierarchy and (2) more favorably to “leader vs. challenger” comparisons that preserve this status quo in the category hierarchy. The theoretical implications of these findings are discussed, as are avenues for future research.