Driver trust in and training for advanced driver assistance systems in Real-World driving

Two-hundred and twenty-three participants completed an online survey regarding their experiences with advanced driver assistance systems (ADAS) on their personal vehicles, with focus on 1) drivers’ trust in 13 ADAS technologies, and 2) perceived effectiveness of currently used methods of training. Eighteen drivers participated in focus groups designed to probe more deeply into survey responses. Results of the survey showed that participant ratings of trust increased significantly with longer vehicle ownership, but participants who experienced unexpected ADAS technology behavior rated their trust over time significantly lower on ADAS technologies with the exception of rear collision avoidance. The majority (75.8%) of participants reported receiving some ADAS instruction at their vehicle dealership, but only 16.6% indicated it was formal. Participants who received formalized training reported it to be significantly more effective than those who received informal overviews of their systems. Use of trial and error and the owner’s manual were the most frequently reported methods of learning outside of dealership training. Responses indicated that the lack of content tailored to trim-specific vehicle features in owner’s manuals was a barrier to effective use.     

Eye movements predict driver reaction time to takeover request in automated driving: A real-vehicle study

For automated driving at SAE level 3 or lower, driver performance in responding to takeover requests (TORs) is decisive in providing system safety. A driver state monitoring system that can predict a driver’s performance in a TOR event will facilitate a safer control transition from vehicle to driver. This experimental study investigated whether driver eye-movement measured before a TOR can predict driving performance in a subsequent TOR event. We recruited participants (N = 36) to obtain realistic results in a real-vehicle study. In the experiment, drivers rode in an automated vehicle on a test track for about 32 min, and a critical TOR event occurred at the end of the drive. Eye movements were measured by a camera-based driver monitoring system, and five measures were extracted from the last 2-min epoch prior to the TOR event. The correlations between each eye-movement measure and driver reaction time were examined, and a multiple regression model was built using a stepwise procedure. The results showed that longer reaction time could be significantly predicted by a smaller number of large saccades, a greater number of medium saccades, and lower saccadic velocity. The implications of these relationships are consistent with previous studies. The present real-vehicle study can provide insights to the automotive industry in the search for a safer and more flexible interface between the automated vehicle and the driver.     

A driving simulator study to assess driver performance during a car-following maneuver after switching from automated control to manual control

A review of the literature on autonomous vehicles has shown that they offer several benefits, such as reducing traffic congestion and emissions, and improving transport accessibility. Until the highest level of automation is achieved, humans will remain an important integral of the driving cycle, which necessitates to fully understand their role in automated driving. A difficult research topic involves an understanding of whether a period of automated driving is likely to reduce driver fatigue rather than increase the risk of distraction, particularly when drivers are involved in a secondary task while driving. The main aim of this research comprises assessing the effects of an automation period on drivers, in terms of driving performance and safety implications. A specific focus is set on the car-following maneuver. A driving simulator experiment has been designed for this purpose. In particular, each participant was requested to submit to a virtual scenario twice, with level-three driving automation: one drive consisting of Full Manual Control Mode (FM); the other comprising an Automated Control Mode (AM) activated in the midst of the scenario. During the automation mode, the drivers were asked to watch a movie on a tablet inside the vehicle. When the drivers had to take control of the vehicle, two car-following maneuvers were planned, by simulating a slow-moving vehicle in the right lane in the meanwhile a platoon of vehicles in the overtaking lane discouraged the passing maneuver. Various driving performances (speeds, accelerations, etc.) and surrogate safety measures (PET and TTC) were collected and analysed, focusing on car-following maneuvers. The overall results indicated a more dangerous behavior of drivers who were previously subjected to driving automation; the percentage of drivers who did not apply the brakes and headed into the overtaking lane despite the presence of a platoon of fast-moving vehicles with unsafe gaps between them was higher in AM drive than in FM drive. Conversely, for drivers who preferred to brake, it was noted that those who had already experienced automated driving, adopted a more careful behavior during the braking maneuver to avoid a collision. Finally, with regard to drivers who had decided to overtake the braking vehicle, it should be noted that drivers who had already experienced automated driving did not change their behavior whilst overtaking the stopped lead vehicle.     

Public acceptance of driver state monitoring for automated vehicles: Applying the UTAUT framework

Driver state monitoring (DSM) systems aim to measure driver/occupant state, considering factors such as fatigue, workload, attentiveness, and wellbeing. They are influential for some vehicles on the road today, but as we move towards higher levels of automation their use is expected to become even more important. Uncertainty around public perception of these systems is a potentially limiting factor, with potential concerns for privacy, data storage, perceived utility, and reliability amongst other things posing potential threats. A survey was conducted based on the Universal Theory of Acceptance and Use of Technology (UTAUT) framework, which collected 311 individual responses to explore the acceptability of this technology in specific relation to conditionally and partially automated vehicles. This paper identified that the factors of effort expectancy, performance expectancy, social influence, and attitudes towards using new technology are all positively related to overall behavioural intention, whereas anxiety is negatively related. Other demographic influences are discussed, and recommendations made for future research and marketing/implementation of DSM systems to ensure acceptance by future users.     

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.     

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 potential of gamification for user education in partial and conditional driving automation: A driving simulator study

Drivers must establish adequate mental models to ensure safe driver-vehicle interaction in combined partial and conditional driving automation. To achieve this, user education is considered crucial. Since gamification has previously shown positive effects on learning motivation and performance, it could serve as a measure to enhance user education on automated vehicles. We developed a tablet-based instruction involving gamified elements and compared it to instruction without gamification and a control group receiving a user manual. After instruction, participants (N = 57) experienced a 30-minute automated drive on a motorway in a fixed-base driving simulator. Participants who received the gamified instruction reported a higher level of intrinsic motivation to learn the provided content. The results also indicate that gamification promotes mental model formation and trust during the automated drive. Taken together, including gamification in user education for automated driving is a promising approach to enhance safe driver-vehicle interaction.     

Developing human-machine trust: Impacts of prior instruction and automation failure on driver trust in partially automated vehicles

To prompt the use of driving automation in an appropriate and safe manner, system designers require knowledge about the dynamics of driver trust. To enhance this knowledge, this study manipulated prior information of a partial driving automation into two types (detailed and less) and investigated the effects of the information on the development of trust with respect to three trust attributions proposed by Muir (1994): predictability, dependability, and faith. Furthermore, a driving simulator generated two types of automation failures (limitation and malfunction), and at six instances during the study, 56 drivers completed questionnaires about their levels of trust in the automation. Statistical analysis found that trust ratings of automation steadily increased with the experience of simulation regardless of the drivers’ levels of knowledge. Automation failure led to a temporary decrease in trust ratings; however, the trust was rebuilt by a subsequent experience of flawless automation. Results showed that dependability was the most dominant belief of drivers’ trust throughout the whole experiment, regardless of their knowledge level. Interestingly, detailed analysis indicated that trust can be accounted by different attributions depending on the drivers’ circumstances: the subsequent experience of error-free automation after the exposure to automation failure led predictability to be a secondary predictive attribution of drivers’ trust in the detailed group whilst faith was consistently the secondary contributor to shaping trust in the less group throughout the experiment. These findings have implications for system design regarding transparency and for training methods and instruction aimed at improving driving safety in traffic environments with automated vehicles.     

Human factors of transitions in automated driving: A general framework and literature survey

The topic of transitions in automated driving is becoming important now that cars are automated to ever greater extents. This paper proposes a theoretical framework to support and align human factors research on transitions in automated driving. Driving states are defined based on the allocation of primary driving tasks (i.e., lateral control, longitudinal control, and monitoring) between the driver and the automation. A transition in automated driving is defined as the process during which the human-automation system changes from one driving state to another, with transitions of monitoring activity and transitions of control being among the possibilities. Based on ‘Is the transition required?’, ‘Who initiates the transition?’, and ‘Who is in control after the transition?’, we define six types of control transitions between the driver and automation: (1) Optional Driver-Initiated Driver-in-Control, (2) Mandatory Driver-Initiated Driver-in-Control, (3) Optional Driver-Initiated Automation-in-Control, (4) Mandatory Driver-Initiated Automation-in-Control, (5) Automation-Initiated Driver-in-Control, and (6) Automation-Initiated Automation-in-Control. Use cases per transition type are introduced. Finally, we interpret previous experimental studies on transitions using our framework and identify areas for future research. We conclude that our framework of driving states and transitions is an important complement to the levels of automation proposed by transportation agencies, because it describes what the driver and automation are doing, rather than should be doing, at a moment of time.     

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.     

The electric vehicle: A new driving experience involving specific skills and rules

The arrival of the electric vehicle (EV) on the market is one consequence of government measures to improve air quality and reduce CO² emissions. However, the EV has specific properties of use associated with its limited range and relative silence compared to normal vehicles, influencing the mobility behaviours of drivers and requiring them to develop some new driving abilities. This paper examines the behavioural modifications brought about by daily use of an electric vehicle at three different levels of driving activity: strategic, tactical and operational. The study collected and analyzed the self-reported behaviours (via questionnaires and travels dairies) of 36 Parisian private drivers, each of whom drove for six months an electric MINI E prototype. The results of the study show that driving an EV requires a learning phase to acquire the skills and knowledge necessary to operate the vehicle. At the strategic level of driving, drivers take into account the restricted range of the EV, implement a daily charge process, and develop new behaviours related to trip planning. The study also examines driver behaviour at the tactical level, in terms of driver interactions with other road users to deal with the silent nature of the EV, and at the operational level of driving, in terms of braking behaviour to master the regenerative braking function of the EV. The paper discusses the interactions between these three levels of driving activity.     

The determinants of Portuguese preference for vehicle automation: A descriptive and explanatory study

During the last century, innovation of automated vehicles (AVs) technologies are successively maturing while progressively excluding the human intervention in vehicle driving. The objective of this paper was to analyze the determinants of Portuguese drivers’ decision to adopt AVs technologies, in an under explored context, where the driver of contemporary vehicles does all or part of the dynamic driving task (DDT) in comparison to vehicles equipped with Automated Driving systems (ADS) where the driver can become a passenger temporarily or permanently. In addition, willingness-to-pay for ADS estimates were also investigated. This study data was collected through a survey designed and deployed in Portugal. A mixed logit model was estimated, and the results obtained are in line with the literature of AVs in a number of determinants, but also highlights differences that can be explained by the Portuguese cultural, social and economic context. Overall, 83.7% of the Portuguese drivers favor contemporary vehicles, today, and among those who prefer vehicles with ADS, highly educated drivers’ are willing to pay, on average, 65,671 € for Conditional AVs, 31,185 € for Highly AVs, and about 28,622 € for Full AVs.     

Mind-over-body beliefs in sport and exercise: A driving force for training volume and performance,but with risks for exercise addiction

We assume that athletic success is associated with certain beliefs that on the one hand promote performance-enhancing behavior (training volume), but on the other hand can also be detrimental to health (sports addiction). These beliefs are succinctly characterized by the title of the 9-item “Mind-over-Body” scale presented here. They are the three beliefs that 1) athletic performance requires a high level of effort, 2) that willpower plays an important role in athletic success, and 3) that athletic success requires pain tolerance. A total of six web-survey-based studies with a total of 1121 participants (approximately gender parity), including individuals with different levels of athletic performance (no competition; amateur sport; regional, national, or international competition), examined the psychometric network and construct and criterion validity of the MoB scale. Exploratory graph analyses, which included the studies with the largest sample sizes, showed that the three belief components (effort, willpower, pain) form separable communities within the MoB network and that the MoB items form communities distinct from self-control and self-efficacy. Meta-analyzed correlations across all six studies showed low positive correlations with self-control and self-efficacy. In terms of criterion validity, MoB beliefs were positively correlated with training volume and exercise addiction. We discuss MoBs as “on the edge of unhealthy” and place MOBs within a framework of related but distinct concepts.     

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.     

A structural equation modeling approach for the acceptance of driverless automated shuttles based on constructs from the Unified Theory of Acceptance and Use of Technology and the Diffusion of Innovation Theory

The present study investigated the attitudes and acceptance of automated shuttles in public transport among 340 individuals physically experiencing the automated shuttle ‘Emily’ from Easymile in a mixed traffic environment on the semi-public EUREF (Europäisches Energieforum) campus in Berlin. Automated vehicle acceptance was modelled as a function of the Unified Theory of Acceptance and Use of Technology (UTAUT) constructs performance expectancy, effort expectancy, social influence, and facilitating conditions, the Diffusion of Innovation Theory (DIT) constructs compatibility and trialability, as well as trust and automated shuttle sharing. The results show that after adding the DIT constructs, automated shuttle sharing, and trust to the model, the effect of performance expectancy on behavioural intention was no longer significant. Instead, compatibility with current travel was the strongest predictor of behavioural intention to use automated shuttles. It was further found that individuals who are willing to share rides in an automated shuttle with fellow travelers (i.e., automated shuttle sharing) and who trust automated shuttles (i.e., trust) are more likely to intend to use automated shuttles (i.e., behavioural intention). The highest mean rating was obtained for believing that automated shuttles are easy to use, while the lowest mean rating was obtained for feeling safe inside the automated shuttle without any type of supervision. The analysis revealed a preference for the supervision of the automated shuttle via an external control room to the supervision by a human steward onboard. We recommend future research to investigate the hypothesis that compatibility could serve as an even stronger predictor of the behavioural intention to use automated shuttles in public transport than performance expectancy.     

Development of the Questionnaire on the Acceptance of Automated Driving (QAAD): Data-driven models for Level 3 and Level 5 automated driving

Automated driving comes with many promises like zero traffic casualties that are, however, only realizable given their technological development and public acceptance for wide-spread deployment. To investigate the potential acceptance, we developed a new data-driven questionnaire focusing on drivers and barriers of the anticipated possible (non-)adoption of automated driving (AD). Therefore, we conducted a cross-sectional questionnaire study with 725 respondents (351 female, 374 male) ranging from 18 to 96 years. We applied exploratory and confirmatory factor analyses and structural equation modeling, to pursue the overarching goal to develop the QAAD questionnaire (short and long version for SAE Level 3 (L3) and 5 (L5) AD). Hence, we identified the three latent factors PRO (positive aspects), CON (negative aspects), and NDRTs (non-driving related tasks) of L3 (short: 9 items; long: 16) and L5 (short: 11, long: 17), respectively. Additionally, we queried general questions on AD (8 items) and extracted the two factors Early Adoption/Pro AD and Sustainability. Our findings and the goodness-of-fit indices suggest data-driven models for L3 and L5 automated driving and on general aspects focusing on early adoption and sustainability in the context of AD. They can be applied in future research settings, in particular, in (quasi-)experimental L3 and L5 AD studies and in population surveys on AD. The evidence of the presented study should be validated and compared to other questionnaires on AD in different countries around the globe.     

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.     

Inference in the Wild: A Framework for Human Situation Assessment and a Case Study of Air Combat

Situation awareness is a key construct in human factors and arises from a process of situation assessment (SA). SA comprises the perception of information, its integration with existing knowledge, the search for new information, and the prediction of the future state of the world, including the consequences of planned actions. Causal models implemented as Bayesian networks (BNs) are attractive for modeling all of these processes within a single, unified framework. We elicited declarative knowledge from two Royal Australian Air Force (RAAF) fighter pilots about the information sources used in the identification (ID) of airborne entities and the causal relationships between these sources. This knowledge was represented in a BN (the declarative model) that was evaluated against the performance of 19 RAAF fighter pilots in a low‐fidelity simulation. Pilot behavior was well predicted by a simple associative model (the behavioral model) with only three attributes of ID. Search for information by pilots was largely compensatory and was near‐optimal with respect to the behavioral model. The average revision of beliefs in response to evidence was close to Bayesian, but there was substantial variability. Together, these results demonstrate the value of BNs for modeling human SA.     

The relations between specific measures of simulated driving ability and functional ability: New insights for assessment and training programs of older drivers

To gain new insights for driving assessment and training, this study had two objectives: (1) to investigate the relations between specific measures of older drivers’ driving ability and demographic/functional ability measures, and (2) to verify the explained variance of these relations to determine the strength of these relations. A sample of 55 older drivers (mean age 76 years) completed a set of functional ability tests as well as a driving simulator test. Results indicate that (1) each specific driving measure is related to a specific set of functional abilities, and (2) only a small proportion of the variability observed in the specific driving measures is explained by demographic variables (3–15%) and by functional abilities (7–36%). For driving assessment programs, it will be necessary to assess several functional abilities to cover the complexity of the driving task. Furthermore, an assessment program focusing solely on demographic and/or functional ability measures, will not be successful in discriminating safe from unsafe older drivers. For driving training programs, it will be necessary to focus on the right set of functional abilities given that specific driving measures are related to different functional abilities. Moreover, a training targeting functional abilities might only have marginal effects on driving ability, given the relatively low amount of driving ability variance that is explained by functional abilities.     

A cross-cultural analysis of aggressive driving: Evidence from Serbia and Romania

Using data from more than 700 drivers from Serbia and Romania, this study verified the dimensionality of aggressive driving in two countries from Eastern Europe. Specifically, the psychometric properties and invariance of the Romanian Driving Anger Expression Inventory (DAX; Deffenbacher et al., 2002, Sârbescu, 2012) were verified. Secondary aspects, such as differences in aggressive driving between countries or gender differences within countries, were also investigated. Our findings support the appropriateness of the three-factor structure in both countries, through the configural invariance of the DAX. Also, males report slightly higher levels of aggressive driving than women (small effect sizes), while Serbian drivers report higher levels of aggressive driving than Romanian drivers (strong effect sizes). Being the first research that verified the invariance of the DAX across two cultures, this study opens new paths and questions for research concerning aggressive driving.