Effect of advanced driver-assistance system trainings on driver workload,knowledge, and trust

Older adults are more likely to get severely injured or die in vehicle crashes. Advanced driver-assistance systems (ADAS) can reduce their risk of crashes; however, due to the lack of knowledge and training, usage rate of these systems among older drivers is limited. The objective of this study was to evaluate the impact of two ADAS training approaches (i.e., video-based and demonstration-based training) on older drivers’ subjective and objective measures of mental workload, knowledge and trust considering drivers’ demographic information. Twenty older adults, balanced by gender, participated in a driving simulation study. Results indicated that the video-based training might be more effective for females in reducing their mental workload while driving, whereas the demonstration-based training could be more beneficial for males. There was no significant difference between the video-based and demonstration-based trainings in terms of drivers’ trust and knowledge of automation. The findings suggested that ADAS training protocols can potentially be more effective if they are tailored to specific driver demographics.     

On-road trust and perceived risk in Level 2 automation

To encourage appropriate use of driving automation, we need to understand and monitor driver’s trust and risk perception. We examined (1) how trust and perceived risk are affected by automation, driving conditions and experience and (2) how well perceived risk can be inferred from behaviour and physiology at three levels: over traffic conditions, aggregated risk events, and individual risk events.30 users with and without automation experience drove a Toyota Corolla with driving support. Safety attitude, subjective ratings, behaviour and physiology were examined.Driving support encouraged a positive safety attitude and active driver involvement. It reduced latent hazards while maintaining saliently perceived risks. Drivers frequently overruled lane centring (3.1 times/minute) and kept their feet on or above the pedals using ACC (65.8% of time). They comfortably used support on curvy motorways and monotonic and congested highways but less in unstable traffic and on roundabouts. They trusted the automation 65.4%, perceived 36.0% risk, acknowledged the need to monitor and would not engage in more secondary tasks than during manual driving.Trust-in situation reduced 2.0% when using automation. It was 8.2% higher than trust-in-automation, presumably due to driver self-confidence. Driving conditions or conflicts between driver and automation did not affect trust-in-automation.At the traffic condition level, physiology showed weak and partially counter-intuitive effects. For aggregated risk events, skin conductance had the clearest response but was discernible from baseline in  < 50%. Pupil dilation and heart rate only increased with strong braking and active lane departure assist. For individual risk events, a CNN classifier could not identify risk events from physiology. We conclude that GSR, heart rate and pupil dilation respond to perceived risk, but lack specificity to monitor it on individual events.     

Are novice drivers competent to take over control from level 3 automated vehicles? A comparative study with experienced drivers

With level 3 automated vehicles poised to appear on the roads soon, takeover remains a major challenge. At present, the effect of manual driving experience on takeover performance is unknown. Therefore, a simulator study was conducted to investigate the influence of driving experience (novice and experienced) on takeover performance in different takeover time budgets (7 s and 5 s) and in combination with a visual secondary task (i.e., surrogate reference task). Data from 48 young and middle-aged participants consisting of 24 novice and 24 experienced drivers were used for this study. Researchers found that the overall stability of evasive maneuvers by novice drivers was considerably worse than that by experienced drivers. A detailed analysis showed that the influence of driving experience on takeover stability was mainly reflected in longitudinal control rather than lateral control. A significant interaction between driving experience and visual secondary task showed that the latter had a substantial impact on the takeover stability of experienced drivers but not on that of novice drivers. Researchers also found that rich manual driving experience cannot make the takeover process of experienced drivers more stable than that of novice drivers under conditions of eye-off-road. In addition, no significant difference was found between novice and experienced drivers in automation disengagement time, takeover time and minimum time to collision. Results indicate that novice drivers have poor takeover stability and weak adaptability, but their longitudinal collision risk is not deteriorated by the lack of manual driving experience.     

Modeling dispositional and initial learned trust in automated vehicles with predictability and explainability

Technological advances in the automotive industry are bringing automated driving closer to road use. However, one of the most important factors affecting public acceptance of automated vehicles (AVs) is the public’s trust in AVs. Many factors can influence people’s trust, including perception of risks and benefits, feelings, and knowledge of AVs. This study aims to use these factors to predict people’s dispositional and initial learned trust in AVs using a survey study conducted with 1175 participants. For each participant, 23 features were extracted from the survey questions to capture his/her knowledge, perception, experience, behavioral assessment, and feelings about AVs. These features were then used as input to train an eXtreme Gradient Boosting (XGBoost) model to predict trust in AVs. With the help of SHapley Additive exPlanations (SHAP), we were able to interpret the trust predictions of XGBoost to further improve the explainability of the XGBoost model. Compared to traditional regression models and black-box machine learning models, our findings show that this approach was powerful in providing a high level of explainability and predictability of trust in AVs, simultaneously.     

Modelling perceived risk and trust in driving automation reacting to merging and braking vehicles

Perceived risk and trust are crucial for user acceptance of driving automation. In this study, we identify important predictors of perceived risk and trust in a driving simulator experiment and develop models through stepwise regression to predict event-based changes in perceived risk and trust. 25 participants were tasked to monitor SAE Level 2 driving automation (ACC + LC) while experiencing merging and hard braking events with varying criticality on a motorway. Perceived risk and trust were rated verbally after each event, and continuous perceived risk, pupil diameter and ECG signals were explored as possible indictors for perceived risk and trust.The regression models show that relative motion with neighbouring road users accounts for most perceived risk and trust variations, and no difference was found between hard braking with merging and hard braking without merging. Drivers trust the automation more in the second exposure to events. Our models show modest effects of personal characteristics: experienced drivers are less sensitive to risk and trust the automation more, while female participants perceive more risk than males. Perceived risk and trust highly correlate and have similar determinants. Continuous perceived risk accurately reflects participants’ verbal post-event rating of perceived risk; the use of brakes is an effective indicator of high perceived risk and low trust, and pupil diameter correlates to perceived risk in the most critical events. The events increased heart rate, but we found no correlation with event criticality. The prediction models and the findings on physiological measures shed light on the event-based dynamics of perceived risk and trust and can guide human-centred automation design to reduce perceived risk and enhance trust.     

Different level automation technology acceptance: Older adult driver opinion

The increase in the number of older adult drivers in developed countries has raised safety concerns due to the decline in their sensory, motor, perceptual, and cognitive abilities which can limit their driving capabilities. Their driving safety could be enhanced by the use of modern Automated Driver Assistance Systems (ADASs) and might totally resolved by full driving automation. However, the acceptance of these technologies by older adult drivers is not yet well understood. Thus, this study investigated older adult drivers’ intention to use six ADASs and full driving automation through two questionnaires with 115 and 132 participants respectively in Rhode Island, USA. A four-dimensional model referred to as the USEA model was used for exploring older adult drivers’ technology acceptance. The USEA model included perceived usefulness, perceived safety, perceived ease of use, and perceived anxiety. Path Analysis was applied to evaluate the proposed model. The results of this study identified the important factors in older adult drivers’ intention to use ADASs and full driving automation, which could assist stakeholders in improving technologies for use by older drivers.     

A matter of style? Driver attributional ‘style’ in accounting for the driving of others as protective or as predisposing drivers towards retaliatory aggressive driving

Driver cognitions about aggressive driving of others are potentially important to the development of evidence-based interventions. Previous research has suggested that perceptions that other drivers are intentionally aggressive may influence recipient driver anger and subsequent aggressive responses. Accordingly, recent research on aggressive driving has attempted to distinguish between intentional and unintentional motives in relation to problem driving behaviours. This study assessed driver cognitive responses to common potentially provocative hypothetical driving scenarios to explore the role of attributions in driver aggression. A convenience sample of 315 general drivers 16–64 yrs (M = 34) completed a survey measuring trait aggression (Aggression Questionnaire AQ), driving anger (Driving Anger Scale, DAS), and a proxy measure of aggressive driving behaviour (Australian Propensity for Angry Driving AusPADS). Purpose designed items asked for drivers’ ‘most likely’ thought in response to AusPADS scenarios. Response options were equivalent to causal attributions about the other driver. Patterns in endorsements of attribution responses to the scenarios suggested that drivers tended to adopt a particular perception of the driving of others regardless of the depicted circumstances: a driving attributional style. No gender or age differences were found for attributional style. Significant differences were detected between attributional styles for driving anger and endorsement of aggressive responses to driving situations. Drivers who attributed the on-road event to the other being an incompetent or dangerous driver had significantly higher driving anger scores and endorsed significantly more aggressive driving responses than those drivers who attributed other driver’s behaviour to mistakes. In contrast, drivers who gave others the ‘benefit of the doubt’ endorsed significantly less aggressive driving responses than either of these other two groups, suggesting that this style is protective.     

How the initial level of trust in automated driving impacts drivers’ behaviour and early trust construction

Trust in Automation is known to influence human-automation interaction and user behaviour. In the Automated Driving (AD) context, studies showed the impact of drivers’ Trust in Automated Driving (TiAD), and linked it with, e.g., difference in environment monitoring or driver’s behaviour. This study investigated the influence of driver’s initial level of TiAD on driver’s behaviour and early trust construction during Highly Automated Driving (HAD). Forty drivers participated in a driving simulator study. Based on a trust questionnaire, participants were divided in two groups according to their initial level of TiAD: high (Trustful) vs. low (Distrustful). Declared level of trust, gaze behaviour and Non-Driving-Related Activities (NDRA) engagement were compared between the two groups over time. Results showed that Trustful drivers engaged more in NDRA and spent less time monitoring the road compared to Distrustful drivers. However, an increase in trust was observed in both groups. These results suggest that initial level of TiAD impact drivers’ behaviour and further trust evolution.     

自动化信任和依赖对航空安全的危害及其改进

自动化系统在现今航空业中得到了广泛应用,然而复杂的自动化系统的引入产生了新的失误模式,使航空人因安全问题变得更加突出。受多种因素影响,操作者在与自动化系统的交互过程中并不总能达到适度的信任校准水平。非适度自动化信任和依赖引发了严重的航空安全事故。值得欣慰的是,以人为中心的自动化显示设计和训练能够将非适度自动化信任和依赖调整到适度状态。     

On-Road vehicle study of the experience of automated driving

Prior studies of automated driving have focused on drivers’ evaluations of advanced driving assistance systems and their knowledge of the technology. An on-road experiment with novice drivers who had never used automated systems was conducted to examine the effects of the automation on the driving experience. Participants drove a Tesla Model 3 sedan with level 2 automation engaged or not engaged on a 4-lane interstate freeway. They reported that driving was more enjoyable and less stressful during automated driving than manual driving. They also indicated that they were less anxious and nervous, and able to relax more with the automation. Their intentions to use and purchase automated systems in the future were correlated with the favorableness of their automated driving experiences. The positive experiences of the first-time users suggest that consumers may not need a great deal of persuading to develop an appreciation for partially automated vehicles.     

Semi-automated versus highly automated driving in critical situations caused by automation failures

The purpose of this study was to examine the effects of vehicle automation and automation failures on driving performance. Previous studies have revealed problems with driving performance in situations with automation failures and attributed this to drivers being out-of-the-loop. It was therefore hypothesized that driving performance is safer with lower than with higher levels of automation. Furthermore, it was hypothesized that driving performance would be affected by the extent of the automation failure. A moving base driving simulator was used. The design contained semi-automated and highly automated driving combined with complete, severe, and moderate deceleration failures. In total the study involved 36 participants. The results indicate that driving performance degrades when the level of automation increases. Furthermore, it is indicated that car drivers are worse at handling complete than partial deceleration failures.     

Measuring drivers’ takeover performance in varying levels of automation: Considering the influence of cognitive secondary task

Mixed control by driver and automated system will remain in use for decades until fully automated driving is perfected. Thus, drivers must be able to accurately regain control of vehicles in a timely manner when the automated system sends a takeover request (TOR) at its limitation. Therefore, determining the factors that affect drivers’ takeover quality at varying levels of automated driving is important. Previous studies have shown that visually distracting secondary tasks impair drivers’ takeover performance and increase the subjective workload. However, the influence of purely cognitive distracting secondary tasks on drivers’ takeover performance and how this influence varies at different levels of automation are still unknown. Hence, a 5 (driving modes) × 3 (cognitive secondary tasks) factorial design with the within-subject factors was adopted for this driving simulator experiment. The sample consisted of 21 participants. The participants’ subjective workloads were recorded by the NASA-Task Load Index (NASA-TLX). Results showed that compared to manual driving conditions, the drivers’ subjective workloads were significantly reduced in both partially and highly automated driving conditions, even with a TOR, confirming the benefit of the automated driving system in terms of reducing the driving workload. Moreover, the drivers exhibited a lower takeover behavior quality at high levels of automation than manual driving in terms of increased reaction time, abnormal performance, standard deviation of lane position, lane departure probability, and reduced minimum of time to collision. However, at the highly automated driving condition, the drivers’ longitudinal driving safety and ability to follow instructions improved when performing a highly cognitive secondary task. This phenomenon possibly occurred because automated driving conditions lead to an underload phenomenon, and the execution of highly cognitive tasks transfers drivers into moderate load, which helps with the drivers’ takeover performance.     

Learning and development of trust,acceptance and the mental model of ACC. A longitudinal on-road study

To harness the potential of advanced driver assistance systems, drivers must learn how to use them in a safe and appropriate manner. The present study investigates the learning process, as well as the development of trust, acceptance and the mental model for interacting with adaptive cruise control (ACC). Research questions aim to model the learning process in mathematical/statistical terms, examine moments and conditions when these processes stabilize, and assess how experience changes the mental model of the system. A sample of fifteen drivers without ACC experience drove a test vehicle with ACC ten consecutive times on the same route within a 2-month period. All participants were fully trained in ACC functionality by reading the owner’s manual in the beginning. Results show that learning, as well as the development of acceptance and trust in ACC follows the power law of learning. All processes stabilize at a relatively high level after the fifth session, which corresponds to 185 km or 3.5 h of driving. No decline is observable with ongoing system experience. However, limitations that are not experienced tend to disappear from the mental model if they are not activated by experience. Therefore, it is recommended that users be periodically reminded of system limitations (e.g. by intelligent tutoring systems) to make sure that corresponding knowledge nodes are activated.     

How to identify the take-over criticality in conditionally automated driving? An examination using drivers’ physiological parameters and situational factors

Autonomous vehicles and advanced driver assistance technology are growing exponentially, and vehicles equipped with conditional automation, which has features like Traffic Jam Pilot and Highway Assist, are already available in the market. And this could expose the driver to a stressful driving condition during the takeover mission. To identify stressful takeover situations and better interact with automated systems, the relationship and effect between drivers’ physiological responses, situational factors (e.g., takeover request [TOR] lead time, takeover frequencies, and scenario types), and takeover criticality were investigated.34 participants were involved in a series of takeover events in a simulated driving environment, which are varied by different TOR lead time conditions and driving scenes. The situational factors, drivers’ skin conductance (SC), heart rate (HR), gaze behaviors, and takeover criticality ratings were collected and analyzed. The results indicated that drivers had a higher takeover criticality rating when they experienced a shorter TOR lead time level or at first to fourth take-overs. Besides, drivers who encountered a dynamic obstacle reported higher takeover criticality ratings when they were at the same Time to collision (TTC). We also observed that the takeover situations of higher criticality have larger driver’s maximum HR, mean pupil size, and maximum change in the SC (relative to the initial value of a takeover stage). Those findings of situational factors and physiological responses can provide additional support for the designing of adaptive alert systems and environmental soothing technology in conditionally automated driving, which will improve the takeover performances and drivers’ experience.     

Supporting the changing driver’s task: Exploration of interface designs for supervision and intervention in automated driving

Driving automation leads to a changing role for drivers, that is from manual vehicle control to supervising automation. Supervision of partial automation requires now and then intervention. Since the automation causes low vigilance and out-of-the-loop performance problems, this changing role is not well suited for human operators. To explore how driver-vehicle interfaces can support drivers in their changed role, we tested three concepts. Concept A was a baseline reference, providing only acoustic warnings. Concept B presented status-information and warnings behind the steering wheel. Concept C used illumination and haptic feedback in the seat-pan to direct attention outside the vehicle and to stimulate response. Concept C only provided vibrotactile feedback when intervention was needed. Results of our study show improved support for supervision with the illumination-concept, i.e. better hazard-detection and raised levels of Situation Awareness in some scenarios relevant for supervisory control. Knowing that supervision will be the dominating driver’s responsibility during partially automated driving, the illumination-concept is a recommended solution for support of the driver’s changing role. Nonetheless, neither concept B, nor C, showed additional support for intervention compared to the baseline. It was hypothesised that the combination of concept C’s stimuli for intervention-support caused counter-productive levels of annoyance. Furthermore, we concluded that intervention and supervision benefit from different interface-features and discussed possible causes underlying ambiguity between support for supervision and support for intervention shown with concept C. Therewith, the considerations in this paper contribute to further development of – and knowledge about – appropriate driver-vehicle interaction while vehicle-operation advances into operating partially automated driving systems.     

Effects of Technology on Attributions of Performance and Employee Evaluation1

This study examines the effects of technological automation on explanations of why a person failed or succeeded at a task, and on evaluations of the user of technology. Subjects were presented with scenarios involving a photographer on an assignment. The scenarios manipulated 3 variables: (a) whether the camera was automatic or required skill, (b) experience level, and (c) whether the picture was a success or a failure. Subjects rated the picture's success or failure on attributions of ability and the technology. They also evaluated the photographer. Internal attribution was associated with technological devices requiring a greater amount of skill, while external attribution was associated with technological devices requiring less skill. When the picture was a success, ratings of internal attributions correlated positively with evaluations. When the picture was a failure, ratings of internal attributions correlated negatively with evaluations.     

The strength of faith and trust

While there has been considerable interest in the nature of faith and trust in recent philosophical literature, relatively little has been said about what it is for faith or trust to be psychologically stronger or weaker. Drawing on recent accounts of propositional faith by Daniel Howard-Snyder and Lara Buchak, I argue that the strength of one’s faith can vary in two distinct dimensions. The first primarily involves the extent to which one’s confidence motivates one to take risks (and secondarily involves other cognitive and emotional factors). The second involves the resilience of the first dimension of strength to possible counterevidence.     

“They have to be better than human drivers!” Motorcyclists’ and cyclists’ perceptions of autonomous vehicles

Road users and the general population by and large recognise the value of vehicles with automated driving systems and features (otherwise typically known as Autonomous Vehicles (AVs)) in terms of road safety, reduced emissions and convenience, but are still wary of their capability, preferring the ‘comfort zone’ of human operator intervention. Motorcyclists and cyclists conversely, are vulnerable to human fallibility in driving, with the majority of crashes occurring as a consequence of other drivers’ inattention. The transition period associated with the introduction of AVs will require AVs and motorcyclists/cyclists sharing the road for a number of years yet, so we need to understand motorcyclists’/cyclists’ perception of AVs. The question of interest here is whether motorcyclists/cyclists reflect the historical literature in this area by having higher levels of trust for human drivers over AVs, or whether they have higher levels of trust in AVs because it removes the ‘human element’ that has been proven to be particularly dangerous for them. Here we surveyed motorcyclists and cyclists about their trust in human drivers and AVs, and developed a novel suite of questions designed to interrogate the difference between trust in general versus trust as a concept of their own personal safety. Some of the salient outcomes suggest that motorcyclists have medium to low levels of trust for both human drivers and AVs, but are significantly more likely to believe that AVs are safer in terms of their own personal safety, such as prioritising or detecting the rider, compared to human drivers. This relationship varies with age and crash experience. The results here are consistent with the logic that motorcyclists/cyclists have a heightened sense of vulnerability on the road and welcome the introduction of AVs as a way of mitigating personal risk when riding. This insight will be crucial to the subsequent roll-out of AVs in the future.     

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.     

Disengagement from driving when using automation during a 4-week field trial

IntroductionA small body of research on the real-world use of commercially available partial driving automation suggests that drivers may struggle with or otherwise lapse in adequately monitoring the system and highway environment, and little is known about key issues such as how behavior associated with system use changes over time. The current study assessed how driver disengagement, defined as visual-manual interaction with electronics or removal of hands from the wheel, differed as drivers became more accustomed to partial automation over a 4-week trial.MethodsTen volunteers drove a Volvo S90 with adaptive cruise control (ACC), which automates speed and headway, and Pilot Assist, which combines ACC and continuous lane centering. Instrumentation captured automation use, secondary task activity, hands-on-wheel status, vehicle speed, and GPS location during all trips.ResultsThe longer drivers used the Pilot Assist partial automation system, the more likely they were to become disengaged, with a significant increase in the odds of observing participants with both hands off the steering wheel or manipulating a cell phone relative to manual control. Results associated with use of ACC found comparable or lower levels of disengagement compared to manual driving as the study progressed.DiscussionThis study highlights concerns about vehicle control and the degree to which drivers remain actively in the loop when using automation. Calls for implementing more robust driver monitoring with partial automation appear warranted—particularly those that track head or eye position.