An adaptive driver support system: user experiences and driving performance in a simulator

A simulator study assessing the effectiveness of training and warning systems on drivers' response performance to vehicle cyberattacks

Modern vehicles are vulnerable to cyberattacks and the consequences can be severe. While technological efforts have attempted to address the problem, the role of human drivers is understudied. This study aims to assess the effectiveness of training and warning systems on drivers' response behavior to vehicle cyberattacks. Thirty-two participants completed a driving simulator study to assess the effectiveness of training and warning system according to their velocity, deceleration events, and count of cautionary behaviors. Participants, who held a valid United States driving license and had a mean age of 20.4 years old, were equally assigned to one of four groups: control (n = 8), training-only (n = 8), warning-only (n = 8), training and warning groups (n = 8). For each drive, mixed ANOVAs were implemented on the velocity variables and Poisson regression was conducted on the normalized time with large deceleration events and cautionary behavior variables. Overall, the results suggest that drivers' response behaviors were moderately affected by the training programs and the warning messages. Most drivers who received training or warning messages responded safely and appropriately to cyberattacks, e.g., by slowing down, pulling over, or performing cautionary behaviors, but only in specific cyberattack events. Training programs show promise in improving drivers' responses toward vehicle cyberattacks, and warning messages show rather moderate improvement but can be further refined to yield consistent behavior.

Effects of Automation and Fatigue on Drivers from Various Age Groups

This study explores how drivers are affected by automation when driving in rested and fatigued conditions. Eighty-nine drivers (45 females, 44 males) aged between 20 and 85 years attended driving experiments on separate days, once in a rested and once in a fatigued condition, in a counterbalanced order. The results show an overall effect of automation to significantly reduce drivers’ workload and effort. The automation had different effects, depending on the drivers’ conditions. Differences between the manual and automated mode were larger for the perceived time pressure and effort in the fatigued condition as compared to the rested condition. Frustration was higher during manual driving when fatigued, but also higher during automated driving when rested. Subjective fatigue and the percentage of eye closure (PERCLOS) were higher in the automated mode compared to manual driving mode. PERCLOS differences between the automated and manual mode were higher in the fatigued condition than in the rested condition. There was a significant interaction effect of age and automation on drivers’ PERCLOS. These results are important for the development of driver-centered automation because they show different benefits for drivers of different ages, depending on their condition (fatigued or rested).

Long-Term Evaluation of Drivers' Behavioral Adaptation to an Adaptive Collision Avoidance System

OBJECTIVE

Taking human factors approach in which the human is involved as a part of the system design and evaluation process, this paper aims to improve driving performance and safety impact of driver support systems in the long view of human-automation interaction.

BACKGROUND

Adaptive automation in which the system implements the level of automation based on the situation, user capacity, and risk has proven effective in dynamic environments with wide variations of human workload over time. However, research has indicated that drivers may not efficiently deal with dynamically changing system configurations. Little effort has been made to support drivers' understanding of and behavioral adaptation to adaptive automation.

METHOD

Using a within-subjects design, 42 participants completed a four-stage driving simulation experiment during which they had to gradually interact with an adaptive collision avoidance system while exposed to hazardous lane-change scenarios over 1 month.

RESULTS

Compared to unsupported driving (stage i), although collisions have been significantly reduced when first experienced driving with the system (stage ii), improvements in drivers' trust in and understanding of the system and driving behavior have been achieved with more driver-system interaction and driver training during stages iii and iv.

CONCLUSION

While designing systems that take into account human skills and abilities can go some way to improving their effectiveness, this alone is not sufficient. To maximize safety and system usability, it is also essential to ensure appropriate users' understanding and acceptance of the system.

APPLICATION

These findings have important implications for the development of active safety systems and automated driving.

Method-oriented systematic review on the simple scale for acceptance measurement in advanced transport telematics

Acceptance intuitively is a precondition for the adaptation and use of technology. In this systematic review, we examine academic literature on the “simple scale for acceptance measurement” provided by Van der Laan, Heino, and de Waard (1997). This measure is increasingly applied in research on mobility systems without having been thoroughly analysed. This article aims to provide such a critical analysis. We identified 437 unique references in three aggregated databases and included 128 articles (N = 6,058 participants) that empirically applied the scale in this review. The typical study focused on a mobility system using a within-subjects design in a driving simulator in Europe. Based on quality indicators of transparent study aim, group allocation procedure, variable definitions, sample characteristics, (statistical) control of confounders, reproducibility, and reporting of incomplete data and test performance, many of the 128 articles exhibited room for improvements (44% below.50; range 0 to 1). Twenty-eight studies (22%) reported reliability coefficients providing evidence that the scale and its sub-scales produce reliable results (median Cronbach’s α >.83). Missing data from the majority of studies limits this conclusion. Only 2 out of 10 factor analyses replicated the proposed two-dimensional structure questioning the use of these sub-scales. Correlation results provide evidence for convergent validity of acceptance, usefulness, and satisfying with limited confidence, since only 14 studies with a median sample size of N = 40 reported correlation coefficients. With these results, the scale might be a valuable addition for technology attitude research. Firstly, we recommend thorough testing for a better understanding of acceptance, usefulness, and satisfying. Secondly, we suggest to report scale results more transparently and rigorously to enable meta-analyses in the future. The study protocol is available at the Open Science Framework (https://osf.io/j782c/).

AR DriveSim: An Immersive Driving Simulator for Augmented Reality Head-Up Display Research

Optical see-through automotive head-up displays (HUDs) are a form of augmented reality (AR) that is quickly gaining penetration into the consumer market. Despite increasing adoption, demand, and competition among manufacturers to deliver higher quality HUDs with increased fields of view, little work has been done to understand how best to design and assess AR HUD user interfaces, and how to quantify their effects on driver behavior, performance, and ultimately safety. This paper reports on a novel, low-cost, immersive driving simulator created using a myriad of custom hardware and software technologies specifically to examine basic and applied research questions related to AR HUDs usage when driving. We describe our experiences developing simulator hardware and software and detail a user study that examines driver performance, visual attention, and preferences using two AR navigation interfaces. Results suggest that conformal AR graphics may not be inherently better than other HUD interfaces. We include lessons learned from our simulator development experiences, results of the user study and conclude with limitations and future work.

Functional requirements of automotive head-up displays: A systematic review of literature from 1994 to present

Despite the long history of automotive head-up displays (HUDs), what information they should display in different situations to best serve the driver remains unanswered. The lack of understanding hinders designing useful automotive HUD systems. In an effort to address this, the current study investigated the developer, researcher and user perspectives on the functional requirements of automotive HUDs through literature review. The review results indicated that: 1) the existing commercial HUDs perform largely the same functions as the conventional in-vehicle displays, 2) past research studies proposed various HUD functions for improving driver situation awareness and driving safety, 3) autonomous driving and other new technologies are giving rise to new HUD information, and 4) little research is currently available on HUD users' perceived information needs. Based on the review results, this study provides insights into the functional requirements of automotive HUDs and also suggests some future research directions for automotive HUD design.

Driving Anger, Aberrant Driving Behaviors, and Road Crash Risk: Testing of a Mediated Model

With the dramatic increase in motorization, road traffic crashes have become the leading cause of death in China. To reduce the losses associated with road safety problems, it is important to understand the risk factors contributing to the high crash rate among Chinese drivers. This study investigated how driving anger and aberrant driving behaviors are related to crash risk by proposing and testing one mediated model. In this model, the effects of driving anger on road crash risk were mediated by aberrant driving behaviors. However, unlike previous studies, instead of using the overall scale scores, the subscales of driving anger and aberrant driving behaviors were used to establish the mediated model in this study. To test the validity of this model, an Internet-based questionnaire, which included various measures of driving anger, aberrant driving, and road crash history, was completed by a sample of 1974 Chinese drivers. The results showed that the model fitted the data very well and aberrant driving behaviors fully mediated the effects of driving anger on road crash risk. Findings from the present study are useful for the development of countermeasures to reduce road traffic crashes in China.

Advanced driver assistance systems: Using multimodal redundant warnings to enhance road safety

This study investigated whether multimodal redundant warnings presented by advanced assistance systems reduce brake response times. Warnings presented by assistance systems are designed to assist drivers by informing them that evasive driving maneuvers are needed in order to avoid a potential accident. If these warnings are poorly designed, they may distract drivers, slow their responses, and reduce road safety. In two experiments, participants drove a simulated vehicle equipped with a forward collision avoidance system. Auditory, vibrotactile, and multimodal warnings were presented when the time to collision was shorter than five seconds. The effects of these warnings were investigated with participants performing a concurrent cell phone conversation (Exp. 1) or driving in high-density traffic (Exp. 2). Braking times and subjective workload were measured. Multimodal redundant warnings elicited faster braking reaction times. These warnings were found to be effective even when talking on a cell phone (Exp. 1) or driving in dense traffic (Exp. 2). Multimodal warnings produced higher ratings of urgency, but ratings of frustration did not increase compared to other warnings. Findings obtained in these two experiments are important given that faster braking responses may reduce the potential for a collision.

The impact of immediate or delayed feedback on driving behaviour in a simulated Pay-As-You-Drive system

Pay-As-You-Drive (PAYD) insurance links an individual's driving behaviour to the insurance fee that they pay, making car insurance more actuarially accurate. The best known PAYD insurance format is purely mileage based and is estimated to reduce accidents by about 15% (Litman, 2011). However, these benefits could be further enhanced by incorporating a wider range of driving behaviours, such as lateral and longitudinal accelerations and speeding behaviour, thereby stimulating not only a safe but also an eco-friendly driving style. Currently, feedback on rewards and driver behaviour is mostly provided through a web-based interface, which is presented temporally separated from driving. However, providing immediate feedback within the vehicle itself could elicit more effect. To investigate this hypothesis, two groups of 20 participants drove with a behavioural based PAYD system in a driving simulator and were provided with either delayed feedback through a website, or immediate feedback through an in-car interface, allowing them to earn up to €6 extra. To be clear, every participant in the web group did actually view their feedback during the one week between sessions. Results indicate clear driving behaviour improvements for both PAYD groups as compared to baseline rides and an equal sized control group. After both PAYD groups had received feedback, the initial advantage of the in-car group was reduced substantially. Taken together with usability ratings and driving behaviours in specific situations these results show a moderate advantage of using immediate in-car feedback. However, the study also showed that under conditions of feedback certainty, the effectiveness of delayed feedback approaches that of immediate feedback as compared to a naïve control group.

Preface to the special section on human factors and automation in vehicles: designing highly automated vehicles with the driver in mind

OBJECTIVE

This special section brings together diverse research regarding driver interaction with advanced automotive technology to guide design of increasingly automated vehicles.

BACKGROUND

Rapidly evolving vehicle automation will likely change cars and trucks more in the next 5 years than the preceding 50, radically redefining what it means to drive.

METHOD

This special section includes 10 articles from European and North American researchers reporting simulator and naturalistic driving studies.

RESULTS

Little research has considered the consequences of fully automated driving, with most focusing on lane-keeping and speed control systems individually. The studies reveal two underlying design philosophies: automate driving versus support driving. Results of several studies, consistent with previous research in other domains, suggest that the automate philosophy can delay driver responses to incidents in which the driver has to intervene and take control from the automation. Understanding how to orchestrate the transfer or sharing of control between the system and the driver, particularly in critical incidents, emerges as a central challenge.

CONCLUSION

Designers should not assume that automation can substitute seamlessly for a human driver, nor can they assume that the driver can safely accommodate the limitations of automation. Designers, policy makers, and researchers must give careful consideration to what role the person should have in highly automated vehicles and how to support the driver if the driver is to be responsible for vehicle control. As in other domains, driving safety increasingly depends on the combined performance of the human and automation, and successful designs will depend on recognizing and supporting the new roles of the driver.