Distraction Mitigation Research Articles

Neuro-Adaptive AI for Dynamic Distraction Mitigation in Autonomous Vehicle Environments

As autonomous vehicles develop, driver distraction becomes even more crucial as it affects both safety and operational efficiency. In this work, we investigate the gamut of new AI tools for combating and processing visual distraction scenarios within autonomous vehicles. This includes AI-based driver monitoring systems to determine the level of attention, visual distraction classification with deep learning models, augmented reality head-up displays for focal projection of critical information and gesture/voice-controlled interfaces are used in order to reduce visual interactions. This also includes how predictive analytics; adaptive user interfaces and personalized distraction mitigation programs will see AI improve driver focus and thus safety. These advanced systems are designed to provide a safer and more efficient driving experience in the emerging era of autonomous capabilities by leveraging the scalability of advanced driver-assistance technologies.

Effects of context-sensitive distraction warnings on drivers’ smartphone use and acceptance: A long-term naturalistic field study

Driver distraction by smartphone use is a major contributor in traffic accidents. Context-sensitive driver (in)attention monitoring and warning systems might mitigate the associated risks. However, few naturalistic studies are yet available on the effects of such systems. In this paper, utility of context-sensitivity in inattention monitoring was studied by a smartphone-based context-sensitive distraction warning system. The warnings were based on driver's phone use and the attentional demands of the upcoming traffic environment. The system's effects on 26 heavy smartphone users’ phone usage and acceptance were analyzed after a within-subject naturalistic study with 12 weeks of control (warnings off) and 12 weeks of interventions (warnings on). The system decreased odds that the drivers would touch their smartphones in reminder areas that were defined a priori as high demanding for attention. Against expectations, the system had no effect in urban road environments. The drivers reported that they had paid more attention to traffic because of the system and that the warnings were acceptable and useful, even if annoying. Similar systems’ safety effects should be further studied. No eye-tracking or driving performance measures were collected and thereby it is questionable if there was a true positive effect on participants’ attention. However, the findings suggest that (in)attention warning systems might benefit from adaptation of the warnings to the upcoming driving demands. The findings can be utilized for the development of proactive and context-sensitive (in)attention monitoring and distraction mitigation systems.

Understanding the Distraction and Distraction Mitigation Factors and Their Relationship with the Procrastination of Master’s and Doctoral Students in Administration

The lack of studies on academic procrastination caused by distractions in the context of social isolation, during the COVID-19 pandemic, motivated the study that sought to answer: How do master’s and doctoral students perceive distraction and distraction mitigation factors in about your procrastinating behavior? Aiming to understand the distraction and attention mitigation factors that influence procrastinating behavior in the postgraduate academic context. This is qualitative research of phenomenological nature. The study participants are twenty-four students, twelve master’s students representing about 23% of the universe, and twelve doctoral students, corresponding to approximately 21% of the universe. Based on a literature review, a theoretical framework developed that allowed comparison with the earlier analysis categories and the data collected in the semi-structured interviews. The topics Academic Distraction Factors (ADF) and Academic Distraction Mitigation (ADM) generated 583 citations, finding sixty-one codes or subcodes. The lack of planning and work outside presented as complicating factors that lead to academic procrastination. Suitable time management and the use of tools to help manage learning are good allies in mitigating distractions.

Young Novice Drivers' Cognitive Distraction Detection: Comparing Support Vector Machines and Random Forest Model of Vehicle Control Behavior.

The use of mobile phones has become one of the major threats to road safety, especially in young novice drivers. To avoid crashes induced by distraction, adaptive distraction mitigation systems have been developed that can determine how to detect a driver's distraction state. A driving simulator experiment was conducted in this paper to better explore the relationship between drivers' cognitive distractions and traffic safety, and to better analyze the mechanism of distracting effects on young drivers during the driving process. A total of 36 participants were recruited and asked to complete an n-back memory task while following the lead vehicle. Drivers' vehicle control behavior was collected, and an ANOVA was conducted on both lateral driving performance and longitudinal driving performance. Indicators from three aspects, i.e., lateral indicators only, longitudinal indicators only, and combined lateral and longitudinal indicators, were inputted into both SVM and random forest models, respectively. Results demonstrated that the SVM model with parameter optimization outperformed the random forest model in all aspects, among which the genetic algorithm had the best parameter optimization effect. For both lateral and longitudinal indicators, the identification effect of lateral indicators was better than that of longitudinal indicators, probably because drivers are more inclined to control the vehicle in lateral operation when they were cognitively distracted. Overall, the comprehensive model built in this paper can effectively identify the distracted state of drivers and provide theoretical support for control strategies of driving distraction.

Real-Time Driving Distraction Recognition Through a Wrist-Mounted Accelerometer.

We propose a method for recognizing driver distraction in real time using a wrist-worn inertial measurement unit (IMU). Distracted driving results in thousands of fatal vehicle accidents every year. Recognizing distraction using body-worn sensors may help mitigate driver distraction and consequently improve road safety. Twenty participants performed common behaviors associated with distracted driving while operating a driving simulator. Acceleration data collected from an IMU secured to each driver's right wrist were used to detect potential manual distractions based on 2-s long streaming data. Three deep neural network-based classifiers were compared for their ability to recognize the type of distractive behavior using F1-scores, a measure of accuracy considering both recall and precision. The results indicated that a convolutional long short-term memory (ConvLSTM) deep neural network outperformed a convolutional neural network (CNN) and recursive neural network with long short-term memory (LSTM) for recognizing distracted driving behaviors. The within-participant F1-scores for the ConvLSTM, CNN, and LSTM were 0.87, 0.82, and 0.82, respectively. The between-participant F1-scores for the ConvLSTM, CNN, and LSTM were 0.87, 0.76, and 0.85, respectively. The results of this pilot study indicate that the proposed driving distraction mitigation system that uses a wrist-worn IMU and ConvLSTM deep neural network classifier may have potential for improving transportation safety.

Distractions in the Operating Room

The goal of this review is to investigate current research in the area of operating room (OR) distractions, in particular, from the perspective of the anesthesia provider. Additionally, the concept of the “sterile cockpit” is discussed as well as the science of attention span in humans. Etiology of distractions may be classified as patient care related, due to technology, noise, and alarms, from interpersonal dynamics or personally driven. Studies show that at least 5% of human factor errors in the OR are due to distractions. Lack of attention has been shown to contribute disproportionately to poor outcomes due to inadequate respiratory monitoring. OR distractions continue to be problematic for the anesthesia providers and patients alike. Implementation of aviation-style distraction mitigation strategies has been proposed though there is limited research to support their efficacy. Additional research in this field is necessary to determine the etiology of patient harm and to guide development of procedures and protocols to reduce OR distractions.

A Taxonomy of Strategies For Supporting Time-Sharing With Non-Driving Tasks in Automated Driving

Driver distraction is one of the leading causes of vehicle crashes. The introduction of higher levels of vehicle control automation is expected to alleviate the negative effects of distraction by delegating the driving task to automation, thus enabling drivers to engage in non-driving-related tasks more safely. However, before fully automated vehicles are realized, drivers are still expected to play a supervisory role and intervene with the driving task if necessary while potentially having more spare capacity for engaging in non-driving-related tasks. Traditional distraction mitigation perspectives need to be shifted for automated vehicles from mainly preventing the occurrence of non-driving-related tasks to dynamically coordinating time-sharing between driving and non-driving-related tasks. In this paper, we provide a revised and expanded taxonomy of driver distraction mitigation strategies, discuss how the different strategies can be used in an automated driving context, and propose directions for future research in supporting time-sharing in automated vehicles.

A Taxonomy of Distraction Mitigation Strategies for Operating Rooms

Operating rooms (ORs) are complex dynamic settings that require individual OR team members to conduct focused and cognitively demanding tasks. The team focus may be disrupted by distractions such as alarms, phone calls, or in-person communications irrelevant to work. Some of these distractions can result in a break in task performance which are known as interruptions (Healey, Primus, & Koutantji, 2007). Distractions are inherent in the ORs and are generally considered as harmful (Mentis, Chellali, Manser, Cao, & Schwaitzberg, 2016). Studies have shown that distractions are associated with disrupted surgical flow (Wiegmann, Elbardissi, Dearani, Daly, & Sundt III, 2007), decreased cognitive task performance (Hsu, Man, Gizicki, Feldman, & Fried, 2008), increased time taken to complete surgical tasks, impaired dexterity, and increased surgical error rates (Ahmed, Ahmad, Stewart, Francis, & Bhatti, 2015). Individuals can experience prospective memory failures after an interruption, i.e., forgetting to resume the interrupted task or resuming the task at an incorrect step (Reason & Hobbs, 2003). Negative effects of distractions have been the central concern of these studies; however, literature is increasingly agreeing on the potential benefits of distractions (e.g., communication) such as decreasing stress, boredom, and mental fatigue (Weigl, Antoniadis, Chiapponi, Bruns, & Sevdalis, 2015) and improving surgical task performance (Kyrillos & Caissie, 2017). It is important to foster positive distractions, while mitigating the negative ones. Research is limited when explaining the conditions in which OR distractions are beneficial or harmful and the consequences of removing or reducing these distractions. Despite this lack of evidence, several techniques, mostly adapted from other high risk industries such as aviation, have been suggested for mitigating OR distractions (see Feil, 2014 for a comprehansive review). We created a taxonomy to categorize OR distraction mitigation strategies that we identified through literature reviews and discussions with OR teams from our partner hospital. Systems approach in human error management expects the system to prevent errors from occurring or propagating by having several defense barriers (Reason, 2008). Hence, our taxonomy sorts mitigation strategies that act as defense barriers using three dimensions: technology type (hard, soft), management control type (feedforward, concurrent, feedback), and target (individual, team, hospital). Hard technology refers to strategies that have tangible forms (e.g., physical tools such as checklists) while soft technology refers to the ones that have intangible forms (e.g., training and policies). Feedforward control aims to mitigate potential distractions by putting in system defenses based on anticipation of OR distractions that may happen. Concurrent control introduces defenses that are active during surgery. Feedback control compares surgical events and performance with desired outcomes and provides feedback and corrective control. Finally, distraction mitigation strategies should not only target individuals but also the OR team and the hospital at large, therefore, the last dimension captures this perspective. Thorough examinations are needed before implementing different mitigation strategies since these strategies, both hard and soft, can become sources of distraction themselves if designed, implemented, or maintained poorly. A safety culture needs to be adopted and supported, which can be achieved through the inclusion of feedforward, concurrent, and feedback control mechanisms for managing OR distraction. Success of distraction mitigation efforts requires active participation from individuals, OR teams, and hospital management. This systematic approach in categorizing mitigation strategies is aimed to assist OR distraction management and to improve patient safety together with work conditions for OR team members. However, further research is needed to assess the effectiveness of different strategies in practice. Acknowledgments. Funding for this work was provided by NSERC and the Centre for Healthcare Engineering of the University of Toronto, Faculty of Applied Science and Engineering.

Classification of Driver Distraction: A Comprehensive Analysis of Feature Generation, Machine Learning, and Input Measures.

The objective of this study was to analyze a set of driver performance and physiological data using advanced machine learning approaches, including feature generation, to determine the best-performing algorithms for detecting driver distraction and predicting the source of distraction. Distracted driving is a causal factor in many vehicle crashes, often resulting in injuries and deaths. As mobile devices and in-vehicle information systems become more prevalent, the ability to detect and mitigate driver distraction becomes more important. This study trained 21 algorithms to identify when drivers were distracted by secondary cognitive and texting tasks. The algorithms included physiological and driving behavioral input processed with a comprehensive feature generation package, Time Series Feature Extraction based on Scalable Hypothesis tests. Results showed that a Random Forest algorithm, trained using only driving behavior measures and excluding driver physiological data, was the highest-performing algorithm for accurately classifying driver distraction. The most important input measures identified were lane offset, speed, and steering, whereas the most important feature types were standard deviation, quantiles, and nonlinear transforms. This work suggests that distraction detection algorithms may be improved by considering ensemble machine learning algorithms that are trained with driving behavior measures and nonstandard features. In addition, the study presents several new indicators of distraction derived from speed and steering measures. Future development of distraction mitigation systems should focus on driver behavior-based algorithms that use complex feature generation techniques.

Video-Based Driver’s Hand Tracking using Fast Normalized Cross Coefficient with Improved Computational Efficiency

Driver distraction is one of the major causes for fatal car accidents. In a distracting activity, manual distraction is a triggered response of other types of distraction, such as cognitive and visual distraction. Therefore, recognition of manual distraction can contribute to the monitoring of overall drivers’ distraction. In this study, a computer vision-based method to track hand movement from the recorded driving behavior is proposed. This method integrates a low computational cost template matching algorithm using fast normalized cross coefficient (NCC) and a novel searching strategy. The proposed method was evaluated by the VIVA hand tracking data set. It achieves 50.83% of marginal accuracy percentage (mAP), 42.18% of multiple object tracking accuracy (MOTA), 31.56% of mostly tracked (MT), and 19.29% of mostly lost (ML), and it outperformed a state of the art algorithm in MOTA and MT. Additionally, the computational cost of the proposed method is greatly improved, and it can run at around 11.1 frames per second. The outcome of this research will further assist driving distraction recognition and mitigation, and improve driving safety.

Effectiveness and user acceptance of infotainment-lockouts: A driving simulator study

Lockout is a system-initiated distraction mitigation strategy that renders certain features of in-vehicle information systems (IVISs) non operable while the vehicle is in motion. The aim of this driving simulator study was to examine the influence of lockouts on driving performance and user acceptance. Overall, 52 participants performed six tasks with fully unlocked, partially locked, and completely locked IVIS. Within a repeated-measures design, we assessed user acceptance. As participants were free to decide where to conduct a secondary task, we could only analyse driving performance of 26 drivers. After each driving section, the participants rated the respective system with respect to acceptance. Driving performance with regard to lateral control was better when the system employed partial or complete lockouts as compared to the unlocked system. In contrast, longitudinal control did not benefit from a lockout. User acceptance decreased with an increasing number of disabled system functions while driving. Thus, lockout as a distraction mitigation strategy comes at the price of reduced user acceptance. To improve acceptance, one could attempt to make the secondary tasks less attractive (e.g., by public campaigns) rather than prohibit them through lockout. In addition, human-machine interfaces in the vehicle could be improved in order to reduce their demands on attentional resources.

Mindfulness Predicts Driver Engagement in Distracting Activities

Distracted driving is a major public health concern because of its potential costs of injury, mortality, and property damage. Understanding the psychological factors that underlie drivers’ willingness to engage in distracting activities despite the clear safety risks can help to identify interventions to mitigate this dangerous behavior. This study examined if mindfulness, defined as one’s attention to and awareness of oneself and the present situation, predicts driver engagement in a wide range of distracting activities, including in-vehicle technology and non-technology-based distraction sources, daydreaming/mind wandering, and distractions external to the vehicle. A total of 312 drivers completed an online survey assessing levels of mindfulness and the frequency with which they engaged in a range of potentially distracting activities. The results showed that while engagement in distracting activities is common, mindfulness was negatively associated with the frequency of driver engagement in all distraction sources studied, apart from passenger interaction. Our results suggest that a single mindfulness intervention could potentially reduce driver engagement in multiple distracting activities at once, and therefore have significant utility as a distraction mitigation technique.

Exploring the mechanisms of distraction from in-vehicle technology: The development of the PARRC model

Technological advances are impacting in-vehicle systems, providing more secondary tasks for drivers to engage with. The adverse impacts of this on driver safety requires further insight to determine the mechanisms involved in distraction and countermeasures to mitigate it. Contemporary approaches highlight the need to move away from individualistic strategies in distraction mitigation, towards a systems perspective that accounts for the road transport system as a whole. This paper applied grounded theory to identify five key factors of distraction and their relationships that were incorporated into the Priority, Adapt, Resource, Regulate, Conflict (PARRC) systems model of driver distraction. Applying this model to a case study suggests how taking a systems perspective to the phenomenon may hold elements within the wider road transport system responsible for distraction incidents. This opens up the potential for countermeasures higher up in the causal chain of events.

What drives technology-based distractions? A structural equation model on social-psychological factors of technology-based driver distraction engagement

BackgroundWith the proliferation of new mobile and in-vehicle technologies, understanding the motivations behind a driver's voluntary engagement with such technologies is crucial from a safety perspective, yet is complex. Previous literature either surveyed a large number of distractions that may be diverse, or too focuses on one particular activity, such as cell phone use. Further, earlier studies about social-psychological factors underlying driver distraction tend to focus on one or two factors in-depth, and those that examine a more comprehensive set of factors are often limited in their analyses methods. ObjectiveThe present work considers a wide array of social-psychological factors within a structural equation model to predict their influence on a focused set of technology-based distractions. A better understanding of these facilitators can enhance the design of distraction mitigation strategies. MethodWe analysed survey responses about three technology-based driver distractions: holding phone conversations, manually interacting with cell phones, and adjusting the settings of in-vehicle technology, as well as responses on five social-psychological factors: attitude, descriptive norm, injunctive norm, technology inclination, and a risk/sensation seeking personality. Using data collected from 525 drivers (ages: 18–80), a structural equation model was built to analyse these social-psychological factors as latent variables influencing self-reported engagement in these three technology-based distractions. ResultsSelf-reported engagement in technology-based distractions was found to be largely influenced by attitudes about the distractions. Personality and social norms also played a significant role, but technology inclination did not. A closer look at two age groups (18–30 and 30+) showed that the effect of social norms, especially of injunctive norm (i.e., perceived approvals), was less prominent in the 30+ age group, while personality remained a significant predictor for the 30+ age group but marginally significant for the younger group. ConclusionFindings from this work provide insights into the social-psychological factors behind intentional engagement in technology-based distractions and in particular suggesting that these factors may be sensitive to demographic differences.

The Attentional Demand of Automobile Driving Revisited: Occlusion Distance as a Function of Task-Relevant Event Density in Realistic Driving Scenarios.

We studied the utility of occlusion distance as a function of task-relevant event density in realistic traffic scenarios with self-controlled speed. The visual occlusion technique is an established method for assessing visual demands of driving. However, occlusion time is not a highly informative measure of environmental task-relevant event density in self-paced driving scenarios because it partials out the effects of changes in driving speed. Self-determined occlusion times and distances of 97 drivers with varying backgrounds were analyzed in driving scenarios simulating real Finnish suburban and highway traffic environments with self-determined vehicle speed. Occlusion distances varied systematically with the expected environmental demands of the manipulated driving scenarios whereas the distributions of occlusion times remained more static across the scenarios. Systematic individual differences in the preferred occlusion distances were observed. More experienced drivers achieved better lane-keeping accuracy than inexperienced drivers with similar occlusion distances; however, driving experience was unexpectedly not a major factor for the preferred occlusion distances. Occlusion distance seems to be an informative measure for assessing task-relevant event density in realistic traffic scenarios with self-controlled speed. Occlusion time measures the visual demand of driving as the task-relevant event rate in time intervals, whereas occlusion distance measures the experienced task-relevant event density in distance intervals. The findings can be utilized in context-aware distraction mitigation systems, human-automated vehicle interaction, road speed prediction and design, as well as in the testing of visual in-vehicle tasks for inappropriate in-vehicle glancing behaviors in any dynamic traffic scenario for which appropriate individual occlusion distances can be defined.

Associations of distraction involvement and age with driver injury severities

IntroductionThis paper investigates the associations between the severity of injuries sustained by a driver who is involved in a two-vehicle crash, the existence and type of driver distraction as well as driver's age. Few studies investigated distraction as it relates to injury severity. Moreover, these studies did not consider driver age which is a significant factor related to driving behavior and the ability to respond in a crash situation. MethodsAn ordered logit model was built to predict injury severity sustained by drivers using data from the U.S. National Automotive Sampling System's General Estimates System (2003 to 2008). Various factors (e.g., weather, gender, and speeding) were statistically controlled for, but the main focus was on the interaction of driver age and distraction type. ResultsThe trends observed for young and mid-age drivers were similar. For these age groups, dialing or texting on the cell phone, passengers, and in-vehicle sources resulted in an increase in a likelihood of more severe injuries. Talking on the cell phone had a similar effect for younger drivers but was not significant for mid-age drivers. Inattention and distractions outside the vehicle decreased the odds of severe injuries. For older drivers, the highest odds of severe injuries were observed with dialing or texting on a cell phone, followed by in-vehicle sources and talking on the cell phone. All these sources were associated with an increased likelihood of injury severity. Similar to young and mid-age drivers, distractions outside the vehicle decreased the odds of severe injuries. Other distraction types did not have a significant effect for the older age group. ConclusionsThe results support previous literature and extend our understanding of crash injury severity. Practical applicationsThe findings have implications for policy making and the design of distraction mitigation systems.

A hybrid Bayesian Network approach to detect driver cognitive distraction

Driver cognitive distraction (e.g., hand-free cell phone conversation) can lead to unapparent, but detrimental, impairment to driving safety. Detecting cognitive distraction represents an important function for driver distraction mitigation systems. We developed a layered algorithm that integrated two data mining methods—Dynamic Bayesian Network (DBN) and supervised clustering—to detect cognitive distraction using eye movement and driving performance measures. In this study, the algorithm was trained and tested with the data collected in a simulator-based study, where drivers drove either with or without an auditory secondary task. We calculated 19 distraction indicators and defined cognitive distraction using the experimental condition (i.e., “distraction” as in the drives with the secondary task, and “no distraction” as in the drives without the secondary task). We compared the layered algorithm with previously developed DBN and Support Vector Machine (SVM) algorithms. The results showed that the layered algorithm achieved comparable prediction performance as the two alternatives. Nonetheless, the layered algorithm shortened training and prediction time compared to the original DBN because supervised clustering improved computational efficiency by reducing the number of inputs for DBNs. Moreover, the supervised clustering of the layered algorithm revealed rich information on the relationship between driver cognitive state and performance. This study demonstrates that the layered algorithm can capitalize on the best attributes of component data mining methods and can identify human cognitive state efficiently. The study also shows the value in considering the supervised clustering method as an approach to feature reduction in data mining applications.

Warn me now or inform me later: Drivers' acceptance of real-time and post-drive distraction mitigation systems

Vehicle crashes caused by driver distraction are of increasing concern. One approach to reduce the number of these crashes mitigates distraction by giving drivers feedback regarding their performance. For these mitigation systems to be effective, drivers must trust and accept them. The objective of this study was to evaluate real-time and post-drive mitigation systems designed to reduce driver distraction. The real-time mitigation system used visual and auditory warnings to alert the driver to distracting behavior. The post-drive mitigation system coached drivers on their performance and encouraged social conformism by comparing their performance to peers. A driving study with 36 participants between the ages of 25 and 50 years old (M=34) was conducted using a high-fidelity driving simulator. An extended Technology Acceptance Model captured drivers' acceptance of mitigation systems using four constructs: perceived ease of use, perceived usefulness, unobtrusiveness, and behavioral intention to use. Perceived ease of use was found to be the primary determinant and perceived usefulness the secondary determinant of behavioral intention to use, while the effect of unobtrusiveness on intention to use was fully mediated by perceived ease of use and perceived usefulness. The real-time system was more obtrusive and less easy to use than the post-drive system. Although this study included a relatively narrow age range (25 to 50 years old), older drivers found both systems more useful. These results suggest that informing drivers with detailed information of their driving performance after driving is more acceptable than warning drivers with auditory and visual alerts while driving.

How Dangerous Is Looking Away From the Road? Algorithms Predict Crash Risk From Glance Patterns in Naturalistic Driving

In this study, the authors used algorithms to estimate driver distraction and predict crash and near-crash risk on the basis of driver glance behavior using the data set of the 100-Car Naturalistic Driving Study. Driver distraction has been a leading cause of motor vehicle crashes, but the relationship between distractions and crash risk lacks detailed quantification. The authors compared 24 algorithms that varied according to how they incorporated three potential contributors to distraction--glance duration, glance history, and glance location--on how well the algorithms predicted crash risk. Distraction estimated from driver eye-glance patterns was positively associated with crash risk. The algorithms incorporating ongoing off-road glance duration predicted crash risk better than did the algorithms incorporating glance history.Augmenting glance duration with other elements of glance behavior--1.5th power of duration and duration weighted by glance location--produced similar prediction performance as glance duration alone. The distraction level estimated by the algorithms that include current glance duration provides the most sensitive indicator of crash risk. The results inform the design of algorithms to monitor driver state that support real-time distraction mitigation systems.

The Impact of Distraction Mitigation Strategies on Driving Performance

An experiment was conducted to assess the effects of distraction mitigation strategies on drivers' performance and productivity while engaged in an in-vehicle information system task. Previous studies show that in-vehicle tasks undermine driver safety and there is a need to mitigate driver distraction. An advising strategy that alerts drivers to potential dangers and a locking strategy that prevents the driver from continuing the distracting task were presented to 16 middle-aged and 12 older drivers in a driving simulator in two modes (auditory, visual) and two road conditions (curves, braking events). Distraction was a problem for both age groups. Visual distractions were more detrimental than auditory ones for curve negotiation, as depicted by more erratic steering, F (6, 155) = 26.76, p < .05. Drivers did brake more abruptly under auditory distractions, but this effect was mitigated by both the advising, t (155) = 8.37, p < .05, and locking strategies, t (155) = 8.49, p < .05. The locking strategy also resulted in longer minimum time to collision for middle-aged drivers engaged in visual distractions, F (6, 138) = 2.43, p < .05. Adaptive interfaces can reduce abrupt braking on curve entries resulting from auditory distractions and can also improve the braking response for distracted drivers. These strategies can be incorporated into existing in-vehicle systems, thus mitigating the effects of distraction and improving driver performance.