Driver Distraction Research Articles

Empowering Driver-Passenger Collaboration: Designing In-Car Systems with a Focus on Social Connectedness, Fairness, and Team Performance

Driving a car can be difficult when it comes to distractions caused by operating the in-vehicle infotainment system (IVIS). In-car passengers often help with performing IVIS-related tasks. However, an IVIS is often not designed with a focus on task collaboration. In this article, we focus on how to design in-car systems with the goal to support collaboration between a driver and a front-seat passenger. Based on infotainment-oriented tasks, we initially explore five key collaborative control concepts by means of an IVIS which differ from each other in terms of the number of available IVIS screens (one or two), access to menus (restricted and unrestricted), and the nature of performing tasks in parallel or one after the other. Results from a simulator study with N = 16 pairs show significant effects of the concepts on social collaboration in terms of perceived social connectedness (measured with sub-dimensions connectedness, affiliation, belongingness, companionship), team performance (coordination effectiveness and team cohesion), and fairness. We found that especially a dedicated passenger IVIS screen empowers front-seat passengers, reduces power dynamics, supports fairness, and minimizes driver distraction (caused by interacting passengers). We discuss the implications of these findings and posit recommendations to design future IVIS in passenger cars with improved driver-passenger collaboration by explicitly designing for balanced power roles, situational awareness, active communication, and a balance between drivers’ privacy and trust toward the passenger. Additionally, we outline a systematic overview of future work to explore the research field of driver-passenger collaboration in more breadth and depth.

Modeling Pedestrian Injury Severity: A Case Study of Using Extreme Gradient Boosting Vs Random Forest in Feature Selection

Walking and bicycling are lauded for their negative net carbon impact and for their health benefits. However, national crash statistics suggest that pedestrians are disproportionately harmed in any vehicle–pedestrian conflict situation. Although automated transportation in the future is anticipated to increase overall safety, multiple incidents involving automated vehicles have been reported recently, indicating that the technology needs more training on real-world scenarios and conflicts. This research is motivated by the need for contextual data and related levels of harm in potential conflict scenarios in mixed traffic and we use a national police reported crash dataset, CRSS, to address this need. Our study uses a new gradient boosting algorithm, XGBoost, to identify important features among a host of seemingly significant variables. We compare the performance of XGBoost with the more frequently used random forest method and find that XGBoost is more reliable and robust for handling an unbalanced and sparse dataset like crash data, and the features extracted are more aligned to findings from previous research on the topic. We also compare feature importance between NASS-GES and CRSS—two national crash databases with different sampling strategies but the same objective—and find that sampling strategy influences selection of feature importance. We further use the features extracted using XGBoost in a multiclass logistic regression to quantify the effect of these features on different levels of pedestrian injury. Our findings indicate that speed limit, light conditions, pre-crash movements, and location of pedestrian are important contributors to crash severity, along with driver distraction and impairment.

Examination of factors associated with fault status and injury severity in intersection-related rear-end crashes: Application of binary and bivariate ordered probit models

Rear-end crashes are a relatively common crash type and often occur at or near intersections. Although rear-end crashes are generally less severe than some other crash types, there were still 2,346 fatal and 595,000 injury rear-end crashes in the US in 2019 alone. These crashes are generally caused by one at-fault driver who strikes a leading vehicle, and it may be useful to assess factors associated with a driver being at-fault. Additionally, it is important to analyze factors associated with injury severity outcomes in order to develop countermeasures aimed at preventing severe injuries. As such, this study investigates factors related to fault status and injury severity (and the interrelation between the two) in two-vehicle intersection-related rear-end crashes using data from a southwestern US state. A binary probit model was estimated to assess factors associated with fault status, while a bivariate ordered probit model was estimated to assess factors associated with driver injury severity by fault status. Importantly, by modelling the injury severity of both crash-involved drivers jointly, potential within-crash correlation can be accounted for. The results of the fault status model indicated numerous factors were associated with a fault status including vehicle type, driver age, and driver impairment or distraction. The results of the bivariate injury severity model indicated numerous factors were significantly associated with injury severity and importantly, differences were observed between at-fault and not-at-fault drivers. Ultimately, the results of this study may assist in development of targeted countermeasures aimed at reducing both crash occurrence and severe injury outcomes.

Impact of non-driving related tasks while operating automated driving systems (ADS): A systematic review

Automated Driving Systems (ADS) (SAE, 2021), promise improved safety and comfort for drivers. Current technological advances have resulted in increased automation capabilities. However, with the increase in automation capabilities, there is a shift in how drivers interact with their vehicles. Drivers can now temporarily hand over the control of the driving task to ADS under certain conditions. However, with ADS in temporary control of the vehicle, drivers may choose to engage in non-driving related tasks (NDRT). The current capabilities of ADS do not allow drivers to hand over control of the driving task indefinitely. Drivers must remain aware and be ready to take back control if necessary. There is a need to better understand drivers’ performance and behaviors when driving with ADS, especially when engaged in NDRTs. This literature review, therefore, aims to understand the state of knowledge on automated vehicle systems and driver distraction. This review was conducted as per PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. Studies found a significant increase in takeover times while engaging in NDRTs and driving with automation active. Studies also discuss a change in driver’s visual attention, with more focus given to NDRTs as compared to the front roadway. The concerning effects of increasing reaction times and decreases in visual attention can be mitigated by using interventions and studies have had success in redirecting drivers attention and reorient them to the task of driving. The review, therefore, includes a discussion of ADS and NDRT engagement and its impact on driving behaviors such as take-over times, visual attention, trust, and workload. Implications on driver safety and performance are discussed in light of this synthesis.

How risky is distracted driving?

We use data on fatal crashes to quantify the risk of distracted driving. We repurpose, extend, and improve a methodology used to estimate the riskiness of drinking drivers (Levitt & Porter, 2001). Our analysis suggests that distracted drivers are three times more likely to cause a fatal crash than focused drivers. We also estimate that distracted drivers represent three to four percent of drivers on the road at any given time. Further, we find that distractions associated with cellphone use are less likely to cause a fatal crash than are distractions from other sources. The externality costs between $0.02 and $0.05 per mile driven. The insurance surcharge for a distracted driving citation that could internalize the avoidable insurance losses is approximately $577 per year. Our work extends the literature on distracted driving and traffic fatalities. We believe our results can inform policymakers on the traffic-safety and economic consequences of distracted driving.

Pose-aware Multi-position Feature Network for Driver Distraction Recognition

This paper introduce pose-aware multi-position feature network for driver distraction recognition, taking into account the high association between the visual and geometric features of neighboring human key points. This method first uses the object detector to detect the driver’s body, and then uses the pose estimation to detect the key points of the human body. Finally, the key points of the human body are deconstructed to obtain key visual features and spatial features.In order to create the visual representation that corresponds to each key point, all the chosen visual representations are sent into a convolutional neural network. The geometric locations of key points on the upper body are used to build the spatial reasoning module, which is then transmitted to the neural network to get spatial features. The visual appearance features are fused with geometric features to predict corresponding driver action through linear layers.The experimental result shows that our methods achieve comparative performance on our own HY Large Vehicle Driver Dataset and the public AUC Driver Distracted Dataset.

Do driver monitoring technologies improve the driving behaviour of distracted drivers? A simulation study to assess the impact of an auditory driver distraction warning device on driving performance

Being distracted while driving is a major cause of road crashes. To help prevent this particular type of crash, new driver monitoring technologies track ocular and head movement and alert drivers when periods of prolonged distraction are detected, thus preventing hazardous situations on the road. In this mixed-factorial multi-level experiment, the behaviour of drivers was measured as they performed a secondary task while being monitored via an auditory Driver Distraction Warning (A-DDW) device which reminded them to look at the road ahead and cease the activity which was the source of distraction. Here, we evaluated the effectiveness of this type of DDW device by measuring longitudinal and lateral behaviour in situations where the driver is intentionally distracted for a significant period of time, and when the device repeatedly reminds the driver of his/her primary task.Forty-two participants were randomly stratified into three groups and subjected to the following distraction levels: (i) not distracted (baseline), (ii) distracted, and (iii) distracted but interacting with the A-DDW device. All participants drove in (i) free-flowing and (ii) stable traffic conditions along straight motorway segments. Speed and speed deviation values for distracted drivers were lower than for undistracted ones but they also experienced a significant deterioration in vehicle lateral control. Conversely, drivers assisted by the A-DDW device experienced a considerable improvement in their lateral control even though their speed and speed deviation values were higher. The reaction times of distracted drivers interacting with the A-DDW device deteriorated as revealed in cognitive tests conducted before and after the driving task.

Modelling the response times of mobile phone distracted young drivers: A hybrid approach of decision tree and random parameters duration model

Research has shown the detrimental effects of using mobile phones whilst driving, which are more prominent and concerning for young drivers, who are often less experienced and riskier. As such, this study investigates young drivers’ response times when they encounter a safety–critical event on a suburban road whilst using a mobile phone. To collect high-quality trajectory data, the CARRS-Q advanced driving simulator was used. Thirty-two licenced young drivers were exposed to the sudden braking of the lead vehicle in their lane in three driving conditions: baseline (no phone conversation), handheld, and hands-free. Unlike extant studies, this paper proposes a hybrid modelling framework for the response times of distracted drivers. This framework combines a decision tree model and a correlated grouped random parameters duration model with heterogeneity-in-means. While the decision tree model identifies a priori relationship among main effects, the random parameter model captures unobserved heterogeneity and correlation between random parameters. The modelling results reveal that mobile phone distraction impairs response time behaviour for the majority of drivers. However, some drivers tend to respond earlier whilst being distracted, suggesting that the perceived risk of mobile use might have led to an early response, indicating their risk compensation behaviour. Female drivers tend to respond earlier compared to male drivers, indicating their safer and risk-averse behaviour. Overall, mobile phone distraction appears to deteriorate response time behaviour and poses a significant safety concern to drivers and the overall traffic stream unless mitigated.

Analysis of the effect of decorated interior walls on drivers' performance: From individual micro-behavior to brain activation

This study reports on the driving behavior changes and drivers' neural activation modifications as a response to information stimulation in the form of tunnel-decorated interior walls during simulated driving, revealing the underlying mechanisms of action. 35 participants performed a simulation experiment consisting of a driving simulator experiment (29 participants) and an fMRI experiment (30 participants, including 24 participants duplicated across both experiments). The study protocol consisted of two scenarios (a baseline and a decorated interior wall scenario) in which participants performed the baseline scenario to initiate the first testing. The driving behavior data (drivers' attention and mental workload, longitudinal speed regulation, and lateral control stability) and drivers' neural activation data were recorded and analyzed for the whole experimental session. This study used repeated measures generalized linear models (GLM) and Statistical Parameter Mapping (SPM12) platforms for statistical analysis. The results indicate that: 1) a decorated interior wall does affect drivers' attention and mental workload significantly, and drivers' boredom, psychological burden, and crash risk are reduced under the information stimulus. The color and element complexity of decorated interior tunnel walls constitute crucial factors in this information regulation mechanism, strongly related to driver distraction; 2) Under the influence of tunnel interior walls decorated with longitudinal strips, the driver's spatial perception and imagination are significantly improved, reducing anxiety and fear associated with hitting the interior walls. The lateral stability of vehicle operation is improved; however, compared with vertical stripes decoration, the effect of the tunnel interior wall decorated with horizontal stripes on drivers' speed regulation is not great.

A Portable Multi-Modal Cushion for Continuous Monitoring of a Driver's Vital Signs.

With higher levels of automation in vehicles, the need for robust driver monitoring systems increases, since it must be ensured that the driver can intervene at any moment. Drowsiness, stress and alcohol are still the main sources of driver distraction. However, physiological problems such as heart attacks and strokes also exhibit a significant risk for driver safety, especially with respect to the ageing population. In this paper, a portable cushion with four sensor units with multiple measurement modalities is presented. Capacitive electrocardiography, reflective photophlethysmography, magnetic induction measurement and seismocardiography are performed with the embedded sensors. The device can monitor the heart and respiratory rates of a vehicle driver. The promising results of the first proof-of-concept study with twenty participants in a driving simulator not only demonstrate the accuracy of the heart (above 70% of medical-grade heart rate estimations according to IEC 60601-2-27) and respiratory rate measurements (around 30% with errors below 2 BPM), but also that the cushion might be useful to monitor morphological changes in the capacitive electrocardiogram in some cases. The measurements can potentially be used to detect drowsiness and stress and thus the fitness of the driver, since heart rate variability and breathing rate variability can be captured. They are also useful for the early prediction of cardiovascular diseases, one of the main reasons for premature death. The data are publicly available in the UnoVis dataset.

ICMFed: An Incremental and Cost-Efficient Mechanism of Federated Meta-Learning for Driver Distraction Detection

Driver distraction detection (3D) is essential in improving the efficiency and safety of transportation systems. Considering the requirements for user privacy and the phenomenon of data growth in real-world scenarios, existing methods are insufficient to address four emerging challenges, i.e., data accumulation, communication optimization, data heterogeneity, and device heterogeneity. This paper presents an incremental and cost-efficient mechanism based on federated meta-learning, called ICMFed, to support the tasks of 3D by addressing the four challenges. In particular, it designs a temporal factor associated with local training batches to stabilize the local model training, introduces gradient filters of each model layer to optimize the client–server interaction, implements a normalized weight vector to enhance the global model aggregation process, and supports rapid personalization for each user by adapting the learned global meta-model. According to the evaluation made based on the standard dataset, ICMFed can outperform three baselines in training two common models (i.e., DenseNet and EfficientNet) with average accuracy improved by about 141.42%, training time saved by about 54.80%, communication cost reduced by about 54.94%, and service quality improved by about 96.86%.

EFFNet-CA: An Efficient Driver Distraction Detection Based on Multiscale Features Extractions and Channel Attention Mechanism.

Driver distraction is considered a main cause of road accidents, every year, thousands of people obtain serious injuries, and most of them lose their lives. In addition, a continuous increase can be found in road accidents due to driver's distractions, such as talking, drinking, and using electronic devices, among others. Similarly, several researchers have developed different traditional deep learning techniques for the efficient detection of driver activity. However, the current studies need further improvement due to the higher number of false predictions in real time. To cope with these issues, it is significant to develop an effective technique which detects driver's behavior in real time to prevent human lives and their property from being damaged. In this work, we develop a convolutional neural network (CNN)-based technique with the integration of a channel attention (CA) mechanism for efficient and effective detection of driver behavior. Moreover, we compared the proposed model with solo and integration flavors of various backbone models and CA such as VGG16, VGG16+CA, ResNet50, ResNet50+CA, Xception, Xception+CA, InceptionV3, InceptionV3+CA, and EfficientNetB0. Additionally, the proposed model obtained optimal performance in terms of evaluation metrics, for instance, accuracy, precision, recall, and F1-score using two well-known datasets such as AUC Distracted Driver (AUCD2) and State Farm Distracted Driver Detection (SFD3). The proposed model achieved 99.58% result in terms of accuracy using SFD3 while 98.97% accuracy on AUCD2 datasets.

Driver distraction detection via multi‐scale domain adaptation network

AbstractDistracted driving is the leading cause of road traffic accidents. It is essential to monitor the driver's status to avoid traffic accidents caused by distracted driving. Current research on detecting distracting behaviours focuses on analysing image features using convolutional neural networks (CNNs). However, the generalisation ability of the current distracted driving models is limited. This paper aims to improve the generalisation ability of distracted driving models that are affected by factors such as the driver himself, the background, the monitoring angle, and so on. A new driver distraction detection method, which is referred to as multi‐scale domain adaptation network (MSDAN), was proposed to improve model adaptability. The method consists of three stages: first, multi‐scale convolution was introduced to build a new backbone to accommodate better the valuable feature of the target on different scales. Secondly, the authors designed the domain adaptation network to improve the model's adaptability to the difference in data sources through adversarial training. Finally, dropout is added to the fully connected layer to increase the model's generalisation ability. The comparison results on the large‐scale driver distraction detection dataset show that the authors’ method can accurately detect driver distraction and has good generalisation performance, with an accuracy improvement in the cross‐driver and cross‐dataset experiments.

The impact of road traffic context on secondary task engagement while driving.

Driver distraction has been recognized for a long time as a significant road safety issue. It has been consistently reported that drivers spend considerable time engaged in activities that are secondary to the driving task. The temporary diversion of attention from safety-critical driving tasks has often been associated with various adverse driving outcomes, from minor driving errors to serious motor vehicle crashes. This study explores the role of the driving context on a driver's decision to engage in secondary activities non-critical to the driving task. The study utilises the Naturalistic Engagement in Secondary Tasks (NEST) dataset, a complementary dataset derived from the SHRP2 naturalistic dataset, the most extensive naturalistic study to date. An initial exploratory analysis is conducted to identify patterns of secondary task engagements in relation to context variables. Maximum likelihood Chi-square tests were applied to test for differences in engagement between types of driver distraction for the selected contextual variables. Pearson residual graphs were employed as a supplementary method to visually depict the residuals that constitute the chi-square statistic.Lastly, a two-step cluster analysis was conducted to identify common execution scenarios among secondary tasks. The exploratory analysis revealed interesting behavioral trends among drivers, with higher engagement rates in left curves compared to right curves, while driving uphill compared to driving downhill, in low-density traffic scenarios compared to high-density traffic scenarios, and during afternoon periods compared to morning periods. Significant differences in engagement were found among secondary tasks in relation to locality, speed, and roadway design. The clustering analysis showed no significant associations between driving scenarios of similar characteristics and the type of secondary activity executed. Overall, the findings confirm that the road traffic environment can influence how car drivers engage in distracted driving behavior.

Efficient Driver Anomaly Detection via Conditional Temporal Proposal and Classification Network

Detecting driver inattentive behaviors is crucial for driving safety in a driver monitoring system (DMS). Recent works treat driver distraction detection as a multiclass action recognition problem or a binary anomaly detection problem. The former approach aims to classify a fixed set of action classes. Although specific distraction classes can be predicted, this approach is inflexible to detect unknown driver anomalies. The latter approach mixes all distraction actions into one class: anomalous driving. Because the objective focuses on finding the difference between safe and distracted driving, this approach has better generalization in detecting unknown driver distractions. However, a detailed classification of the distraction is missing from the predictions, meaning that the downstream DMS can only treat all distractions with the same severity. In this work, we propose a two-phase anomaly proposal and classification framework [driver anomaly detection and classification network (DADCNet)] robust for open-set anomalies while maintaining high-level distraction understanding. DADCNet makes efficient allocation of multimodal and multiview inputs. The anomaly proposal network first utilizes a subset of the available modalities and views to suggest suspicious anomalous driving behavior. Then, the classification network employs more features to verify the anomaly proposal and classify the proposed distraction action. Through extensive experiments in two driver distraction datasets, our approach significantly reduces the total amount of computation during inference time while maintaining high anomaly detection sensitivity and robust performance in classifying common driver distractions.

Психофизиология невнимательности и утомляемости у водителей автотранспортных средств, отвлекающихся на электронные устройства

Summary. Investigation purposes – a researching of psychophysiological features and an opportunity of inattention and fatigue prevention in car drivers distracted for electronic devices. Materials and methods of the investigation. An analysis of 45 publications in Russian and English found using Cyberleninka, PubMed, Elibrary, ScienceDirect data bases was conducted. Investigation results and their analysis A distraction of car drivers for electronic devices make them instant fatigued because of attempts of combining of driving and with unrelated to it actions. A distraction for mobile phone can stimuli all forms of attention looses – auditory, visual, biomechanical, cognitive. A conclusion was made that nowadays algorithms for attention tiredness / distraction analysis in car drivers are not distributed enough.

Car-Following Behavior of Human-Driven Vehicles in Mixed-Flow Traffic: A Driving Simulator Study

Connected and autonomous vehicles (CAVs) and human-driven vehicles (HDVs) will inevitably coexist on roads in the future, creating mixed-flow traffic. The heterogeneous car-following (CF) behavior of HDVs can degrade the control performance of CAVs and introduce inefficiencies in CAV operations. To address these challenges, it is necessary to comprehend HDV CF behavior in mixed-flow traffic. This driving simulator-based study investigates HDV CF behavior in mixed-flow traffic under three different CAV control settings (string-stable, string-unstable, and HDV-like). The effects of traffic congestion level and demographic characteristics on CF behavior are also considered. Statistical analysis and CF model calibration, based on trajectory data collected from 72 participants in driving simulator experiments, are performed to examine the impacts of these factors on string stability, traffic efficiency, and safety. Then, online parameter estimation is conducted to illustrate the time-varying desired time headway, and sensitivity to spacing and speed variations (i.e., CF behavior evolution). Additionally, analysis of post-experiment interview results and eye-tracking data show that the string-stable CAV control setting is preferred by most HDV drivers, but can trigger driver distraction. The results also provide insights for CF behavior prediction and the optimal mixed platoon formation to enhance CAV benefits for traffic flow.

Development and Fabrication of Modified Eye Blinking Vehicle Accident Avoiding System

Abstract: The Eye Blinking Car Accident Avoiding System (EBCAAS) is a novel approach to enhance driving safety by detecting and preventing accidents caused by driver drowsiness or distraction. The system uses a camera mounted on the dashboard of the vehicle to continuously monitor the driver's eyes and detect any abnormal blinking patterns that may indicate fatigue or distraction. Upon detection of such patterns, the system sends an alert to the driver in the form of visual or audio cues, reminding them to stay alert and focused on the road. Additionally, the system is equipped with an automatic emergency braking mechanism that can be activated in the event of a potential collision. The EBCAAS has the potential to significantly reduce the incidence of accidents caused by driver fatigue or distraction and improve road safety for all users. Accidents due to drowsiness can be controlled and prevented with the help of eye blink sensor using IR rays. It consists of IR transmitter and an IR receiver. The transmitter transmits IR rays into the eye

An Emotion Recognition Based Assistant For Vehicles

Numerous studies have been conducted on automated vehicles. Additionally, considerable research has been done to increase vehicle safety. The majority of safety-related research done in this field uses hardware and electronic components. Lane keep assistance, parking assistance, and city break assistance are a few instances that can be given. Only a small number of software-related parts are utilized in current automobiles, nevertheless. The primary illustration for this is vehicle assistants. However, current vehicle aides can carry out commands and carry out some specified responsibilities for drivers or passengers. In some aspects, this might be able to lessen driver stickiness and increase driving enjoyment. In contrast to the current vehicle assistants, this proposed assistant represents a fresh method. It can detect drivers' emotions and adjust behavior accordingly. The major goal of this research is to reduce accidents that are caused by driver distraction. Additionally, this suggested helper may recognize emotional states in drivers, such as fatigue, grief, and anger, and act accordingly to keep them focused on the road.

Impact of In-Air Gestures on In-Car Task’s Diver Distraction

As in-vehicle information systems (IVIS) grow increasingly complex, the demand for innovative artificial intelligence-based interaction methods that enhance cybersecurity becomes more crucial. In-air gestures offer a promising solution due to their intuitiveness and individual uniqueness, potentially improving security in human–computer interactions. However, the impact of in-air gestures on driver distraction during in-vehicle tasks and the scarcity of skeleton-based in-air gesture recognition methods in IVIS remain largely unexplored. To address these challenges, we developed a skeleton-based framework specifically tailored for IVIS that recognizes in-air gestures, classifying them as static or dynamic. Our gesture model, tested on the large-scale AUTSL dataset, demonstrates accuracy comparable to state-of-the-art methods and increased efficiency on mobile devices. In comparative experiments between in-air gestures and touch interactions within a driving simulation environment, we established an evaluation system to assess the driver’s attention level during driving. Our findings indicate that in-air gestures provide a more efficient and less distracting interaction solution for IVIS in multi-goal driving environments, significantly improving driving performance by 65%. The proposed framework can serve as a valuable tool for designing future in-air gesture-based interfaces for IVIS, contributing to enhanced cybersecurity.