Effects of smartphone based advanced driver assistance system on distracted driving behavior: A simulator study

Modeling distracted driving behavior considering cognitive processes

The modeling of distracted driving behavior has been studied for many years, however, there remain many distraction phenomena that can not be fully modeled. This study proposes a new method that establishes the model using the queuing network model human processor (QN-MHP) framework. Unlike previous models that only consider distracted-driving-related human factors from a mathematical perspective, the proposed method reflects the information processing in the human brain, and simulates the distracted driver's cognitive processes based on a model structure supported by physiological and cognitive research evidence. Firstly, a cumulative activation effect model for external stimuli is adopted to mimic the phenomenon that a driver responds only to stimuli above a certain threshold. Then, dual-task queuing and switching mechanisms are modeled to reflect the cognitive resource allocation under distraction. Finally, the driver's action is modeled by the Intelligent Driver Model (IDM). The model is developed for visual distraction auditory distraction separately. 773 distracted car-following events from the Shanghai Naturalistic Driving Study data were used to calibrate and verify the model. Results show that the model parameters are more uniform and reasonable. Meanwhile, the model accuracy has improved by 57% and 66% compared to the two baseline models respectively. Moreover, the model demonstrates its ability to generate critical pre-crash scenarios and estimate the crash rate of distracted driving. The proposed model is expected to contribute to safety research regarding new vehicle technologies and traffic safety analysis.

Monitoring Distracted Driving Behaviours with Smartphones: An Extended Systematic Literature Review

Driver behaviour monitoring is a broad area of research, with a variety of methods and approaches. Distraction from the use of electronic devices, such as smartphones for texting or talking on the phone, is one of the leading causes of vehicle accidents. With the increasing number of sensors available in vehicles, there is an abundance of data available to monitor driver behaviour, but it has only been available to vehicle manufacturers and, to a limited extent, through proprietary solutions. Recently, research and practice have shifted the paradigm to the use of smartphones for driver monitoring and have fuelled efforts to support driving safety. This systematic review paper extends a preliminary, previously carried out author-centric literature review on smartphone-based driver monitoring approaches using snowballing search methods to illustrate the opportunities in using smartphones for driver distraction detection. Specifically, the paper reviews smartphone-based approaches to distracted driving behaviour detection, the smartphone sensors and detection methods applied, and the results obtained.

Driver distraction and in-vehicle interventions: A driving simulator study on visual attention and driving performance

Driving simulator studies are popular means to investigate driving behaviour in a controlled environment and test safety-critical events that would otherwise not be possible in real-world driving conditions. While several factors affect driving performance, driving distraction has been emphasised as a safety-critical issue across the globe. In this context, this study explores the impact of distraction imposed by mobile phone usage, i.e., writing and reading text messages, on driver behaviour. As part of the greater i-DREAMS project, this study uses a car driving simulator experimental design in Germany to investigate driver behaviour under various conditions: (I) monitoring scenario representing normal driving conditions, (II) intervention scenario in which drivers receive fixed timing in-vehicle intervention in case of unsafe driving manoeuvres, and (III) distraction scenario in which drivers receive in-vehicle interventions based on task completion capability, where mobile phone distraction is imposed. Besides, eye-tracking glasses are used to further explore drivers' attention allocation and eye movement behaviour. This research focuses on driver response to risky traffic events (i.e., potential pedestrian collisions, and tailgating) and the impact of distraction on driving performance, by analysing a set of eye movement and driving performance measures of 58 participants. The results reveal a significant change in drivers' gaze patterns during the distraction drives with significantly higher gaze points towards the i-DREAMS intervention display (the utilised advanced driver assistance systems in this study). The overall statistical analysis of driving performance measures suggests nearly similar impacts on driver behaviour during distraction drives; a higher deviation of lateral positioning was noted irrespective of the event risk levels and lower longitudinal acceleration rates were observed for pedestrian collisions and non-critical events during distracted driving.

On the impact of advanced driver assistance systems on driving distraction and risky behaviour: An empirical analysis of irish commercial drivers

Advanced driver assistance systems (ADAS) present promising benefits in mitigating road collisions. However, these benefits are limited when risky drivers continue engaging in distraction events. While there is evidence that real-time warnings help improve driving behaviour, the sustained benefits of warning-based ADAS on reducing driving distraction in light commercial vehicle (LCV) drivers remain unclear. This research determines the effect of receiving instant distraction warnings over two years using a naturalistic driving dataset comprising around one million trips from 373 LCV drivers in the Republic of Ireland. Furthermore, the study applies Association Rule Mining (ARM) to find the contextual variables (e.g., speed limit, road type, traffic conditions) that increase the likelihood of distraction events. The results show that warning-based ADAS providing real-time warnings helps reduce distraction events triggering driver inattention, forward collision, and lane departure warnings. Over half of the studied fleet reduced these warnings by at least 50% - lane departure after two months and driver inattention and forward collision after six months. It is found that both passive and active monitoring systems, coupled with coaching and rewards, significantly reduce aggressive driving behaviours tied to harsh acceleration (by 76%) and harsh braking (by 65%). The results of ARM show that the driving context introduces explanatory information for road safety programs. Low-speed urban roads and the summer season increase the likelihood of driver inattention and forward collision warnings. In contrast, high-speed rural roads increase the likelihood of lane departure warnings. These research findings support road safety stakeholders in developing risk assessments based on warning-based ADAS, targeted campaigns to reduce driving distraction, and driving coaching programs.

Distracted driving behavior in patients with insomnia

Insomnia is one of the most common sleep disorders and is characterized by a subjective perception of difficulty falling asleep. Drivers with insomnia are vulnerable to distraction and exhibit higher levels of risk while driving. This study investigated the effect of two sources of in-vehicle distractions on the driving performance of drivers with insomnia and good sleepers by analyzing different driving behavior measures. Twenty-one drivers with insomnia and twenty-one healthy volunteers were recruited to complete simulated driving dual tasks. The primary task required the participants to perform: (a) a lane-keeping task, and (b) a lane-change task. The secondary task required the participants to deal with: (a) baseline (non-task), (b) internal distraction task, and (c) external distraction task. The internal distraction task required participants to complete quantitative reasoning tasks, while the external distraction task was a 0-back test. The relationship between distracted driving ability and cognitive function was also investigated. The results demonstrate that for lane-keeping tasks, drivers with insomnia had significantly higher standard deviations (SD) for speed, throttle position, acceleration, and lateral position than healthy drivers under internal distraction, but the driving performance did not differ significantly between groups under internal distraction or baseline. In the lane-change task, drivers with insomnia had higher SDs for steering wheel angle, steer angular velocity, lateral acceleration, and lateral speed than healthy drivers under external distraction. Moreover, external distraction impaired driving behavior in the healthy group, while internal distraction impaired driving ability in both groups. Healthy drivers with cognitive impairment displayed impaired lane-keeping abilities under internal distractions and impaired lane-changing abilities under external distractions. Driving performance in the insomnia group was not significantly associated with cognitive function. The results demonstrate that insomnia and distraction impair driving ability, and driver performance is affected differently by the distraction source (internal or external). The driving ability of healthy drivers with decreased cognition was impaired, but not that of insomniacs.The findings of this study provide new insights for preventing and estimating the potential influence of distracted driving behavior in individuals with insomnia.

Texting While Driving: A Literature Review on Driving Simulator Studies

Road safety is increasingly threatened by distracted driving. Studies have shown that there is a significantly increased risk for a driver of being involved in a car crash due to visual distractions (not watching the road), manual distractions (hands are off the wheel for other non-driving activities), and cognitive and acoustic distractions (the driver is not focused on the driving task). Driving simulators (DSs) are powerful tools for identifying drivers' responses to different distracting factors in a safe manner. This paper aims to systematically review simulator-based studies to investigate what types of distractions are introduced when using the phone for texting while driving (TWD), what hardware and measures are used to analyze distraction, and what the impact of using mobile devices to read and write messages while driving is on driving performance. The review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analysis extension for Scoping Reviews (PRISMA-ScR) guidelines. A total of 7151 studies were identified in the database search, of which 67 were included in the review, and they were analyzed in order to respond to four research questions. The main findings revealed that TWD distraction has negative effects on driving performance, affecting drivers' divided attention and concentration, which can lead to potentially life-threatening traffic events. We also provide several recommendations for driving simulators that can ensure high reliability and validity for experiments. This review can serve as a basis for regulators and interested parties to propose restrictions related to using mobile phones in a vehicle and improve road safety.

How do the type and duration of distraction affect speed selection and crash risk? An evaluation using naturalistic driving data

Distracted driving is among the leading causes of roadway crashes worldwide. However, due to limitations of police-reported crash data, it is often challenging to understand the nature and magnitude of this problem. Distraction has also been shown to affect driver speed selection, which is important as both mean speed and speed variance have substantive impacts on crash risk. This study utilizes naturalistic driving data to investigate the relationship between the engagement in various secondary (non-driving) tasks and driver speed selection under different driving contexts. Separate analyses were conducted for low-speed and high-speed driving environments. Two-way random effects linear regression models were estimated for both speed regimes, while controlling for driver, roadway, and traffic characteristics. The differences were assessed based upon ten types of secondary tasks. In general, engagement in all tasks was found to decrease speeds in high-speed environments while the effects were mixed in low-speed settings. The changes in speeds were much pronounced for secondary tasks that include a combination of visual, manual, and cognitive distractions, such as cell phone use. Among all secondary tasks, an average episode of a driver talking on a handheld cellphone was associated with a 6-mph speed reduction in high-speed environments, but a 3.5-mph increase in low-speed settings. In addition to examining impacts on speed selection, the risk of involvement in crash and near-crash events was also evaluated in consideration of the type and duration of distraction. Interestingly, distractions tended to show similar relationships, in both direction and magnitude, with the risk of involvement in both crash and near-crash events. From a policy standpoint, this study provides further motivation for legislation and other programs aimed at curbing distracted driving.

Fear of Missing Out Predicts Distraction by Social Reward Signals Displayed on a Smartphone in Difficult Driving Situations

Smartphones are particularly likely to elicit driver distraction with obvious negative repercussions on road safety. Recent selective attention models lead to expect that smartphones might be very effective in capturing attention due to their social reward history. Hence, individual differences in terms of Fear of Missing Out (FoMO) – i.e., of the apprehension of missing out on socially rewarding experiences – should play an important role in driver distraction. This factor has already been associated with self-reported estimations of greater attention paid to smartphones while driving, but the potential link between FoMO and smartphone-induced distraction has never been tested empirically. Therefore, we conducted a preliminary study to investigate whether FoMO would modulate attentional capture by reward distractors displayed on a smartphone. First, participants performed a classical visual search task in which neutral stimuli (colored circles) were associated with high or low social reward outcomes. Then, they had to detect a pedestrian or a roe deer in driving scenes with various levels of fog density. The social reward stimuli were displayed as distractors on the screen of a smartphone embedded in the pictures. The results showed a significant three-way interaction between FoMO, social reward distraction, and task difficulty. More precisely, under attention-demanding conditions (i.e., high-fog density), individual FoMO scores predicted attentional capture by social reward distractors, with longer reaction times (RTs) for high rather than low social reward distractors. These results highlight the importance to consider reward history and FoMO when investigating smartphone-based distraction. Limitations are discussed, notably regarding our sample characteristics (i.e., mainly young females) that might hamper the generalization of our findings to the overall population. Future research directions are provided.

Effect Evaluation of Forward Collision Warning System Using IoT Log and Virtual Driving Simulation Data

Advanced driver-assistance systems (ADAS) are primarily known for their positive impact in improving the safety of drivers. Previous studies primarily analyzed the positive effects of ADAS with short-term experiments and accident data without considering the long-term changes in drivers’ safety perception. The human factor is the most dominant among factors that cause traffic accidents, and safety effect evaluation should be performed considering changes in human errors. To this end, this study classified the safety effect of ADAS-forward collision warning (FCW) on taxi drivers in Seoul into behavioral control and attitude change to perform analysis on respective factors. With regard to behavioral control, virtual driving simulation was used to analyze the reaction time of drivers and deceleration rate, and for attitude change, autoregressive integrated moving average (ARIMA) time series analysis was employed to predict the long-term perception change of drivers. The analysis results indicated that, in terms of behavioral control, ADAS-FCW reduces the cognitive reaction time of drivers in risk situations on the road, similar to the findings in previous studies. However, in terms of attitude change, ADAS-FCW has the adverse long-term effect of increasing violations in maintaining safety distance in the case of nighttime-drivers under 60 years old. As can be seen from these results, new technologies in the road safety arena can have a short-term effect of improving safety with behavioral control but may have a negative impact in the long term. The results of this study are expected to provide a theoretical basis for reference in the safety evaluation of ADAS and traffic safety facilities.

Driving Performance and Technology Acceptance Evaluation in Real Traffic of a Smartphone-Based Driver Assistance System

Technological advances are changing every aspect of our lives, from the way we work, to how we learn and communicate. Advanced driver assistance systems (ADAS) have seen an increased interest due to the potential of ensuring a safer environment for all road users. This study investigates the use of a smartphone-based ADAS in terms of driving performance and driver acceptance, with the aim of improving road safety. The mobile application uses both cameras of a smartphone to monitor the traffic scene and the driver’s head orientation, and offers an intuitive user interface that can display information in a standard mode or in augmented reality (AR). A real traffic experiment consisting of two driving conditions (a baseline scenario and an ADAS scenario), was conducted in Brasov, Romania. Objective and subjective data were recorded from twenty-four participants with a valid driver’s license. Results showed that the use of the ADAS influences the driving performance, as most of them adopted an increased time headway and lower mean speeds. The technology acceptance model (TAM) questionnaire was used to assess the users’ acceptance of the proposed driver assistance system. The results showed significant interrelations between acceptance factors, while the hierarchical regression analysis indicates that the variance of behavioral intention (BI) can be predicted by attitude toward behavior.

Assessment pedestrian crossing safety using vehicle-pedestrian interaction data through two different approaches: Fixed videography (FV) vs In-Motion Videography (IMV)

A significant portion of pedestrian accidents occurs in the outskirts areas due to the high vehicle speed and lack of safety facilities for pedestrians. Behavioral study on drivers and pedestrians is the key to better understand the causes of pedestrian accidents in order to develop safety models. Despite numerous studies on pedestrian safety based on various roads, outskirt areas have not been considered. Hence, the present study focuses on evaluating the safety of pedestrian crossing in urban and outskirt areas and to determine the differences of drivers and pedestrians' behaviors between these areas through data based on fixed videography (FV) and in-motion videography (IMV). These approaches may lead to an exact analysis of the behavioral differences of road users behaviors from the perspective of pedestrians (FV data) and drivers (IMV data) in urban and outskirts roads. Accordingly, behavioral studies were conducted at urban and outskirts sites through FV as well as IMV using the behavior of 29 participants in the same roads in Babol city, Iran. The gap acceptance model using linear regression and pedestrian crossing probability model using logistic regression for both approaches showed similarity on results in both urban and outskirts roads. Furthermore, behaviors of pedestrians crossing and drivers' yielding on urban and outskirts roads were very similar. Vehicle speed, the distance of vehicle to pedestrian at the possible collision point, size of pedestrian groups, and waiting time before crossing were the most important behavioral differences of pedestrian for choosing a gap acceptance and probability of crossing on various sites through two different approaches. The inference of the models obtained in this study will lead to a better understanding of the behavior of road users for studies on advanced driving assistance systems (ADAS).

The Application of Augmented Reality in the Automotive Industry: A Systematic Literature Review

Augmented reality (AR) is a fairly new technology enabling human machine interaction by superimposing virtual information on a real environment. Potential applications can be found in many areas of research from recent years. This study presents a systematic review of existing AR systems in the automotive field, synthesizing 55 studies from 2002 to 2019. The main research questions are: where AR technology has been applied within the automotive industry, what is the purpose of its application, what are the general characteristics of these systems, and what are the emphasized benefits and challenges of using AR in this field? The aim of this paper is to provide an insight into the AR applications and technologies in the automotive field.

A critical overview of driver recording tools

INTRODUCTION

Technological advancements during recent decades have led to the development of a wide array of tools and methods in order to record driving behavior and measure various aspects of driving performance. The aim of the present study is to present and comparatively assess the various driver recording tools that researchers have at their disposal.

METHOD

In order to achieve this aim, a multitude of published studies from the international literature have been examined based on the driver recording methodologies that have been implemented. An examination of more traditional survey methods (questionnaires, police reports, and direct observer methods) is initially conducted, followed by investigating issues pertinent to the use of driving simulators. Afterwards, an extensive section is provided for naturalistic driving data tools, including the utilization of on-board diagnostics (OBD) and in-vehicle data recorders (IVDRs). Lastly, in-depth incident analysis and the exploitation of smartphone data are discussed.

RESULTS

A critical synthesis of the results is conducted, providing the advantages and disadvantages of utilizing each tool and including additional knowledge regarding ease of experimental implementation, data handling issues, impacts on subsequent analyses, as well as the respective cost parameters.

CONCLUSIONS

New technologies provide undeniably powerful tools that allow for seamless data handling, storage, and analysis, such as smartphones and in-vehicle data recorders. However, this sometimes comes at considerable costs (which may or may not pay off at a later stage), while legacy driver recording methods still have their own niches to fill in research. Practical Applications: The present research supports researchers when designing driver behavior monitoring studies. The present work enables better scheduling and pacing of research activities, but can also provide insights for the distribution of research funds.

Control Oriented Prediction of Driver Brake Intention and Intensity Using a Composite Machine Learning Approach

Driver perception, decision, and control behaviors are easily affected by traffic conditions and driving style, showing the tendency of randomness and personalization. Brake intention and intensity are integrated and control-oriented parameters that are crucial to the development of an intelligent braking system. In this paper, a composite machine learning approach was proposed to predict driver brake intention and intensity with a proper prediction horizon. Various driving data were collected from Controller Area Network (CAN) bus under a real driving condition, which mainly contained urban and rural road types. ReliefF and RReliefF (they don’t have abbreviations) algorithms were employed as feature subset selection methods and applied in a prepossessing step before the training. The rank importance of selected predictors exhibited different trends or even negative trends when predicting brake intention and intensity. A soft clustering algorithm, Fuzzy C-means, was adopted to label the brake intention into categories, namely slight, medium, intensive, and emergency braking. Data sets with misplaced labels were used for training of an ensemble machine learning method, random forest. It was validated that brake intention could be accurately predicted 0.5 s ahead. An open-loop nonlinear autoregressive with external input (NARX) network was capable of learning the long-term dependencies in comparison to the static neural network and was suggested for online recognition and prediction of brake intensity 1 s in advance. As system redundancy and fault tolerance, a close-loop NARX network could be adopted for brake intensity prediction in the case of possible sensor failure and loss of CAN message.