Understanding the impacts of mobile phone distraction on driving performance: A systematic review

The use of mobile phones while driving—one of the most common driver distractions—has been a significant research interest during the most recent decade. While there has been a considerable amount research and excellent reviews on how mobile phone distractions influence various aspects of driving performance, the mechanisms by which the interactions with mobile phone affect driver performance is relatively unexamined. As such, the aim of this study is to examine the mechanisms involved with mobile phone distractions such as conversing, texting, and reading and the driving task, and subsequent outcomes. A novel human-machine framework is proposed to isolate the components and various interactions associated with mobile phone distracted driving. The proposed framework specifies the impacts of mobile phone distraction as an inter-related system of outcomes such as speed selection, lane deviations and crashes; human-car controls such as steering control and brake pedal use and human-environment interactions such as visual scanning and navigation. Eleven literature-review/meta-analyses papers and 62 recent research articles from 2005 to 2015 are critically reviewed and synthesised following a systematic classification scheme derived from the human-machine system framework. The analysis shows that while many studies have attempted to measure system outcomes or driving performance, research on how drivers interactively manage in-vehicle secondary tasks and adapt their driving behaviour while distracted is scant. A systematic approach may bolster efforts to examine comprehensively the performance of distracted drivers and their impact over the transportation system by considering all system components and interactions of drivers with mobile phones and vehicles. The proposed human-machine framework not only contributes to the literature on mobile phone distraction and safety, but also assists in identifying the research needs and promising strategies for mitigating mobile phone-related safety issues. Technology based countermeasures that can provide real-time feedback or alerts to drivers based on eye/head movements in conjunction with vehicle dynamics should be an important research direction.     

Classifiers for driver activity monitoring

The goal of this work is the detection and classification of driver activities in an automobile using computer vision. To this end, this paper presents a novel two-step classification algorithm, namely, an unsupervised clustering algorithm for grouping the actions of a driver during a certain period of time, followed by a supervised activity classification algorithm. The main contribution of this work is the combination of the two methods to provide a computationally fast solution for deployment in real-world scenarios that is robust to illumination and segmentation issues under most conditions experienced in the automobile environment. The unsupervised clustering groups the actions of the driver based on the relative motion detected using a skin-color segmentation algorithm, while the activity classifier is a binary Bayesian eigenimage classifier. Activities are grouped as safe or unsafe and the results of the classification are shown on several subjects obtained from two distinct driving video sequences.     

Personalised feedback and eco-driving: An explorative study

Conventional road transport has negative impact on the environment. Stimulating eco-driving through feedback to the driver about his/her energy conservation performance has the potential to reduce CO₂ emissions and promote fuel cost savings. Not all drivers respond well to the same type of feedback. Research has shown that different drivers are attracted to different types of information and feedback. The goal of this paper is to explore which different driver segments with specific psychographic characteristics can be distinguished, how these characteristics can be used in the development of an ecodriving support system and whether tailoring eco-driving feedback technology to these different driver segments will lead to increased acceptance and thus effectiveness of the eco feedback technology. The driver segments are based on the value orientation theory and learning orientation theory. Different possibilities for feedback were tested in an exploratory study in a driving simulator. An explorative study was selected since the choice of the display (how and when the information is presented) may have a strong impact on the results. This makes testing of the selected driver segments very difficult. The results of the study nevertheless suggest that adapting the display to a driver segment showed an increase in acceptance in certain cases. The results showed small differences for ratings on acceptation, ease of use, favouritism and a lower general rating between matched (e.g., learning display with learning oriented drivers) and mismatched displays (e.g., learning display with performance oriented drivers). Using a display that gives historical feedback and incorporates learning elements suggested a non-verifiable increase in acceptance for learning oriented drivers. However historical feedback and learning elements may be less effective for performance oriented drivers, who may need comparative feedback and game elements to improve energy conserving driving behaviour.     

Crash prediction with behavioral and physiological features for advanced vehicle collision avoidance system

Real-time crash prediction is the key component of the Vehicle Collision Avoidance System (VCAS) and other driver assistance systems. The further improvements of predictability requires the systemic estimation of crash risks in the driver-vehicle-environment loop. Therefore, this study designed and validated a prediction method based on the supervised learning model with added behavioral and physiological features. The data samples were extracted from 130 drivers’ simulator driving, and included various features generated from synchronized recording of vehicle dynamics, distance metrics, driving behaviors, fixations and physiological measures. In order to identify the optimal configuration of proposed method, the Discriminant Analysis (DA) with different features and models (i.e. linear or quadratic) was tested to classify the crash samples and non-crash samples. The results demonstrated the significant improvements of accuracy and specificity with added visual and physiological features. The different models also showed significant effects on the characteristics of sensitivity and specificity. These results supported the effectiveness of crash prediction by quantifying drivers’ risky states as inputs. More importantly, such an approach also provides opportunities to integrate the driver state monitoring into other vehicle-mounted systems at the software level.     

The “field of safe travel” revisited: interpreting driving behaviour performance through a holistic approach

The present paper proposes a conceptual framework for the driver’s visual–spatial perceptual processes. Based on a theoretical analysis of driving proposed by Gibson and Crooks [(1938). A theoretical field-analysis of automobile-driving. The American Journal of Psychology, 51, 453–471. doi:10.2307/1416145], the developed field of safe travel (FoST) framework suggests that at any moment the driver constructs a “field” by integrating two perceptual entities: (i) the possible available spatial fields for locomotion and (ii) the driver’s mental image of ego-vehicle outer-line and motion dynamics. This framework is used to reinterpret in a unified way a number of disparate research findings reported in the literature concerning specific driving sub-tasks (e.g. lane keeping and car following). It is argued that the FoST framework may be used to predict drivers’ behaviour in various traffic/situation environments based on their prioritisation between the above two perceptual entities. Implications of the proposed framework at a theoretical and practical level, in view of the future of driving with multiple levels of automation, are also discussed.     

Manual vs. adaptive cruise control – Can driver’s expectation be matched?

The role of the driver in the longitudinal car following control task will change from operator to supervisor with most of manual control replaced by automation as adaptive cruise control (ACC) technologies become commonplace. The extent to which manual control can be replaced by ACC will be determined by many factors. An important issue is the compatibility between ACC performance and the driver’s expectations.This paper describes the results of a simulation study of the performance of ACC relative to driver expectation. Driver’s expectation is quantitatively defined as the expected deceleration rate for several time-to-collision (TTC) levels, and an absolute minimum TTC that drivers tried to avoid in all cases. A two-level ACC algorithm was used to simulate the performance of an ACC equipped vehicle in various scenarios, and the result was compared to the driver’s expectations. The investigation has focused on scenarios which ACC is able to manage technically, but where driver expectations might be breached.By systematically changing variables such as the parameters of the ACC algorithms, traffic scenarios and time-headway settings, a large number of situations have been tested. The results have revealed that whilst appropriate ACC settings can be found which will meet the driver’s expectations, the ACC settings that are most capable in a range of traffic conditions are not necessarily the most user-friendly. A discussion on the implications of the findings is also presented.     

Interface design considerations for an in-vehicle eco-driving assistance system

This high-fidelity driving simulator study used a paired comparison design to investigate the effectiveness of 12 potential eco-driving interfaces. Previous work has demonstrated fuel economy improvements through the provision of in-vehicle eco-driving guidance using a visual or haptic interface. This study uses an eco-driving assistance system that advises the driver of the most fuel efficient accelerator pedal angle, in real time. Assistance was provided to drivers through a visual dashboard display, a multimodal visual dashboard and auditory tone combination, or a haptic accelerator pedal. The style of advice delivery was varied within each modality. The effectiveness of the eco-driving guidance was assessed via subjective feedback, and objectively through the pedal angle error between system-requested and participant-selected accelerator pedal angle. Comparisons amongst the six haptic systems suggest that drivers are guided best by a force feedback system, where a driver experiences a step change in force applied against their foot when they accelerate inefficiently. Subjective impressions also identified this system as more effective than a stiffness feedback system involving a more gradual change in pedal feedback. For interfaces with a visual component, drivers produced smaller pedal errors with an in-vehicle visual display containing second order information on the required rate of change of pedal angle, in addition to current fuel economy information. This was supported by subjective feedback. The presence of complementary audio alerts improved eco-driving performance and reduced visual distraction from the roadway. The results of this study can inform the further development of an in-vehicle assistance system that supports ‘green’ driving.     

Modelling driver distraction effects due to mobile phone use on reaction time

Phone use during driving causes decrease in situation awareness and delays response to the events happening in driving environment which may lead to accidents. Reaction time is one of the most suitable parameters to measure the effect of distraction on event detection performance. Therefore, this paper reports the results of a simulator study which analysed and modelled the effects of mobile phone distraction upon reaction time of the Indian drivers belonging to three different age groups. Two different types of hazardous events: (1) pedestrian crossing event and (2) road crossing event by parked vehicles were included for measuring drivers’ reaction times. Four types of mobile phone distraction tasks: simple conversation, complex conversation, simple texting and complex texting were included in the experiment. Two Weibull AFT (Accelerated Failure Time) models were developed for the reaction times against both the events separately, by taking all the phone use conditions and various other factors (such as age, gender, and phone use habits during driving) as explanatory variables. The developed models showed that in case of pedestrian crossing event, the phone use tasks: simple conversation, complex conversation, simple texting and complex texting caused 40%, 95%, 137% and 204% increment in the reaction times and in case of road crossing event by parked vehicles, the tasks caused 48%, 65%, 121% and 171% increment in reaction times respectively. Thus all the phone use conditions proved to be the most significant factors in degrading the driving performance.     

Driving analytics using smartphones: Algorithms,comparisons and challenges

The present work investigates the use of smartphones as an alternative to gather data for driving behavior analysis. The proposed approach incorporates i. a device reorientation algorithm, which leverages gyroscope, accelerometer and GPS information, to correct the raw accelerometer data, and ii. a machine-learning framework based on rough set theory to identify rules and detect critical patterns solely based on the corrected accelerometer data. To evaluate the proposed framework, a series of driving experiments are conducted in both controlled and “free-driving” conditions. In all experiments, the smartphone can be freely positioned inside the subject vehicle. Findings indicate that the smartphone-based algorithms may accurately detect four distinct patterns (braking, acceleration, left cornering and right cornering) with an average accuracy comparable to other popular detection approaches based on data collected using a fixed position device.     

Real time detection of driver attention: Emerging solutions based on robust iconic classifiers and dictionary of poses

Real time monitoring of driver attention by computer vision techniques is a key issue in the development of advanced driver assistance systems. While past work mostly focused on structured feature-based approaches, characterized by high computational requirements, emerging technologies based on iconic classifiers recently proved to be good candidates for the implementation of accurate and real-time solutions, characterized by simplicity and automatic fast training stages.In this work the combined use of binary classifiers and iconic data reduction, based on Sanger neural networks, is proposed, detailing critical aspects related to the application of this approach to the specific problem of driving assistance. In particular it is investigated the possibility of a simplified learning stage, based on a small dictionary of poses, that makes the system almost independent from the actual user.On-board experiments demonstrate the effectiveness of the approach, even in case of noise and adverse light conditions. Moreover the system proved unexpected robustness to various categories of users, including people with beard and eyeglasses. Temporal integration of classification results, together with a partial distinction among visual distraction and fatigue effects, make the proposed technology an excellent candidate for the exploration of adaptive and user-centered applications in the automotive field.     

The design of an in-vehicle assistance system to support eco-driving

This driving simulator study was the second of two studies investigating the most effective and acceptable in-vehicle system for the provision of guidance on fuel efficient accelerator usage. Three eco-driving interfaces were selected for test (a second-order display visual display with auditory alerts and two haptic accelerator pedal systems) following a pilot study of 12 different interfaces. These systems were tested in a range of eco-driving scenarios involving acceleration, deceleration and speed maintenance, and assessed through their effects on fuel economy, vehicle control, distraction, and driver subjective feedback. The results suggest that a haptic accelerator pedal system is most effective for preventing over-acceleration, whilst minimal differences were observed between systems in terms of the effect of the assistance provided to prevent under-acceleration. The visual–auditory interface lowered the time spent looking towards the road, indicating a potential negative impact on driver safety from using this modality to provide continuous green driving support. Subjective results were consistent with the objective findings, with haptic pedal systems creating lower perceived workload than a visual–auditory interface. Driver acceptability ratings suggested a slight favouring of a haptic-force system for its usefulness, whereas the more subtle haptic-stiffness system was judged more acceptable to use. These findings offer suggestions for the design of a user-friendly, eco-driving device that can help drivers improve their fuel economy, specifically through the provision of real-time guidance on the manipulation of the accelerator pedal position.     

Can feedback from in-vehicle data recorders improve driver behavior and reduce fuel consumption?

This paper evaluates the effectiveness of feedback, based on In-Vehicle Data Recorders (IVDR), to improve driving behavior, increase driving safety, and reduce fuel consumption. We developed a framework for driving-behavior measurement, incorporating second-by-second data collected by IVDRs. IVDR units were installed in over 150 vehicles driven by more than 350 drivers for over a year. The experiment was divided into three stages. The first stage was a “blind”, control stage, with no feedback. The second stage incorporated verbal feedback given only to riskiest drivers. In the third stage all drivers received a bi-weekly written report about their driving performance. Safety events, such as braking, lateral acceleration or speeding, were recorded. Supplementary data regarding safety related events and fuel consumption were also collected. Safety incidents and fuel consumption were modeled as a function of IVDR measurement-based events, in order to identify which events best reflect safety incidents and excessive fuel consumption. Our results show that braking events best explain safety incidents, and all events together best explain fuel consumption. In addition, we found that for the riskiest drivers, feedback significantly reduced the IVDR events. Our models show that feedback can lead to a reduction of 8% in safety incidents, and 3–10% in fuel consumption, with a larger reduction obtained for large vehicles.     

Lane changing intention recognition based on speech recognition models

Poor driving habits such as not using turn signals when changing lanes present a major challenge to advanced driver assistance systems that rely on turn signals. To address this problem, we propose a novel algorithm combining the hidden Markov model (HMM) and Bayesian filtering (BF) techniques to recognize a driver’s lane changing intention. In the HMM component, the grammar definition is inspired by speech recognition models, and the output is a preliminary behavior classification. As for the BF component, the final behavior classification is produced based on the current and preceding outputs of the HMMs. A naturalistic data set is used to train and validate the proposed algorithm. The results reveal that the proposed HMM–BF framework can achieve a recognition accuracy of 93.5% and 90.3% for right and left lane changing, respectively, which is a significant improvement compared with the HMM-only algorithm. The recognition time results show that the proposed algorithm can recognize a behavior correctly at an early stage.     

Eco-driving key factors that influence fuel consumption in heavy-truck fleets: A Colombian case

This research identifies key variables that influence fuel consumption that might be improved through eco-driving training programs under three circumstances that have been scarcely studied before: (a) heavy- and medium-duty truck fleets, (b) long-distance freight transport, and (c) the Latin American region. Based on statistical analyses that include multivariate regression of operational variables on fuel consumption, the impacts of an eco-driving training campaign were measured by comparing ex ante and ex post data. Operational variables are grouped into driving errors, trip conditions, driver behavior, driver profile, and vehicle attributes.The methodology is applied in a freight fleet with nationwide transport operations located in Colombia, where the steepness of its roads plays an important role in fuel consumption. The fleet, composed of 18 trucks, is equipped with state-of-the-art real-time data logger systems. During four months, 517 trips traveling a total distance of 292,512 km and carrying a total of 10,034 tons were analyzed.The results show a baseline average fuel consumption (FC) of 1.716 liters per ton-100 km. A different logistics performance indicator, which measures FC in liters per ton transported each 100 km, shows an average of 3.115. After the eco-driving campaign, reductions of 6.8% and 5.5% were obtained. Drivers’ experience, driving errors, average speed, and weight-capacity ratio, among others, were found to be highly relevant to FC. In particular, driving errors such as acceleration, braking and speed excesses are the most sensitive to eco-driving training, showing reductions of up to 96% on the average number of events per trip.     

Analysis of three independent real-world driving studies: A data driven and expert analysis approach to determining parameters affecting fuel economy

It is well established that individual variations in driving style have a significant impact on vehicle energy efficiency. The literature shows certain parameters have been linked to good fuel economy, specifically acceleration, throttle use, number of stop/starts and gear change behaviours. The primary aim of this study was to examine what driving parameters are specifically related to good fuel economy using a non-homogeneous extended data set of vehicles and drivers over real-world driving scenarios spanning two countries. The analysis presented in this paper shows how three completely independent studies looking at the same factor (i.e., the influence of driver behaviour on fuel efficiency) can be evaluated, and, despite their notable differences in location, environment, route, vehicle and drivers, can be compared on broadly similar terms. The data from the three studies were analysed in two ways; firstly, using expert analysis and the second a purely data driven approach. The various models and experts concurred that a combination of at least one factor from the each of the categories of vehicle speed, engine speed, acceleration and throttle position were required to accurately predict the impact on fuel economy. The identification of standard deviation of speed as the primary contributing factor to fuel economy, as identified by both the expert and data driven analysis, is also an important finding. Finally, this study has illustrated how various seemingly independent studies can be brought together, analysed as a whole and meaningful conclusions extracted from the combined data set.     

Method for investigating intradriver heterogeneity using vehicle trajectory data: A Dynamic Time Warping approach

After first extending Newell’s car-following model to incorporate time-dependent parameters, this paper describes the Dynamic Time Warping (DTW) algorithm and its application for calibrating this microscopic simulation model by synthesizing driver trajectory data. Using the unique capabilities of the DTW algorithm, this paper attempts to examine driver heterogeneity in car-following behavior, as well as the driver’s heterogeneous situation-dependent behavior within a trip, based on the calibrated time-varying response times and critical jam spacing. The standard DTW algorithm is enhanced to address a number of estimation challenges in this specific application, and a numerical experiment is presented with vehicle trajectory data extracted from the Next Generation Simulation (NGSIM) project for demonstration purposes. The DTW algorithm is shown to be a reasonable method for processing large vehicle trajectory datasets, but requires significant data reduction to produce reasonable results when working with high resolution vehicle trajectory data. Additionally, singularities present an interesting match solution set to potentially help identify changing driver behavior; however, they must be avoided to reduce analysis complexity.     

From behavioral psychology to acceleration modeling: Calibration,validation, and exploration of drivers’ cognitive and safety parameters in a risk-taking environment

We investigate a utility-based approach for driver car-following behavioral modeling while analyzing different aspects of the model characteristics especially in terms of capturing different fundamental diagram regions and safety proxy indices. The adopted model came from an elementary thought where drivers associate subjective utilities for accelerations (i.e. gain in travel times) and subjective dis-utilities for decelerations (i.e. loss in travel time) with a perceived probability of being involved in rear-end collision crashes. Following the testing of the model general structure, the authors translate the corresponding behavioral psychology theory – prospect theory – into an efficient microscopic traffic modeling with more elaborate stochastic characteristics considered in a risk-taking environment.After model formulation, we explore different model disaggregate and aggregate characteristics making sure that fidelity is kept in terms of equilibrium properties. Significant effort is then dedicated to calibrating and validating the model using microscopic trajectory data. A modified genetic algorithm is adopted for this purpose while focusing on capturing inter-driver heterogeneity for each of the parameters. Using the calibration exercise as a starting point, simulation sensitivity analysis is performed to reproduce different fundamental diagram regions and to explore rear-end collisions related properties. In terms of fundamental diagram regions, the model in hand is able to capture traffic breakdowns and different instabilities in the congested region represented by flow-density data points scattering. In terms of incident related measures, the effect of heterogeneity in both psychological factors and execution/perception errors on the accidents number and their distribution is studied. Through sensitivity analysis, correlations between the crash-penalty, the negative coefficient associated with losses in speed, the positive coefficient associated with gains in speed, the driver’s uncertainty, the anticipation time and the reaction time are retrieved. The formulated model offers a better understanding of driving behavior, particularly under extreme/incident conditions.     

Theory of jam-absorption driving

“Can a single car really absorb a traffic jam without making new jams?” In this paper, we focus on this frequently-discussed question, and have succeeded in making a theoretical framework of a driving technique how to absorb a traffic jam by using a minimal microscopic model. Jam-absorption driving comes from Beaty (Beaty, 1998, Beaty, 2013), and it is composed of a sequence of two actions termed the “slow-in” and “fast-out”. The “slow-in” is the action to avoid being captured by a jam and remove it by decelerating and taking a longer headway in advance. The “fast-out” is performed after the “slow-in”, and it is the action to follow the car in front without unnecessary time gaps by accelerating quickly. In our theoretical framework, we have represented the recipe of the actions such as the time–space points and the velocity. Moreover, we have clarified the condition of no secondary jams due to this driving, i.e., the condition that compression and expansion waves caused by this driving meet each other and disappear. Particularly, we have calculated how these waves propagates to the following cars and the point where and when they disappear. Besides, we have analyzed how this point moves in time–space diagrams by varying the timing to start the jam-absorption, and revealed that the pattern of this movement is not constant but changes greatly by the velocity-headway relationships. Furthermore, as a more realistic problem, we have formulated the driving for jam-absorption in two steps of deceleration, which brings rich patterns of collisions among compression and expansion waves.     

Driving by choice or necessity?

From just about all accounts, Americans are driving more than ever, not just to work but to shopping, to school, to soccer practice and band practice, to visit family and friends, and so on. Americans also seem to be complaining more than ever about how much they drive—or, more accurately, how much everyone else drives. However, the available evidence suggests that a notable share of their driving is by choice rather than necessity. Although the distinction between choice and necessity is not always so clear, it is important for policy makers. For necessary trips, planners can explore ways of reducing the need for or length of the trip or ways of enhancing alternatives to driving. For travel by choice, the policy implications are much trickier and touch on basic concepts of freedom of choice. This paper first develops a framework for exploring the boundary between choice and necessity based on a categorization of potential reasons for and sources of “excess driving”, and then uses in-depth one-on-one interviews guided by this framework to characterize patterns of excess driving. This research contributes to a deeper understanding of travel behavior and provides a basis for developing policy proposals directed at reducing the growth in driving.