Effects of mobile phone use on driving performance in a multiresource workload scenario

The effects of distraction on younger drivers: A neurophysiological perspective

Distracted driving remains a significant cause of traffic accidents globally, including in Australia. However, many younger drivers still admit to using a phone while driving. A simulated driving study investigated the neurophysiological effects of visual, auditory, and higher-order cognitive (i.e., requiring the use of executive functions) distraction on young drivers. In total, 24 young adults aged 18-25 years completed four 8 min simulated driving sessions while concurrently engaging in various distractor tasks. Neurophysiological arousal was measured via EEG. Additionally, subjective workload and objective driving performance were assessed. Frontal beta and gamma power exhibited their highest levels during tasks involving higher-order cognitive and visual demands. The higher-order cognitive condition was rated as the most mentally demanding. In comparison, the visual condition had the most significant impact on both the standard deviation of speed and standard deviation of lateral positioning. This study has significant implications for all road users, particularly those aged 18-25 years, and it reinforces the importance of not using a phone while driving.

Evaluation of the visual-manual resources required to perform calling and navigation tasks in conventional mode with a portable phone and in full- touch mode with an embedded system

This study investigates the differences in a driver's visual-manual behaviour when performing secondary tasks while driving under the full-touch mode (FTM) and the conventional mode (CM). To this end, 30 participants were recruited to perform secondary tasks while driving two vehicles equipped with different HMI system interaction modes. The results show that compared to the CM, in the FTM, fewer visual-manual resources are required to perform the calling task, but for the navigation task, this requirement is higher. Additionally, in both modes, the driver exhibited self-regulation visual-manual behaviour when performing secondary tasks as the driving speed increased. However, the effect of the driving speed on visual-manual behaviour was greater in the FTM than in the CM. The main limitation of this study is that the effect of the difference between the two experimental vehicles on the findings was not considered, however, this does not affect the generalisation of the findings. Practitioner summary: Potential applications of this study include improving drivers' knowledge about the effect of performing secondary tasks in different modes on driving safety, and this study also provides useful insights human-machine co-driving systems to develop user-friendly control strategies and for automotive companies to improve the full-touch interactive mode for automotive companies.

Analysis of mobile phone use engagement during naturalistic driving through explainable imbalanced machine learning

While driver distraction remains an issue in modernized societies, technological advancements in data collection, storage and analysis provide the means for deeper insights of this complex phenomenon. In this research, factors influencing when driver distraction through mobile phone use occurs during naturalistic driving are investigated. Naturalistic data from a 6-stage, 230-driver experiment are exploited, in which drivers installed a non-intrusive driving recording application in their devices and conducted their trips normally across a 21-month timespan, coupled with corresponding questionnaire data. The various experiment stages involved providing progressively more behavioral feedback to drivers while continuing to record them. Subsequently, supervised Machine Learning XGBoost algorithms were employed to model the contributions of naturalistic driving and questionnaire features to the decision to engage mobile phone use. Mobile phone use percentages were heavily skewed towards zero, therefore imbalanced ML with a minority-oversampling approach in a binary format was employed. To increase the explainability offered by the algorithm, SHAP values were calculated for the informative features. Results indicate that the decision of drivers to use a mobile while driving is governed by a number of complex, non-linear relationships. Total trip distance is the most significant predictor variable by a wide margin, with mean SHAP values of 0.79 towards affecting the model decisions for the probability of mobile phone use of each driver. However, other variables influence the final predictions as well, such as the number of tickets in the last three years (m.SHAP = 0.30), declared mobile phone use (m.SHAP = 0.26), the amount and variety of provided feedback (m.SHAP = 0.17) (i.e. experiment phase number) and family member numbers (m.SHAP = 0.09) decrease the probability of using a mobile phone while driving. Conversely, increases in driver experience (m.SHAP = 0.22), driver age (m.SHAP = 0.11), engine capacity (m.SHAP = 0.11) and total kilometers driven annually (m.SHAP = 0.08) increase the probability of using a mobile phone in naturalistic driving conditions. SHAP dependency plots reveal non-linear effects present in almost all variables. Fuel consumption had a particularly strong non-linear effect, as higher values of this variable lead to both higher and lower probability of drivers using a mobile phone, deviating from the safer average. Legislation, campaigns and enforcement measures can be restructured to take advantage of gains margins in terms of understanding and predicting driver distraction behavior, as explored in the present study.

The Effects of Dynamic Complexity on Drivers' Secondary Task Scanning Behavior under a Car-Following Scenario

The user interface of vehicle interaction systems has become increasingly complex in recent years, which makes these devices important factors that contribute to accidents. Therefore, it is necessary to study the impact of dynamic complexity on the carrying capacity of secondary tasks under different traffic scenarios. First, we selected vehicle speed and vehicle spacing as influencing factors in carrying out secondary tasks. Then, the average single scanning time, total scanning time, and scanning times were selected as evaluation criteria, based on the theories of cognitive psychology. Lastly, we used a driving simulator to conduct an experiment under a car-following scenario and collect data on scanning behavior by an eye tracker, to evaluate the performance of the secondary task. The results show that the relationship between the total scanning time, scanning times, and the vehicle speed can be expressed by an exponential model, the relationship between the above two indicators and the vehicle spacing can be expressed by a logarithmic model, and the relationship with the total number of icons can be expressed by a linear model. Combined with the above relationships and the evaluation criteria for driving secondary tasks, the maximum number of icons at different vehicle speeds and vehicle spacings can be calculated to reduce the likelihood of accidents caused by attention overload.

Targeting Smartphone Use While Driving: Drivers’ Reactions to Different Types of Safety Messages

Only a few previous studies analyzed the effectiveness of road safety messages targeting smartphone use while driving and only several of them used messages from an ongoing road safety campaign. Thus, contributing to the field, this study aimed at testing the effectiveness of two types of social messages (threat appeal and threat appeal together with safe behavior role modelling) targeting smartphone use while driving. Ninety-three drivers were randomly assigned to two experimental (n1 = 26; n2 = 37) and one control (n = 29) groups. Each experimental group was presented with one 30 s length video message to reduce or stop smartphone use while driving. Messages differed in terms of threat appeal and modelling of safe behavior. The control group was presented with a 30 s length video clip showing neutral driving related content. The results revealed that threat appeals (alone or together with a safe role model) resulted in less positive emotions when compared to the control group’s reported emotional reactions. The message with threat appeal only also resulted in more negative emotions compared to the control group. With regards to behavioral intentions, road safety messages used in this study had minor effectiveness: the threat appeal message reduced the intentions to use smartphones while driving, only when previous behavior has been controlled. In sum, messages targeting smartphone use while driving were effective at least to some extent in changing drivers’ emotions and intentions not to be involved in targeted behavior, but the effect was minor and threat appeal only showed higher effectiveness.

From phone use to speeding and driving under influence: Identifying clusters of driving risk behaviors as an opportunity for targeted interventions

Identifying the profile of risky behaviors among drivers is central to propose effective interventions. Due to the multidimensional and overlapping aspects of risky driving behaviors, cluster analysis can provide additional insights in order to identify specific subgroups of risk. This study aimed to identify clusters of driving risk behavior (DRB) among car drivers, and to verify intra-cluster differences concerning clinical and sociodemographic variables. We approached a total of 12,231 drivers and we included 6392 car drivers. A cluster algorithm was used to identify groups of car drivers in relation to the DRB: driving without a seat belt (SB), exceeding the speed limit (SPD), using a cell phone while driving (CELL), and driving after drinking alcohol (DUI). The algorithm classified drivers within five different DRB profiles. In cluster 1 (20.1%), subjects with a history of CELL. In cluster 2 (41.4%), drivers presented no DRB. In cluster 3 (9.3%), all drivers presented SPD. In cluster 4 (12.5%), drivers presented all DRB. In cluster 5 (16.6%), all drivers presented DUI. Clusters with DUI-related offenses (4 and 5) comprised more men (81.9 and 78.8%, respectively) than the overall sample (63.4%), with more binge drinking (50.9 and 45.7%) and drug use in the previous year (13.5 and 8.6%). Cluster 1 had a high years of education (14.4 ± 3.4) and the highest personal income (Md = 3000 IQR [2000-5000]). Cluster 2 had older drivers (46.6 ± 15), and fewer bingers (10.9%). Cluster 4 had the youngest drivers (34.4 ± 11.4) of all groups. Besides reinforcing previous literature data, our study identified five unprecedented clusters with different profiles of drivers regarding DRB. We identified an original and heterogeneous group of drivers with only CELL misuse, as well as other significant differences among clusters. Hence, our findings show that targeted interventions must be developed for each subgroup in order to effectively produce safe behavior in traffic.

A Scoping Study on Driver’s Perspective of Distracting Factors

Distracting activities while driving are common and can result in errors that threaten road users’ safety. The main objectives of this study were to investigate drivers’ perspectives of the factors contributing to distraction, determine the relative rank of types of distractions, recognize the road factors and environmental effects that make distractions more dangerous, and identify the most effective measures to reduce driver distractions. A survey was conducted to assess Jordanian drivers’ experiences with distracted driving, and what solutions they believed could be implemented to solve the problems. The study’s outcomes revealed that drivers perceive visual distractions as the most dangerous, followed by cognitive, manual, and auditory distractions, respectively. It was also found that “mobile phone texting or dialing” was ranked the top most dangerous visual and manual distracting factor. “Baby is crying or kids are fighting in the back seat” was perceived by all demographic groups as the riskiest auditory factor. Regarding cognitive distraction, four factors were perceived as the most serious, of which “Baby is crying”, “Driving while angry or sad or agitated”, “Talking on a cell phone—even a hands-free one” and “Conversing with passengers” were determined to be the top four distracting factors. The results also revealed that drivers believe that “laws and enforcement” is the most effective measure to reduce distractions while driving.