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.

Technology acceptance comparison between on-road dynamic message sign and on-board human machine interface for connected vehicle-based variable speed limit in fog area

Purpose Connected vehicle-based variable speed limit (CV-VSL) systems in fog area use multi-source detection data to indicate drivers to make uniform change in speed when low visibility conditions suddenly occur. The purpose of the speed limit is to make the driver's driving behavior more consistent, so as to improve traffic safety and relieve traffic congestion. The on-road dynamic message sign (DMS) and on-board human–machine interface (HMI) are two types of warning technologies for CV-VSL systems. This study aims to analyze drivers’ acceptance of the two types of warning technologies in fog area and its influencing factors. Design/methodology/approach This study developed DMS and on-board HMI for the CV-VSL system in fog area on a driving simulator. The DMS and on-board HMI provided the driver with weather and speed limit information. In all, 38 participants participated in the experiment and completed questionnaires on drivers’ basic information, perceived usefulness and ease of use of the CV-VSL systems. Technology acceptance model (TAM) was developed to evaluate the drivers’ acceptance of CV-VSL systems. A variance analysis method was used to study the influencing factors of drivers’ acceptance including drivers’ characteristics, technology types and fog density. Findings The results showed that drivers’ acceptance of on-road DMS was significantly higher than that of on-board HMI. The fog density had no significant effect on drivers’ acceptance of on-road DMS or on-board HMI. Drivers’ gender, age, driving year and driving personality were associated with the acceptance of the two CV-VSL technologies differently. This study is beneficial to the functional improvement of on-road DMS, on-board HMI and their market prospects. Originality/value Previous studies have been conducted to evaluate the effectiveness of CV-VSL systems. However, there were rare studies focused on the drivers’ attitude toward using which was also called as acceptance of the CV-VSL systems. Therefore, this research calculated the drivers’ acceptance of two normally used CV-VSL systems including on-road DMS and on-board HMI using TAM. Furthermore, variance analysis was conducted to explore whether the factors such as drivers’ characteristics (gender, age, driving year and driving personality), technology types and fog density affected the drivers’ acceptance of the CV-VSL systems.

The effect of fog on detection of driving hazards after dark

The presence of fog leads to an increase in road traffic accidents. An experiment was carried out using a scale model to investigate how the detection of hazards in peripheral vision was affected by changes in luminance (0.1 cd/m2 and 1.0 cd/m2 road surface luminance), scotopic/photopic (S/P) ratio (0.65 and 1.40) and fog density (none, thin and thick). Two hazards were used, a road surface obstacle and lane change of another vehicle. Increasing luminance, and reducing from thick to thin fog, led to significant increase in detection rate and a reduction in reaction time, for both types of hazard. The effect of a change in S/P ratio was significant only when measuring detection of the surface obstacle using reaction times, under the thick fog, with an increase in S/P ratio leading to a shorter reaction time.

Impacts of Fog Characteristics, Forward Illumination, and Warning Beacon Intensity Distribution on Roadway Hazard Visibility

Warning beacons are critical for the safety of transportation, construction, and utility workers. These devices need to produce sufficient luminous intensity to be visible without creating glare to drivers. Published standards for the photometric performance of warning beacons do not address their performance in conditions of reduced visibility such as fog. Under such conditions light emitted in directions other than toward approaching drivers can create scattered light that makes workers and other hazards less visible. Simulations of visibility of hazards under varying conditions of fog density, forward vehicle lighting, warning beacon luminous intensity, and intensity distribution were performed to assess their impacts on visual performance by drivers. Each of these factors can influence the ability of drivers to detect and identify workers and hazards along the roadway in work zones. Based on the results, it would be reasonable to specify maximum limits on the luminous intensity of warning beacons in directions that are unlikely to be seen by drivers along the roadway, limits which are not included in published performance specifications.