Driver distraction by digital billboards? Structural equation modeling based on naturalistic driving study data: A case study of Iran

Introduction: Digital billboards (DBs) are a competing factor for attracting drivers’ attention; evidence shows that DBs may cause crashes and vehicle conflicts because they catch drivers’ attention. Because of the complexity of a system that includes road conditions, driver features, and environmental factors, it is simply not possible to identify relationships between these factors. Thus, the present study was conducted to provide a well-organized procedure to analyze the effects of DBs on drivers’ behavior and measure factors responsible for drivers’ distraction in Babol, Iran, as a case study. Method: Corresponding data were collected through a Naturalistic Driving Study (NDS) of 78 participants when facing DBs (1,326 samples). These data were analyzed by applying structural equation modeling (SEM) to concurrently recognize relationships between endogenous and exogenous variables. Human, environmental, and road factors were determined as exogenous latent variables in a model to evaluate their influences on drivers’ distraction as an endogenous variable. Results: The results showed that road, environmental, and human factors reciprocally interact with drivers’ distraction, although the estimated coefficient of human factors was more of a factor than that of the other groups. Furthermore, younger drivers, beginner drivers, and male drivers (as human factors); night and unclear weather like a rainy day (as environmental factors); and installing DBs at complicated traffic positions like near-intersections (as road factors) were determined to be the main factors that increase the possibility of drivers’ distraction. Finally, model assessment was suggested using the goodness-of-fit indices.     

The detection of drowsiness using a driver monitoring system

Abstract

Objective: Drowsiness is a major cause of driver impairment leading to crashes and fatalities. Research has established the ability to detect drowsiness with various kinds of sensors. We studied drowsy driving in a high-fidelity driving simulator and evaluated the ability of an automotive production-ready driver monitoring system (DMS) to detect drowsy driving. Additionally, this feature was compared to and combined with signals from vehicle-based sensors.

Methods: The National Advanced Driving Simulator was used to expose drivers to long, monotonous drives. Twenty participants drove for about 4?h in the simulator between 10 p.m. and 2 a.m. They were allowed to use cruise control and traffic was sparse and semirandom, with both slower- and faster-moving vehicles. Observational ratings of drowsiness (ORDs) were used as the ground truth for drowsiness, and several dependent measures were calculated from vehicle and DMS signals. Drowsiness classification models were created that used only vehicle signals, only driver monitoring signals, and a combination of the 2 sources.

Results: The model that used DMS signals performed better than the one that used only vehicle signals; however, the combination of the two performed the best. The models were effective at discriminating low levels of drowsiness from moderate to severe drowsiness; however, they were not effective at telling the difference between moderate and severe levels. A binary model that lumped drowsiness into 2 classes had an area under the receiver operating characteristic (ROC) curve of 0.897.

Conclusions: Blinks and saccades have been shown to be predictive of microsleeps; however, it may be that detection of microsleeps and lane departures occurs too late. Therefore, it is encouraging that the model was able to distinguish mild from moderate drowsy driving. The use of automation may make vehicle-based signals useless for characterizing driver states, providing further motivation for a DMS. Future improvements in impairment detection systems may be expected through a combination of improved hardware, physiological measures from unobtrusive sensors and wearables, and the intelligent integration of environmental variables like time of day and time on task.     

First evaluation of the possibility of testing for drugged driving using exhaled breath sampling

Objective: Driving under the influence of psychoactive drugs causes an increased risk for accidents. In combating this, sobriety tests at the roadside are common practice in most countries. Sampling of blood and urine for forensic investigation cannot be done at the roadside and poses practical problems associated with costs and time. An alternative specimen for roadside testing is therefore warranted and the aerosol particles in exhaled breath are one such alternative.

Methods: The present study investigated how the exhaled breath sample compared with the routine legal investigations of blood and urine collected from suspects of drugged driving at 2 locations in Sweden. Exhaled breath was collected using a simple filter collection device and analyzed with state-of-the-art mass spectrometry technique.

Results: The total number of cases used for this investigation was 67. In 54 of these cases (81%) the results regarding a positive or negative drug test result agreed and in 13 they disagreed. Out of these, the report from the forensic investigation of blood/urine was negative in 21 cases. In 6 of these, analytical findings were made in exhaled breath and these cases were dominated by the detection of amphetamine. In 7 cases a positive drug test from the forensic investigation was not observed in the breath sample and these cases were dominated by detection of tetrahydrocannabinol in blood. In total, 45 samples were positive with breath testing and the number of positives with established forensic methods was 46.

Conclusion: The promising results from this study provide support to exhaled breath as a viable specimen for testing of drugged driving. The rapid, easy, and convenient sampling procedure offers the possibility to collect a drug test specimen at the roadside. The analytical investigation must be done in a laboratory at present because of the need for a highly sensitive instrument, which is already in use in forensic laboratories. The analytical work is not more challenging than for blood or oral fluid and should not cause an increase in cost. However, more studies need to be done before exhaled breath drug testing can be applied routinely for drugged driving investigation.     

Hazard perception of stroke drivers in a video-based Japanese hazard perception task

Objective: Hazard perception (HP) is the ability to identify a hazardous situation while driving. Though HP has been well studied among neurologically intact populations, little is known about the HP of neurologically impaired populations (in this study, stroke patients). The purpose of this study is, first, to investigate the HP of stroke patients and, second, to verify the effect of lesion side (right or left hemisphere) on HP, from the viewpoint of hazard types.

Methods: Sixty-seven neurologically intact age-matched older drivers and 63 stroke patients with valid driver’s licenses conducted a video-based Japanese HP task. Participants were asked to indicate the hazardous events in the driving scenario. These events were classified into 3 types: (1) behavioral prediction hazards (BP), which are those where the cause is visible before it becomes a hazard; (2) environmental prediction hazards (EP), which are those where the ultimate hazard may be hidden from view; and (3) dividing and focusing attention hazards (DF), which are those where there is more than one potential hazard to monitor on approach．Participants also took part in the Trail Making Test (TMT) to evaluate visual information processing speed.

Results: The results showed that the number of responses was significantly fewer for stroke patients than for age-matched drivers for all hazard types (P < .001), and this difference was not affected by lesion side (P > .05). It was also found that stroke patients showed a slower response time than age-matched drivers only for BP (P < .001). The lesion side did not affect response latency (P > .05). Results of the TMT revealed that age-matched drivers completed the task significantly faster than stroke patients (P < .001) and that neither TMT-A nor TMT-B differentiated between patients with left hemisphere damage and patients with right hemisphere damage (P > .05).

Conclusions: Firstly, HP in stroke patients is low compared to age-matched drivers. Secondly, even if stroke patients notice hazards, their response may be delayed in a BP situation, due to a slower visual information processing speed. Thirdly, the lesion side does not appear to affect HP.     

Market basket analysis of safety at active highway-railroad grade crossings

IntroductionData from the Federal Railroad Administration show high numbers of incidents at the approximately 210,446 highway-railroad grade crossings across the United States. One cause for this unsettling trend is the problem of drivers stopping within the dynamic envelope zone (DEZ) of the train while in queue. This research seeks to study DEZ stopping behavior at highway-railroad grade crossings by evaluating regulatory signage and further analyze variables that may affect this behavior. Method: A comparative safety analysis is undertaken to evaluate the effectiveness of the standard “Do Not Stop on Tracks” (R8-8) sign by using percentage change calculations and chi-squared tests. The study then conducts a market basket analysis (MBA) to extrapolate on these results and to identify underlying factors associated with observed driver behavior using variables influenced by visibility, perception, and maneuverability. Results: Rather low reductions in safety violations resulted from the R8-8 installation, including a 2.2% reduction in DEZ stopping behavior and only a slight 3.7% increase in compliance. The results of the MBA identified associations that affirmed assumptions about driver behavior, while other associations were not as direct but altogether helped broaden the understanding of interactions at grade crossings. Conclusions: This study concluded that the R8-8 had a positive but minimal effect on driver behavior at the grade crossings. The MBA successfully demonstrated its value by providing further insight on the safety analysis and by increasing the number of variables that can be analyzed simultaneously. The methodology offers the scientific community an innovative approach to analyzing driver behavior. Practical Applications: The results identified important variables for developing preventive measures, which will ultimately help reduce safety violations at grade crossings. The MBA can provide practical insight for railroad safety professionals and transportation engineers when determining problematic intersections and can be used to improve the education on grade crossing interactions.     

Near crash characteristics among risky drivers using the SHRP2 naturalistic driving study

Problem: Previous research have focused extensively on crashes, however near crashes provide additional data on driver errors leading to critical events as well as evasive maneuvers employed to avoid crashes. The Strategic Highway Research Program 2 (SHRP2) Naturalistic Driving Study contains extensive data on real world driving and offers a reliable methodology to study near crashes. The current study utilized the SHRP2 database to compare the rate and characteristics associated with near crashes among risky drivers. Methods: A subset from the SHRP2 database consisting of 4,818 near crashes for teen (16–19 yrs), young adult (20–24 yrs), adult (35–54 yrs), and older (70+ yrs) drivers was used. Near crashes were classified into seven incident types: rear-end, road departure, intersection, head-on, side-swipe, pedestrian/cyclist, and animal. Near crash rates, incident type, secondary tasks, and evasive maneuvers were compared across age groups. For rear-end near crashes, near crash severity, max deceleration, and time-to-collision at braking were compared across age. Results: Near crash rates significantly decreased with increasing age (p < 0.05). Young drivers exhibited greater rear-end (p < 0.05) and road departure (p < 0.05) near crashes compared to adult and older drivers. Intersection near crashes were the most common incident type among older drivers. Evasive maneuver type did not significantly vary across age groups. Near crashes exhibited a longer time-to-collision at braking (p < 0.01) compared to crashes. Summary: These data demonstrate increased total near crash rates among young drivers relative to adult and older drivers. Prevalence of specific near crash types also differed across age groups. Timely execution of evasive maneuvers was a distinguishing factor between crashes or near crashes. Practical Applications: These data can be used to develop more targeted driver training programs and help OEMs optimize ADAS to address the most common errors exhibited by risky drivers.     

Improvement of driver active interventions during automated driving by displaying trajectory pointers—A driving simulator study

Abstract

Objective: The handover of vehicle control from automated to manual operation is a critical aspect of interaction between drivers and automated driving systems (ADS). In some cases, it is possible that the ADS may fail to detect an object. In this event, the driver must be aware of the situation and resume control of the vehicle without assistance from the system. Consequently, the driver must fulfill the following 2 main roles while driving: (1) monitor the vehicle trajectory and surrounding traffic environment and (2) actively take over vehicle control if the driver identifies a potential issue along the trajectory. An effective human–machine interface (HMI) is required that enables the driver to fulfill these roles. This article proposes an HMI that constantly indicates the future position of the vehicle.

Methods: This research used the Toyota Dynamic Driving Simulator to evaluate the effect of the proposed HMI and compares the proposed HMI with an HMI that notifies the driver when the vehicle trajectory changes. A total of 48 test subjects were divided into 2 groups of 24: One group used the HMI that constantly indicated the future position of the vehicle and the other group used the HMI that provided information when the vehicle trajectory changed.

The following instructions were given to the test subjects: (1) to not hold the steering wheel and to allow the vehicle to drive itself, (2) to constantly monitor the surrounding traffic environment because the functions of the ADS are limited, and (3) to take over driving if necessary.

The driving simulator experiments were composed of an initial 10-min acclimatization period and a 10-min evaluation period. Approximately 10?min after the start of the evaluation period, a scenario occurred in which the ADS failed to detect an object on the vehicle trajectory, potentially resulting in a collision if the driver did not actively take over control and manually avoid the object.

Results: The collision avoidance rate of the HMI that constantly indicated the future position of the vehicle was higher than that of the HMI that notified the driver of trajectory changes, χ² = 6.38, P < .05. The steering wheel hands-on and steering override timings were also faster with the proposed HMI (t test; P < .05).

Conclusions: This research confirmed that constantly indicating the position of the vehicle several seconds in the future facilitates active driver intervention when an ADS is in operation.     

Driving difficulties as reported by older drivers with mild cognitive impairment and without neurological impairment

Objective: Considerable evidence indicates that medical conditions prevalent among older individuals lead to impairments in visual, cognitive, or psychomotor functions needed to drive safely. The purpose of this study was to explore the factors determining driving difficulties as seen from the viewpoint of 30 older drivers with mild cognitive impairment (MCI) and 30 age-matched controls without cognitive impairment.

Methods: Perceptions of driving difficulties from both groups were examined using data from an extensive questionnaire. Samples of drivers diagnosed with MCI and age-matched controls were asked to report the frequency with which they experienced driving difficulties due to functional deficits and knowledge of new traffic rules and traffic signs.

Results: The analysis revealed that 2 factors underlie MCI perceptions of driving difficulties, representing (1) difficulties associated with late detection combined with slowed response to relevant targets in the peripheral field of view and (2) difficulties associated with divided attention between tasks requiring switching from automatic to conscious processing particularly of long duration. The analysis for healthy controls revealed 3 factors representing (1) difficulties in estimating speed and distance of approaching vehicles in complex (attention-dividing) high-information-load conditions; (2) difficulties in moving head, neck, and feet; and (3) difficulties in switching from automatic responses to needing to use cognitive processing in new or unexpected situations.

Conclusions: Though both group analyses show difficulties with switching from automatic to decision making, the difficulties are different. For the control group, the difficulty in switching involves switching in new or unexpected situations associated with high-information-load conditions, whereas this switching difficulty for the MCI group is associated with divided attention between easier tasks requiring switching. These findings underline the ability of older drivers (with MCI and without cognitive impairment) to indicate probable impairments in various driving skills. The patterns of difficulties perceived by the MCI group and the age-matched healthy control group are indicative of demanding driving situations that may merit special attention for road designers and road safety engineers. They may also be considered in the design of older drivers’ fitness to drive evaluations, training programs, and/or vehicle technologies that provide for older driver assistance.     

Household hazardous waste quantification,characterization and management in China’s cities: A case study of Suzhou

A four-stage systematic tracking survey of 240 households was conducted from the summer of 2011 to the spring of 2012 in a Chinese city of Suzhou to determine the characteristics of household hazardous waste (HHW) generated by the city. Factor analysis and a regression model were used to study the major driving forces of HHW generation. The results indicate that the rate of HHW generation was 6.16 (0.16–31.74, 95% CI) g/person/day, which accounted for 2.23% of the household solid waste stream. The major waste categories contributing to total HHW were home cleaning products (21.33%), medicines (17.67%) and personal care products (15.19%). Packaging and containers (one-way) and products (single-use) accounted for over 80% of total HHW generation, implying a considerable potential to mitigate HHW generation by changing the packaging design and materials used by manufacturing enterprises. Strong correlations were observed between HHW generation (g/person/day) and the driving forces group of “household structure” and “consumer preferences” (among which the educational level of the household financial manager has the greatest impact). Furthermore, the HHW generation stream in Suzhou suggested the influence of another set of variables, such as local customs and culture, consumption patterns, and urban residential life-style. This study emphasizes that HHW should be categorized at its source (residential households) as an important step toward controlling the HHW hazards of Chinese cities.     

基于HRV量化的高速公路出口匝道区域驾驶负荷特性研究

为提高高速公路主线出口匝道区域的行车安全性,基于HRV指标量化分析该区域的驾驶负荷特性和影响因素.分析了交通标志设计方案对主线出口匝道区域交通安全影响特性.采用HRV指标对驾驶负荷进行量化分析,同时采用不同信息量的交通标志对驾驶负荷进行定量加载,以汉十高速公路沿线匝道区为场景,设计并开展了主线出口匝道区域驾驶负荷的实车道路试验,采集不同试验场景下多个驾驶人的生理指标和驾驶特性.结果表明,交通标志信息量可以作为认知负荷加载的方式.从2 km到500m到出口位置的预告标志,表征驾驶负荷的HRV指标呈先下降后上升的趋势.驾驶人在通过主线出口匝道区域不同路段时的行驶车速的变化趋势与驾驶负荷强度的变化趋势具有较好的一致性.