Wertheim's hypothesis on 'highway hypnosis': empirical evidence from a study on motorway and conventional road driving

An Identification Method for Road Hypnosis Based on the Fusion of Human Life Parameters

A driver in road hypnosis has two different types of characteristics. One is the external characteristics, which are distinct and can be directly observed. The other is internal characteristics, which are indistinctive and cannot be directly observed. The eye movement characteristic, as a distinct external characteristic, is one of the typical characteristics of road hypnosis identification. The electroencephalogram (EEG) characteristic, as an internal feature, is a golden parameter of drivers' life identification. This paper proposes an identification method for road hypnosis based on the fusion of human life parameters. Eye movement data and EEG data are collected through vehicle driving experiments and virtual driving experiments. The collected data are preprocessed with principal component analysis (PCA) and independent component analysis (ICA), respectively. Eye movement data can be trained with a self-attention model (SAM), and the EEG data can be trained with the deep belief network (DBN). The road hypnosis identification model can be constructed by combining the two trained models with the stacking method. Repeated Random Subsampling Cross-Validation (RRSCV) is used to validate models. The results show that road hypnosis can be effectively recognized using the constructed model. This study is of great significance to reveal the essential characteristics and mechanisms of road hypnosis. The effectiveness and accuracy of road hypnosis identification can also be improved through this study.

An Identification Method for Road Hypnosis Based on Human EEG Data

The driver in road hypnosis has not only some external characteristics, but also some internal characteristics. External features have obvious manifestations and can be directly observed. Internal features do not have obvious manifestations and cannot be directly observed. They need to be measured with specific instruments. Electroencephalography (EEG), as an internal feature of drivers, is the golden parameter for drivers' life identification. EEG is of great significance for the identification of road hypnosis. An identification method for road hypnosis based on human EEG data is proposed in this paper. EEG data on drivers in road hypnosis can be collected through vehicle driving experiments and virtual driving experiments. The collected data are preprocessed with the PSD (power spectral density) method, and EEG characteristics are extracted. The neural networks EEGNet, RNN, and LSTM are used to train the road hypnosis identification model. It is shown from the results that the model based on EEGNet has the best performance in terms of identification for road hypnosis, with an accuracy of 93.01%. The effectiveness and accuracy of the identification for road hypnosis are improved in this study. The essential characteristics for road hypnosis are also revealed. This is of great significance for improving the safety level of intelligent vehicles and reducing the number of traffic accidents caused by road hypnosis.

A Recognition Method for Road Hypnosis Based on Physiological Characteristics

Road hypnosis is a state which is easy to appear frequently in monotonous scenes and has a great influence on traffic safety. The effective detection for road hypnosis can improve the intelligent vehicle. In this paper, the simulated experiment and vehicle experiment are designed and carried out to obtain the physiological characteristics data of road hypnosis. A road hypnosis recognition model based on physiological characteristics is proposed. Higher-order spectra are used to preprocess the electrocardiogram (ECG) and electromyography (EMG) data, which can be further fused by principal component analysis (PCA). The Linear Discriminant Analysis (LDA), Quadratic Discriminant Analysis (QDA), and K-Nearest Neighbor (KNN) models are constructed to identify road hypnosis. The proposed model has good identification performance on road hypnosis. It provides more alternative methods and technical support for real-time and accurate identification of road hypnosis. It is of great significance to improve the intelligence and active safety of intelligent vehicles.

Research on Recognition of Road Hypnosis in the Typical Monotonous Scene

Road traffic safety can be influenced by road hypnosis. Accurate detection of the driver's road hypnosis is a very important function urgently required in the driver assistance system. Road hypnosis recurs frequently in a certain period, and it tends to occur in a typical monotonous scene such as a tunnel or a highway. Taking the scene of a tunnel or a highway as a typical example, road hypnosis was studied through simulated driving experiments and vehicle driving experiments. A road hypnosis recognition model based on principal component analysis (PCA) and a long short-term memory network (LSTM) was proposed, where PCA was used to extract various parameters collected by the eye tracker, and the LSTM model was constructed to identify road hypnosis. The accuracy rates of 93.27% and 97.01% in simulated driving experiments and vehicle driving experiments were obtained. The proposed method was compared with k-nearest neighbor (KNN) and random forest (RF). The results showed that the proposed PCA-LSTM model had better performance. This paper provides a novel and convenient method to realize the driver's road hypnosis detection function of the intelligent driver assistance system in practical applications.

Road environment characteristics and fatal crash injury during the rush and non-rush hour periods in the U.S: Model testing and cluster analysis

This study aims to assess the relationship between county-level fatal crash injuries and road environmental characteristics at all times of the day and during the rush and non-rush hour periods. We merged eleven-year (2010 - 2020) data from the Fatality Analysis Reporting System. The outcome variable was the county-level fatal crash injury counts. The predictor variables were measures of road types, junction types and work zone, and weather types. We tested the predictiveness of two nested negative binomial models and adjudged that a nested spatial negative binomial regression model outperformed the non-spatial negative binomial model. The median county crash mortality rates at all times of the day and during the rush and non-rush hour periods were 18.4, 7.7, and 10.4 per 100,000 population, respectively. Fatal crash injury rate ratios were significantly elevated on interstates and highways at all times of the day - rush and non-rush hour periods inclusive. Intersections, driveways, and ramps on highways were associated with elevated fatal crash injury rate ratios. Clusters of high fatal crash injury rates were observed in counties located in Montana, Nevada, Colorado, Kansas, New Mexico, Oklahoma, Texas, Arkansas, Mississippi, Alabama, Georgia, and Nevada. The built and natural road environment factors are associated with county-level fatal crash injuries during the rush and non-rush hour periods. Understanding the association of road environment characteristics and the cluster distribution of fatal crash injuries may inform areas in need of focused intervention.

Reducing passive driver fatigue through a suitable secondary motor task by means of an interactive seating system

OBJECTIVE

The primary objective of the study was to evaluate the effect of a secondary motor task induced by an interactive seating system (IASS) on passive driver fatigue in a monotonous simulated driving task. The effect was compared to that of a state-of-the-art massage seating system (MS), which may reduce monotony through additional tactile stimuli. The secondary objective was to compare the user experience of both systems.

METHOD

The independent variables were three conditions: one with the IASS, another with the MS, and a control without intervention. The study included seven dependent variables in total: a rating of subjective fatigue, three parameters measuring lane keeping ability, and three parameters reflecting fatigue-related eye movements. The duration of the simulator ride was 40 min in each condition. The study included thirty-five subjects.

RESULTS

The assessment of subjective fatigue and lane keeping showed that the use of the IASS resulted in significantly lower passive driver fatigue compared to the massage and control conditions. The alerting effects of the IASS were also reflected by an increased eyelid distance. Frequency and duration of blinks, however, showed no clear patterns of fatigue over time in any of the conditions. Thus, both parameters did not seem be suitable to capture passive driver fatigue in this study. Regarding user experience, the subjects preferred the IASS over the MS as well.

CONCLUSION

The IASS showed a strong potential as an effective measure against passive driver fatigue within monotonous driving situations. The MS, on the other hand, induced no measurable effects.

What Attracts the Driver’s Eye? Attention as a Function of Task and Events

This study explores how drivers of an automated vehicle distribute their attention as a function of environmental events and driving task instructions. Twenty participants were asked to monitor pre-recorded videos of a simulated driving trip while their eye movements were recorded using an eye-tracker. The results showed that eye movements are strongly situation-dependent, with areas of interest (windshield, mirrors, and dashboard) attracting attention when events (e.g., passing vehicles) occurred in those areas. Furthermore, the task instructions provided to participants (i.e., speed monitoring or hazard monitoring) affected their attention distribution in an interpretable manner. It is concluded that eye movements while supervising an automated vehicle are strongly ‘top-down’, i.e., based on an expected value. The results are discussed in the context of the development of driver availability monitoring systems.

The prevalence of alcohol-involved crashes across high and low complexity road environments: Does knowing where drinking drivers crash help explain why they crash?

Objective

Alcohol use has been linked to impairment of cognitive and psychomotor driving skills, yet the extent to which skill impairment contributes to actual crashes is unknown. A reasonable assumption is that some driving situations have higher skill demands than others. We contend that intersections, the presence of other vehicles or moving objects, and work zones are examples of common situations with higher skill demands. Accordingly, if skill deficits are largely responsible for alcohol-involved crashes, crashes involving a drinking driver (versus only sober drivers) should be overrepresented in these driving situations.

Method

Publicly available FARS data from 2010 to 2017 were collected. Fatal crashes were coded as alcohol-involved (1+ driver with a blood alcohol concentration [BAC] ≥ .05 g/dl) or having no impaired driver (BACs = .000). Drug-positive crashes were excluded. Crashes were also coded as involving moving versus stationary objects, occurring at versus away from intersections, being multivehicle versus single vehicle, occurring at or away from work zones.

Results

Across multiple models, controlling for time of day and type of road, alcohol-involved crashes were significantly underrepresented in crashes at intersections, with moving objects, and other vehicles. Most strikingly, alcohol-involved crashes were 24 percentage points more likely to be with a stationary object than a moving object.

Conclusions

No evidence supported the idea that skill reductions are a primary contributor to alcohol-involved crashes. Alternative explanations and limitations are discussed.

An evaluation and comparison of commercial driver sleepiness detection technology: a rapid review

Objective. Sleepiness-related motor vehicle crashes, caused by lack of sleep or driving during night-time hours, often result in serious injury or fatality. Sleepiness detection technology is rapidly emerging as a sleepiness risk mitigation strategy for drivers. Continuous monitoring technologies assess and alert to driver sleepiness in real-time, while fit for duty technologies provide a single assessment of sleepiness state. The aim of this rapid review was to evaluate and compare sleepiness detection technologies in relation to specifications, cost, target consumer group and validity.Approach. We evaluated a range of sleepiness detection technologies suitable for consumer groups ranging from regular drivers in private vehicles through to work-related drivers within large businesses.Main results. Continuous monitoring technologies typically ranged between $100 and $3000 AUD and had ongoing monthly costs for telematics functionality and manager alerts. Fit for duty technologies had either a one-off purchase cost or a monthly subscription cost. Of concern, the majority of commercial continuous monitoring technologies lacked scientific validation. While some technologies had promising findings in terms of their ability to detect and reduce driver sleepiness, further validation work is required. Field studies that evaluate the sensitivity and specificity of technology alerts under conditions that are regularly experienced by drivers are necessary. Additionally, there is a need for longitudinal naturalistic driving studies to determine whether sleepiness detection technologies actually reduce sleepiness-related crashes or near-crashes.Significance. There is an abundance of sleepiness detection technologies on the market, but a majority lacked validation. There is a need for these technologies and their validation to be regulated by a driver safety body. Otherwise, consumers will base their technology choices on cost and features, rather than the ability to save lives.

Eye blink parameters to indicate drowsiness during naturalistic driving in participants with obstructive sleep apnea: A pilot study

OBJECTIVES

To determine whether continuous eye blink measures could identify drowsiness in patients with obstructive sleep apnea (OSA) during a week of naturalistic driving.

DESIGN

Observational study comparing OSA patients and healthy controls.

SETTING

Regular naturalistic driving across one week.

PARTICIPANTS

Fifteen untreated moderate to severe OSA patients and 15 age (± 5 years) and sex (female = 6) matched healthy controls.

MEASUREMENTS

Participants wore an eye blink drowsiness recording device during their regular driving for one week.

RESULTS

During regular driving, the duration of time with no ocular movements (quiescence), was elevated in the OSA group by 43% relative to the control group (mean [95% CI] 0.20[0.17, 0.25] vs 0.14[0.12, 0.18] secs, P = .011). During long drives only, the Johns Drowsiness Scale was also elevated and increased by 62% in the OSA group relative to the control group (1.05 [0.76, 1.33] vs 0.65 [0.36, 0.93], P = .0495). Across all drives, critical drowsiness events (defined by a Johns Drowsiness Scale score ≥2.6) were twice as frequent in the OSA group than the control group (rate ratio [95% CI] =1.93 [1.65, 2.25], P ≤ .001).

CONCLUSIONS

OSA patients were drowsier than healthy controls according to some of the continuous real time eye blink drowsiness measures. The findings of this pilot study suggest that there is potential for eye blink measures to be utilized to assess fitness to drive in OSA patients. Future work should assess larger samples, as well as the relationship of eye blink measures to conventional fitness to drive assessments and crash risk.

The Role of Eye Tracking Technology in Assessing Older Driver Safety

A growing body of literature is focused on the use of eye tracking (ET) technology to understand the association between objective visual parameters and higher order brain processes such as cognition. One of the settings where this principle has found practical utility is in the area of driving safety.

METHODS

We reviewed the literature to identify the changes in ET parameters with older adults and neurodegenerative disease.

RESULTS

This narrative review provides a brief overview of oculomotor system anatomy and physiology, defines common eye movements and tracking variables that are typically studied, explains the most common methods of eye tracking measurements during driving in simulation and in naturalistic settings, and examines the association of impairment in ET parameters with advanced age and neurodegenerative disease.

CONCLUSION

ET technology is becoming less expensive, more portable, easier to use, and readily applicable in a variety of clinical settings. Older adults and especially those with neurodegenerative disease may have impairments in visual search parameters, placing them at risk for motor vehicle crashes. Advanced driver assessment systems are becoming more ubiquitous in newer cars and may significantly reduce crashes related to impaired visual search, distraction, and/or fatigue.

Effects of multitasking and intention-behaviour consistency when facing yellow traffic light uncertainty

We examined the effects of multitasking on resolving response bistability to yellow traffic lights, using the performance metrics of reaction time and stopping frequency. We also examined whether people's actual behaviours, measured by implicit foot pedal responses, differed from their intentions related to these factors, as measured by explicit verbal commands. In a dual-task paradigm, participants responded to random traffic light changes, presented over a static background photograph of an intersection, using either foot pedals or verbal commands, while simultaneously identifying spoken words as either "animals" or "artefacts" via button pressing. The dual-task condition was found to prolong reaction times relative to a single-task condition. In addition, verbal commands were faster than the foot pedal responses, and conservativeness was the same for both types of responses. A second experiment, which provided a more dynamic simulation of the first experiment, confirmed that conservativeness did not differ between verbal commands and foot pedal responses. We conclude that multitasking affects a person's ability to resolve response bistability to yellow traffic lights. If one considers that prolonged reaction times reduce the amount of distance available to safely stop at intersections, this study underscores how multitasking poses a considerable safety risk for drivers approaching a yellow traffic light.

Long-term retention of a divided attention psycho-motor test combining choice reaction test and postural balance test: A preliminary study

Driving in degraded psychophysical conditions, such as under the influence of alcohol or drugs but also in a state of fatigue or drowsiness, is a growing problem. The current roadside tests used for detecting drugs from drivers suffer various limitations, while impairment is subjective and does not necessarily correlate with drug metabolite concentration found in body fluids. This work is a validation step towards the study of feasibility of a novel test conceived to assess psychophysical conditions of individuals performing at-risk activities. Motor gestures, long-term retention and learning phase related to the protocol are analysed in unimpaired subjects. The protocol is a divided attention test, which combines a critical tracking test achieved with postural movements and a visual choice reaction test. Ten healthy subjects participated in a first set of trials and in a second set after about six months. Each session required the carrying out of the test for ten times in order to investigate learning effect and performance over repetitions. In the first set the subjects showed a learning trend up to the third trial, whilst in the second set of trials they showed motor retention. Nevertheless, the overall performance did not significantly improve. Gestures are probably retained due to the type of tasks and the way in which the instructions are conveyed to the subjects. Moreover, motor retention after a short training suggests that the protocol is easy to learn and understand. Implications for roadside test usage and comparison with current tests are also discussed.

Effects of driving time on microsaccadic dynamics

Driver fatigue is a common cause of car accidents. Thus, the objective detection of driver fatigue is a first step toward the effective management of fatigue-related traffic accidents. Here, we investigated the effects of driving time, a common inducer of driver fatigue, on the dynamics of fixational eye movements. Participants drove for 2 h in a virtual driving environment while we recorded their eye movements. Microsaccade velocities decreased with driving time, suggesting a potential effect of fatigue on microsaccades during driving.

A strategically timed verbal task improves performance and neurophysiological alertness during fatiguing drives

OBJECTIVE

The objective of this study was to investigate if a verbal task can improve alertness and if performance changes are associated with changes in alertness as measured by EEG.

BACKGROUND

Previous research has shown that a secondary task can improve performance on a short, monotonous drive. The current work extends this by examining longer, fatiguing drives. The study also uses EEG to confirm that improved driving performance is concurrent with improved driver alertness.

METHOD

A 90-min, monotonous simulator drive was used to place drivers in a fatigued state. Four secondary tasks were used: no verbal task, continuous verbal task, late verbal task, and a passive radio task.

RESULTS

When engaged in a secondary verbal task at the end of the drive, drivers showed improved lane-keeping performance and had improvements in neurophysiological measures of alertness.

CONCLUSION

A strategically timed concurrent task can improve performance even for fatiguing drives.

APPLICATION

Secondary-task countermeasures may prove useful for enhancing driving performance across a range of driving conditions.

A simulator study on the impact of traffic calming measures in urban areas on driving behavior and workload

This study examined the impact of traffic calming measures (TCM) on major roads in rural and urban areas. More specifically we investigated the effect of gate constructions located at the entrance of the urban area and horizontal curves within the urban area on driving behavior and workload. Forty-six participants completed a 34km test-drive on a driving simulator with eight thoroughfare configurations, i.e., 2 (curves: present, absent)×2 (gates: present, absent)×2 (peripheral detection task (PDT): present, absent) in a within-subject design. PDT performance (mean response time (RT) and hit rate) indicated that drivers experienced the road outside the urban area as cognitively less demanding relative to the more complex road environment inside the urban area. Whereas curves induced a speed reduction that was sustained throughout the entire urban area, variability of acceleration/deceleration and lateral position were increased. In addition, PDT performance indicated higher workload when curves were present (versus absent). Gate constructions locally reduced speed (i.e., shortly before and after the entrance) and slightly increased variability of acceleration/deceleration and lateral position nearby the entrance. However, the effects on SDL-A/D and SDLP are too small to expect traffic safety problems. It can be concluded that both curves and gate constructions can improve traffic safety. Notwithstanding, the decision to implement these measures will depend on contextual factors such as whether the road serves a traffic-, rather than a residential function.

Sleep-related collisions in United Arab Emirates

INTRODUCTION

Road traffic collisions (RTC) are a major health problem in UAE. Sleep as a contributing factor to RTC is not well-studied in the Middle East.

OBJECTIVE

We aimed to study to the proportion of RTC caused by sleep behind the wheel and the factors contributing to sleep related collisions (SRC).

METHODS

Data of all hospitalized drivers who were involved in RTC in Al-Ain city were prospectively collected during the period of April 2006-October 2007. Variables studied included, driver's demographic data, time, date, location, mechanism of collision, speed at collision and whether sleepiness was a contributing factor as reported by the drivers. A direct logistic regression model was performed to define factors related to sleep while driving.

RESULTS

444 drivers (92% males) were involved in RTC during the study period. Sleepiness of drivers was a contributing factor in 5%. Most of the drivers experiencing SRC (79%) reported speeds of 100km/h or more during the collision. SRC was strongly over-represented during the month of Ramadan (42%) and in driving on highways (83%). A logistic regression model has shown that driving during the lunar month of Ramadan (p<0.0001, OR=6.36) and on highways (p=0.037, OR=3.75) were the most significant independent contributors to increasing the odds of SRC.

CONCLUSION

Sleep is an important contributing factor to RTC in UAE. Drivers should be advised to discontinue driving when feeling sleepy especially during the lunar month of Ramadan and while driving on highways.

A study of factors affecting highway accident rates using the random-parameters tobit model

A large body of previous literature has used a variety of count-data modeling techniques to study factors that affect the frequency of highway accidents over some time period on roadway segments of a specified length. An alternative approach to this problem views vehicle accident rates (accidents per mile driven) directly instead of their frequencies. Viewing the problem as continuous data instead of count data creates a problem in that roadway segments that do not have any observed accidents over the identified time period create continuous data that are left-censored at zero. Past research has appropriately applied a tobit regression model to address this censoring problem, but this research has been limited in accounting for unobserved heterogeneity because it has been assumed that the parameter estimates are fixed over roadway-segment observations. Using 9-year data from urban interstates in Indiana, this paper employs a random-parameters tobit regression to account for unobserved heterogeneity in the study of motor-vehicle accident rates. The empirical results show that the random-parameters tobit model outperforms its fixed-parameters counterpart and has the potential to provide a fuller understanding of the factors determining accident rates on specific roadway segments.

Driving performance impairments due to hypovigilance on monotonous roads

Drivers' ability to react to unpredictable events deteriorates when exposed to highly predictable and uneventful driving tasks. Highway design reduces the driving task mainly to a lane-keeping manoeuvre. Such a task is monotonous, providing little stimulation and this contributes to crashes due to inattention. Research has shown that driver's hypovigilance can be assessed with EEG measurements and that driving performance is impaired during prolonged monotonous driving tasks. This paper aims to show that two dimensions of monotony - namely road design and road side variability - decrease vigilance and impair driving performance. This is the first study correlating hypovigilance and driver performance in varied monotonous conditions, particularly on a short time scale (a few seconds). We induced vigilance decrement as assessed with an EEG during a monotonous driving simulator experiment. Road monotony was varied through both road design and road side variability. The driver's decrease in vigilance occurred due to both road design and road scenery monotony and almost independently of the driver's sensation seeking level. Such impairment was also correlated to observable measurements from the driver, the car and the environment. During periods of hypovigilance, the driving performance impairment affected lane positioning, time to lane crossing, blink frequency, heart rate variability and non-specific electrodermal response rates. This work lays the foundation for the development of an in-vehicle device preventing hypovigilance crashes on monotonous roads.

Real-time performance modelling of a Sustained Attention to Response Task

Vigilance declines when exposed to highly predictable and uneventful tasks. Monotonous tasks provide little cognitive and motor stimulation and contribute to human errors. This paper aims to model and detect vigilance decline in real time through participants' reaction times during a monotonous task. A laboratory-based experiment adapting the Sustained Attention to Response Task (SART) is conducted to quantify the effect of monotony on overall performance. Relevant parameters are then used to build a model detecting hypovigilance throughout the experiment. The accuracy of different mathematical models is compared to detect in real time - minute by minute - the lapses in vigilance during the task. It is shown that monotonous tasks can lead to an average decline in performance of 45%. Furthermore, vigilance modelling enables the detection of vigilance decline through reaction times with an accuracy of 72% and a 29% false alarm rate. Bayesian models are identified as a better model to detect lapses in vigilance as compared with neural networks and generalised linear mixed models. This modelling could be used as a framework to detect vigilance decline of any human performing monotonous tasks. STATEMENT OF RELEVANCE: Existing research on monotony is largely entangled with endogenous factors such as sleep deprivation, fatigue and circadian rhythm. This paper uses a Bayesian model to assess the effects of a monotonous task on vigilance in real time. It is shown that the negative effects of monotony on the ability to sustain attention can be mathematically modelled and predicted in real time using surrogate measures, such as reaction times. This allows the modelling of vigilance fluctuations.

Tobit analysis of vehicle accident rates on interstate highways

There has been an abundance of research that has used Poisson models and its variants (negative binomial and zero-inflated models) to improve our understanding of the factors that affect accident frequencies on roadway segments. This study explores the application of an alternate method, tobit regression, by viewing vehicle accident rates directly (instead of frequencies) as a continuous variable that is left-censored at zero. Using data from vehicle accidents on Indiana interstates, the estimation results show that many factors relating to pavement condition, roadway geometrics and traffic characteristics significantly affect vehicle accident rates.

Event-related potentials and secondary task performance during simulated driving

Inattention and distraction account for a substantial number of traffic accidents. Therefore, we examined the impact of secondary task performance (an auditory oddball task) on a primary driving task (lane keeping). Twenty healthy participants performed two 20-min tests in the Divided Attention Steering Simulator (DASS). The visual secondary task of the DASS was replaced by an auditory oddball task to allow recording of brain activity. The driving task and the secondary (distracting) oddball task were presented in isolation and simultaneously, to assess their mutual interference. In addition to performance measures (lane keeping in the primary driving task and reaction speed in the secondary oddball task), brain activity, i.e. event-related potentials (ERPs), was recorded. Performance parameters on the driving test and the secondary oddball task did not differ between performance in isolation and simultaneous performance. However, when both tasks were performed simultaneously, reaction time variability increased in the secondary oddball task. Analysis of brain activity indicated that ERP amplitude (P3a amplitude) related to the secondary task, was significantly reduced when the task was performed simultaneously with the driving test. This study shows that when performing a simple secondary task during driving, performance of the driving task and this secondary task are both unaffected. However, analysis of brain activity shows reduced cortical processing of irrelevant, potentially distracting stimuli from the secondary task during driving.

Working in light vehicles--a review and conceptual model for occupational health and safety

Occupational light vehicle (OLV) use is the leading cause of work related traumatic deaths in Westernised countries. Previous research has focused primarily on narrow contexts of OLV-use such as corporate fleet vehicles. We have proposed a comprehensive systems model for OLV-use to provide a framework for identifying research needs and proposing policy and practice interventions. This model presents the worker as the locus of injury at the centre of work- and road-related determinants of injury. Using this model, we reviewed existing knowledge and found most studies focused only on company car drivers, neglecting OLV-users in non-traditional employment arrangements and those using other vehicle types. Environmental exposures, work design factors and risk and protective factors for the wider OLV-user population are inadequately researched. Neither road- nor work-related policy appropriately addresses OLV-use, and population surveillance relies largely on inadequate workers compensation insurance data. This review demonstrates that there are significant gaps in understanding the problem of OLV-use and a need for further research integrating public health, insurance and road safety responses. The model provides a framework for understanding the theory of OLV-use OHS and guidance for urgently needed intervention research, policy and practice.