Analysis of Freeway Safety Influencing Factors on Driving Workload and Performance Based on the Gray Correlation Method

Abstract

Human-related factors are a crucial inducement of traffic accidents. Understanding the influence of freeway environments on the driving behavior and workload experienced by drivers has been demonstrated to be of primary importance for improving traffic safety. To study the effect of alignment, traffic flow, and sign information on drivers’ mental workload and behavior, 16 scenarios were constructed using the orthogonal design method, and simulated driving experiments were carried out with 45 participants. During driving, indicators such as the mean and standard deviation of vehicle speed and lane departure were collected, and the NASA-TLX questionnaire was adopted to measure workload. Analysis of variance results indicated that the radius of the horizontal curve, gradient, flow, and sign information level have a significant influence on drivers’ workload and speed keeping ability. In addition, the horizontal curve radius has a significant effect on lane keeping ability. The importance of safety influencing factors on driving workload and performance was quantitatively ranked by integrating the trend of Deng’s correlation degree, comprehensive correlation degree, and similar correlation degree, whose weight was calculated using the entropy method. Traffic sign information was found to have the greatest impact on workload. In terms of driving performance, traffic volume has the greatest influence on the mean and standard deviation of vehicle speed, followed by the amount of sign information. Lane departure is most affected by the radius of the horizontal curve. These findings provide guidance for freeway traffic safety regulation, including workload control and road facility optimization.

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1. Introduction

Statistics of road traffic accidents show that 80% of accidents and 65% of dangerous driving conditions are directly or indirectly caused by human factors, and that the driver has no obvious illegal and subjective errors in 41% of these accidents [1]. In 1988, John [2] proposed the concept of mental workload, also known as mental load or cognitive load, and noted that the cognitive process of information will consume the memory resources of the brain and cause a certain workload. Extended to the field of transportation, driving workload is considered to be a measure of mental resources occupied by a series of activities related to driving tasks, such as receiving external stimuli, rapidly processing information and forming a clear judgment [3]. Mental workload can be defined as the ratio between the capacity of the information processing system needed to correctly perform the task and the amount of available attentional resources [4]. From the driver’s point of view, every driving event provokes a specific level of workload depending on its complexity and the road environment, such as road design, road layout, and traffic flow [5].

Mental workload is a multifaceted concept, and the measures available for the assessment of workload can be grouped into three categories: subjective, physiological, and performance. Subjective measures rely on the assumption that workload effectively experienced by consulted drivers is the most accurate judgment, which is collected after the task without interference to the driving. One of the most common self-report questionnaires is the NASA-Task Load Index (NASA-TLX) [6]. Physiological indexes can be realized continuously through online evaluation of driving workload based on the real-time observation through recording medical device that comprises blood pressure, pulse, heart rate, skin temperature, galvanic skin, EEG, and other indicators [7]. Performance measures refer to drivers’ operation behavior as an alternative indicator of mental load, which potentially reflects the state of stress. Driving behavior measurements include not only the analysis of the primary task indicator but also the investigation of secondary activities [8]. In general, the primary task measurement mainly focuses on the driver’s operating characteristics for completing driving tasks in specific scenarios. In previous simulation driving research, indicators such as vehicle speed, reaction time, and the number of driver mistakes are often used to measure performance. In field tests, characteristics such as vehicle trajectory, steering wheel, accelerator, and brake pedal control are considered [9]. Secondary task measurement is often used when the driver needs to complete multiple tasks. There are two main paradigms: task loaded and subordinate task. Neng-Chao et al. [10] proposed quantifying direction sign information via information entropy theory and used different signs to load driving workload, which was verified to be effective. Baldauf et al. [11] studied the feasibility of perception time as a driving workload evaluation index. Experiments show that the more complex the driving environment, the longer the driver’s reaction time to peripheral tasks, and the higher the workload experienced.

Driving performance can not only be used as an evaluation index of workload [12] but also interact with driving workload, because the perception and information transmission of the road environment runs throughout the driving process. To quantify the relationship between driving workload and behavior, Ryu and Myung [13] proposed a single-resource model. When the task demand increases, the central nervous system increases the resource supply so that the task can be better completed. When all psychological events share the same cognitive resources, each individual has a limited ability to process information. Based on the single-resource model, Wiberg et al. [14] believe that there are different types of resources, such as visual channels, auditory channels, information processing, and response execution. If several tasks need to use the same channel resources, the workload will increase. Consistent with the Yerkes-Dodson law, Zokaei et al. [15] tracked drivers’ physiological response and behavior under different workload levels and found that to maintain the best driving performance the workload should not be too high or too low.

Although driving workload and driving performance are affected by the individual differences, strategies, and emotions of drivers [16], the impact of the external traffic environment, which includes road alignment, flow, traffic facilities, etc., is of greater concern to scholars. Among these factors, road geometry undoubtedly plays a key role. Complex road alignments, such as curves or slopes, have been shown to demand more driver attention [17]. As the uncertainty of the road ahead increases due to insufficient viewing distance, drivers must allocate more attention resources to collecting information and decision making, which results in an increased driving workload [18]. Tight horizontal radius curves associated with abrupt grade changes, as well as compound curves, were identified as a threat to the controllability of cars and can contribute to severe crashes [19]. The driver must adjust the vehicle speed and lane offset to adapt to the alignment changes and even use corner cutting action or continuous pedal control to regulate the engine’s output power and braking force to maintain driving stability [20]. Considering that pedal operation on steep downhill sections can easily lead to an increase in mental workload, Hu et al. [21] proposed designing the definition standard of long and steep sections based on driving demand to design long and vertical slope sections from the driving needs to define standards, which is conducive to improve the driving performance. In contrast, few geometric changes combined with repetitive and stimulus-free environments create a monotonous driving experience that can lead to mind wandering and task disengagement and subsequently affect drivers’ performance [22].

It is widely known that there are limitations to the amount of information humans can process; the more information displayed to a driver at one time, the more difficult it is for the driver to accurately process all of the information [23]. Studies have shown that drivers’ recognition time increases with increasing sign information, and when the sign information reaches a certain threshold, the recognition time will increase significantly [24]. Multiboard signs present information more dispersedly, which requires the drivers to search for longer periods of time to find the target, which undoubtedly increases the task processing demand [25]. In addition to a longer reaction time, a complex visual environment will lead to a higher workload and lower driving speed, as shown in simulated driving conditions [26]. According to the comprehensive efficiency of the cognitive response of road signs, the number of place name thresholds of highway direction signs should be 6 to 7 [25, 27].

Traffic density is an important indicator of traffic complexity. With an increase in traffic flow, drivers’ workload shows a linear upward trend until the level of medium traffic flow. When lane change occurs in front of the driver’s field of vision, the driving workload increases; the closer the distance, the greater the workload [28]. Similarly, Fitzpatrick [29] found that compared with free driving, traffic congestion will lead to an increased driving workload based on an analysis of drivers’ attention and reaction time to the deceleration of the car in front. In some special road sections, such as ramps, vehicle interleaving conflict increases drivers’ lane offset and deteriorates lane keeping capacity, resulting in increased driving load [30]. Meanwhile, an increase in the number of heavy trucks will lead to an increase in the psychological load of adjacent drivers and an increase in safe distances between vehicles [31].

Previous studies have shown that during driving, drivers’ situational awareness, decision making, and vehicle operating capacity are limited [14]. Higher driving demands were found to increase subjective workload levels and impair performance, particularly through behaviors associated with attention [32]. The task capability interface model [33] poses that the loss of control may occur when the demand of the driving task is greater than the ability of the driver because driver attention is probably detached from context driving tasks, such as situational awareness, which results in perception, decision making, and operational errors. Conversely, drivers who are in a state of low workload for long periods of time are prone to drowsiness or distraction. Both high and low levels of mental workload have been shown to lead to inaccurate perceptions, low levels of attention, and insufficient time for accurate information processing, which are the main causes of road collisions [34]. Therefore, it is of great significance to clarify the influence of driving environment factors on mental workload and performance to reduce traffic accidents and improve driving safety.

Road alignment, sign information, and traffic flow are the most common components of freeways, and they can interact and create complex traffic situations. Although the impact of the external road environment on drivers has been widely considered by scholars, there are few studies comparing the influencing factors. Currently, it is difficult to determine how and which factors profoundly affect driving workload and behavior. Therefore, the purpose of this study was to study the influence of various road environmental factors on drivers’ psychology and behavior on the basis of previous research [35]. In current study, the L₁₆(4⁵) orthogonal experimental design was used to select representative points from the overall experiment, which reduced the number of experiments and resulted in a small data sample, and the reflected information was inaccurate and incomplete, with gray characteristics. Meanwhile, in view of the disadvantage that the L₁₆(4⁵) orthogonal test cannot analyze the interaction of various factors, the gray correlation method has been proposed to quantitatively evaluate the influence of the horizontal curve radius, longitudinal slope, traffic flow, and sign information on driving workload and performance, which is suitable for the analysis of uncertainty problems with the characteristics of small sample size, irregular data, lack of information, and insufficient experience and has been widely used in the analysis of influencing factors in various fields [36].

2. Materials and Methods

The supplementary experiment was carried out on the basis of a previous study [35]. The number of subjects increased from 11 to 45 in this research. More details of the experimental design can be found in a previous study.

2.1. Experiment Design and Procedure

2.1.1. Apparatus

The driving simulation platform developed by the Intelligent Transportation Systems Research Center of Wuhan University of Technology was used to conduct the experiment and collect the data, which were composed of an instrumented vehicle and UC-win/road software, as shown in Figure 1.

[figure omitted; refer to PDF]

The mean value and standard deviation of lane deviation under different test scenarios are shown in Figure 4. There were significant differences in the mean and standard deviation of lane departure under each scenario (Brown Forsythe and Welch corrected $p<0.001$). Although the vehicle speeds in sharp curve sections (such as scenes 1, 7, 9, and 11) are significantly reduced, the lane offset is relatively high. In addition to the mean value of lane offset increasing, the standard deviation of lane offset is also relatively high. However, the fluctuation range of the standard deviation of lane offset is large, especially in the 9^th and 7^th test sections, which indicates that subjects show different lane keeping abilities in specific test sections.

[figure omitted; refer to PDF]

According to a ranking of the correlation degree of each influencing factor, the mean and standard deviation of lane offset are found to most affected by the radius of curve, followed by the amount of sign information (see Figure 7). Dijksterhuis et al. [44] determined that the standard deviation of the lateral position (SDLP) may show a downward trend after additional efforts were mobilized to serve the steering task when the demand for lateral control increased during driving. However, a curved road design increases the difficulty of vehicle control, which requires drivers to adopt more precise pedal and steering control to adapt to changes in curvature. When the demand exceeds the range that can be compensated by the degree of effort, the lane keeping ability decreases. Therefore, the curve is still the focus of lane lateral control optimization and sideslip and rollover accident prevention.

[figure omitted; refer to PDF]

In conclusion, this work proposes a comprehensive evaluation based on the entropy weight method, which realizes the quantification and ranking of workload and driving performance influencing factors. The findings clarify that factors should be the focus of workload and driving behavior analysis. This can not only provide a reference for the selection of index weights for constructing a comprehensive evaluation model of traffic safety considering people-vehicles-roads-environment but also provide guidance for freeway alignment design, sign optimization, and driving workload regulation.

4.3. Relationship between Driving Workload and Performance

It has been demonstrated that an increase in road context complexity can lead to impaired performance due to an increase in mental workload and could cause unsuitable maneuvers and even road accidents [38]. In line with the results of Figures 3 and 4, which indicated that in complex scenes, such as the 9^th section, not only does the driver experience the highest workload but also the average and amplitude of the variance of speed and lane deviation reach the maximum. However, the increased complexity of the environment may not always lead to increased driving workload or driving behavior damage. When drivers are engaged in secondary tasks, to manage workload and control driving risk, they will take strategies of interrupting, delaying, and resuming secondary tasks [45]. This may trigger drivers to adopt a compensation strategy to reduce the driving workload. For example, to obtain more time to process information for a foggy vehicle, both speed and acceleration are decreased, and the following distance of the vehicle is increased, an intentional strategy taken by the driver [23]. Fitch et al. [41] found that from a single task to multiple tasks, although the subjective workload score increased, the standard deviation of the lateral position (SDLP) decreased, which suggests that an increase in workload may improve driving performance. Similarly, it indicated that longitudinal and lateral performance parameters remain or even improve according to the task [46]. In this paper, it is found that the speed of the sharp curve section with a small horizontal curve radius (such as scenes 1, 7, 9, and 11) was significantly reduced, as shown in Figure 3, which potentially indicates that the deceleration compensation strategy has been adopted.

However, the relationship between workload and driving behavior is very complex. A large number of simulated and field driving experiments show that complex driving contexts trigger drivers to adopt compensation strategies [47]. However, it remains unclear when to take compensation behavior, the characteristics of compensation behavior, and the interaction mechanism between workload and driving behavior, which will be the focus of the author’s follow-up research.

5. Conclusion

Through orthogonal experimental design, a simulated driving experiment with 16 test scenarios was carried out, and the influence of the radius, slope, traffic flow, and sign information factors on driving workload and performance was studied. The results of one-way ANOVA showed that there were significant differences in drivers’ workload and performance under different test scenarios. In the 9^th test section, drivers not only showed a higher subjective workload score but also showed the characteristics of large speed fluctuation and unstable lane maintenance. This is a result of the 9^th test section being a sharp curve and steep slope section with a large traffic volume and too much road name information on signboards. Drivers not only have a high demand for information perception and processing but also require complex vehicle control on sharp curves and steep slope sections. Although the control strategy of reducing vehicle speed is adopted, some drivers still experience deteriorated driving performance. Multivariate analysis of variance of the orthogonal test indicated that all of the influencing factors have significant effects on the driver’s workload and speed keeping ability, but driver’s lane keeping ability was only significantly affected by the horizontal curve radius. The comprehensive evaluation of the correlation degree using the entropy weight method demonstrated that the amount of sign information has the greatest impact on the subjective workload, the traffic flow has the greatest influence on the mean and standard deviation of vehicle speed, and the radius of curve has the greatest effect on the mean and standard deviation of lane offset.

Note that since the research object is a freeway, it has the characteristics of full access control and a relatively simple traffic composition. Variable workloads caused by uncertain environments, such as traffic events, and their impact on driving behavior are not considered. Therefore, the established correlation degree model has a specific scope of application. For further research, we could introduce a comprehensive evaluation method such as the human factors analysis and classification system (HFACS) [48] to analyze the influencing factors from a systematic perspective, considering humans, vehicles, the environment, and management.

Acknowledgments

The authors acknowledge the support from the National Nature Science Foundation of China (51775396), the National Nature Science Foundation of Guangxi Province (2019JJB160047), and Hubei Province Science Fund for Distinguished Young Scholars (2020CFA081).

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