Analiza rozproszenia uwagi niedoświadczonych kierowców przy użyciu modelu rozmytego - wyniki badań Analysis of the attention distraction of inexperienced drivers using a fuzzy model – research results
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(4) Warsaw University of Technology. ANALYSIS OF THE ATTENTION DISTRACTION OF INEXPERIENCED DRIVERS USING A FUZZY MODEL – RESEARCH RESULTS Manuscript delivered, June 2019. Abstract: Limiting the number and consequences of the traffic accidents is one of the most important goals of the EU policy for the road transport. Despite significant efforts in this area, the targets set for the 2010-2020 decade are unlikely to be achieved. This may be due to, inter alia, the increasing importance of the driver attention distraction as a factor contributing to their occurrence. In order to limit the effects of distraction, attempts are made to develop a method to detect such a state of a driver. The distraction of the driver affects the way he drives the vehicle. The authors in their earlier work conducted a research aimed at developing model for detecting states of distraction of the driver's attention, based on a change in the method of vehicle steering. The developed model uses fuzzy logic to detect distraction. This paper presents the results of this model's operation on a sample of 72 drivers, including 36 inexperienced drivers who were the main object of the tests. Keywords: cognitive load, driving simulators, fuzzy logic. 1. INTRODUCTION Road accidents represent one of the most serious problems of the road transport. Reducing the number and consequences of road accidents is one of the most important goals of the EU policy. It can already be assumed that one of the pillars of the EU policy for the decade 2010-2020 in this respect, i.e. limiting the number of accident victims by half, will not be achieved. One of the reasons for this is the in-crease in the importance of distraction of the driver's attention as a factor contributing to their occurrence. In the USA itself, where the relevant statistics have been conducted for many years, it was pointed out that at least 3,166 people died in 2017 because of distraction of the driver’s attention [12] [42]. In a study by Klauer et al. [26], it was determined that 78% of accidents and 65% of situations close to an accident were caused by the driver's inattention and at the same time 25% of cases were caused by interaction with the devices inside the vehicle (telephone, navigation,.
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(7) Nader. etc.). Engaging cognitive resources in the tasks not related to driving a car usually affects the driver at the operational level, in a short-term (during and immediately after involvement in a given activity). The need to be involved in additional activities is of-ten associated with insufficient attention devoted to the basic task - driving. Drivers, however, often allow the possibility of deterioration of control over the vehicle in order to meet the requirements of the additional task. This may result from various motivational motives, but invariably has a degrading effect on the control of the vehicle and the driving process. The group that is particularly vulnerable due to the negative impact of distraction are inexperienced drivers. It is a group of drivers who have the highest accident rate (according to KGP data [27]). The research indicated that this group is three times more likely to participate in a road accident than a driver with several years of experience. Young drivers are particularly vulnerable to operational and tactical errors because they may not have sufficient skills to control the vehicle, they can misinterpret threats and overestimate their skills [31]. Inexperienced drivers often assess their skills higher than the objective measures indicate. This leads them to engage in additional tasks that are unrelated to driving. An important example of this type of behaviour is the use of mobile phones while driving [6]. Research shows that inexperienced drivers are more likely to engage in visual-manual multimedia handling tasks than more experienced drivers while driving [18] [36]. As a consequence of these facts, this group was selected as the target group of the developed solution. Three main areas related to vehicle motion control can be identified, which are affected by distraction: driving speed and maintaining distance, control in the cross-section of the lane, response time to sudden traffic incidents. For all these areas, the research has repeatedly pointed out to the significant effect of distraction on the vehicle control. However, the factor that causes distraction and plays a significant role may change the observed reactions of the drivers. Changes in driving speed are a parameter used very often, and in terms of changes resulting from the performance of additional tasks, they have been used, among the others, in the studies [1] [14] [15] [41]. In a significant part of the studies [7] [10] [22] [37] [38], there is a clear negative effect on the quality of the driver maintaining speed and distance to the vehicle ahead. The most commonly observed reactions in the study are speed reduction (observed, among the others, in the studies [5] [13] [16] [41]) and increased variability of speed (observed, among the others, in the studies [9] [22] [43]). Despite observing significant differences in the speed control, the use of parameters associated with it in the engineering solutions can be very difficult. The vehicle steering in the cross-section of the lane is characterized by numerous parameters. Typically, the increase in steering variation is considered an unfavourable effect and is treated as a measure of the quality of driving (e.g. in the studies [20] [24] [39]). The effects of distraction are most often observed in the situations of visual distraction of the driver, which is confirmed, among the others, by the research [19] [22] [23] [41]. Parameters related to the vehicle steering in the lane cross-section are applied to the engineering solutions, and the work focuses on detecting undesirable driver states. The reaction time to sudden road accidents is of particular importance for the emergence of the road accidents. The studies have repeatedly confirmed the effect of distracting the driver's attention on extending the driver's reaction time (among the others in the studies.
(8) Analysis of the attention distraction of inexperienced drivers using a fuzzy model – research results. 55. [4] [12] [32] [33] [35] [40]). Despite the effects that are important for the road safety, the longer reaction time cannot be used as an indicator of the driving quality. In order to limit the effects of distraction, attempts are made to develop a method to detect such a driver's state, which was the subject of previous research [29] [30]. In order to limit the effects of distraction, attempts are made to develop a method to detect such a driver's state. For this purpose, different methods were used: Cybernetic Driver Model (CDM), which was developed using unscented Kalman filter [3]; convolutional neural networks were used to monitor visually driver activity in VGG model [34] to detect states that may rise driver distraction; Weibull acceleration failure time model that takes into account reaction time delays [11]. Also, some of the solutions focuses on driver gaze monitoring [2] [18] [25] and distraction detection basing on eye activity. Authors in the presented solution focused on the second area of impact of distraction on the quality of driving and for the selected parameters the model was developed for detecting the state of distraction of the driver's attention, which is an extension to actual state of knowledge. The next part of the work presents the most important elements of the developed model and the results of its verification on the tested driver’s sample.. 2. THE FUZZY MODEL FOR EVALUATING DISTRACTION OF THE DRIVER’S ATTENTION In the model, due to the limited sample of the tested drivers (72 participants of the study), the theory of fuzzy logic was used, which has the so-called ability to generalize knowledge. Learning dataset was obtained during simulator-based experiments where the distracting task (adoption of “Arrow’s task” [16]) was performed in parallel to primary driving task (adoption of 3VPT [4]). Dataset contained all simulation data of the vehicle steering. Experiment design was published in earlier papers performed by the authors [28] [29]. The presented approach is, according to the authors' knowledge, the first of its kind application of fuzzy logic.. 2.1. CHARACTERISTICS OF THE MODEL The assumption was made for the development of the model that all studied processes have a normal theoretical distribution. The Fuzzy Logic Designer tool was used to develop the fuzzy controller in the MATLAB environment. Statistical analysis of the results of the experiment indicated three parameters in which effects resulting from performing a visual and manual distraction task, were observed: standard deviation of the vehicle's position in the lane (POZ), steering wheel reversal rate (SWRR) and time to lane change (TtLC)..
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(11) Nader. All the parameters mentioned above were used as an inputs of the fuzzy controller, while the designed output was the numerically expressed task load level, which assumed values in the range of 0 points up to 120 points (according to the NASA-TLX [21] scale). Figure 1 presents developed fuzzy controller model.. Fig. 1. The fuzzy controller model designed in the Matlab environment Source: own elaboration using Matlab Fuzzy Logic Designer.. The TMW output (Total Mental Workload) of the controller has been characterized by three affiliation functions: not distracted alarm and distracted. The "not distracted" function characterizes the low load status of the driver, the "warn" characterizes the state of the average task load and the "distracted" function characterizes the state of the high task load of the driver.. 2.2. EXPERIMENT CHARACTERISTICS The data obtained from 72 participants were used to verify the model. 36 participants were inexperienced drivers, and 36 were experienced drivers under the age of 30. In the study, the drivers were asked to perform an additional task not related to driving, which served as a distracting element. At the same time, they performed a standardized driving task in a three-vehicle platoon task (3VPT) adapted based on the results of the Driver Workload Metrics project [4]. The distracting task was a visual-manual task and was called the "arrows task". It was developed based on the Engstrom publication [16]. It involved searching for a suitable arrow on the touch device (pointing vertically upwards). Such a single arrow was displayed on the table in a matrix with a size from 3x3 to 6x6, where arrows pointing in other directions were also displayed. The remaining arrows (except for the one searched) were either displayed in one direction (task of matching arrows - further described as e.g. 3x3Z) or each in a different direction (task of various arrows - further described as e.g. 3x3R). This resulted in 8 levels of difficulty of the task. First, the least complicated task variants were performed, and then the level of difficulty was increased. The view of the arrow task being performed is shown in Figure 2..
(12) Analysis of the attention distraction of inexperienced drivers using a fuzzy model – research results. 57. Fig. 2. The "arrow task" carried out during the research scenario Source: own materials.. The distracting task uses the structural effect of distraction on the sense of sight. Such understood effect causes the driver's vision to turn away from the road in order to search for the information relevant for this task. Therefore, it reduces the time of observation of the road, which should negatively affect the quality of driving. A full description of the characteristics of the experiment and the procedures used were published in earlier works performed by the authors [28] [29] [30].. 2.3. RESULTS OF MODEL VERIFICATION The verification conducted was aimed at determining the suitability of the developed model for use in the driver state monitoring systems (DSMS). The results were analyzed in groups of experienced and inexperienced drivers. The change in the average result for both these groups, due to the variant of the task being performed, is shown in Figure 3.. Fig. 3. Average results of the model operation broken down into experienced and inexperienced drivers Source: own data..
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(15) Nader. Average results obtained by inexperienced drivers were higher than those obtained by experienced drivers for each variant of the task being performed. The average difference is 5.97 points, and in different variants has a value from 3.60 points to 8.59 points. It is important, for the results of model verification, that the results show a growing trend as the difficulty level of the task increases. Local decreases in the average cognitive load may be caused by the effect of learning - the tasks followed each other from the easiest to the most difficult one, while a small change in the level of difficulty may not have a significant impact on the driver. It should also be noted that between the 6x6Z and 3x3R tasks there was a break lasting about 40 seconds, which may cause a drop in the parameter value for the experienced drivers. The next element of the verification was the analysis using the TMW threshold value, which was set at 64. Exceeding this value meant qualifying the driver as "distracted". Figure 4 present the results of the analysis for inexperienced and experienced driver groups.. Fig. 4. Participation of drivers classified as "distracted" divided into experienced and inexperienced drivers Source: own data.. The results indicate a greater share of drivers distracted among inexperienced drivers than among experienced drivers. In both groups of drivers, a significant increase in the share of drivers distracted in the tasks 6x6Z and 5x5R can be observed. At the same time, as in the previously described results, one can observe the effect of the task learning effect on the results of the model's operation. Research observations show that drivers at the time of significant task load, which temporarily exceeded their capabilities, chose different strategies to deal with this situation. For example, some drivers reduced their involvement in the performance of the distracting task. Due to this fact, additional verification should be foreseen in the future, taking into account the level of involvement of the driver in the performance of the additional task..
(16) Analysis of the attention distraction of inexperienced drivers using a fuzzy model – research results. 59. 3. CONCLUSIONS The article presents a model for assessing the distraction of the driver made in the MATLAB environment and the results of the verification of this model. The presented approach, according to the authors' knowledge, is the first solution using fuzzy logic to assess the driver's state. The model was verified for inexperienced and experienced drivers. At first, it should be noted that the performed tests bear the hermetic characteristics and the application of the developed solution in the real vehicle requires further development and verification work. The experiment used only one of the possible driving scenarios, characterizing the road situation driving in the column of vehicles. However, the vehicle movement parameters used in the model are not dependent on factors that were regulated during the experiment, so it can be assumed that the results of the model operation should be applicable to other traffic situations. The usefulness of these parameters was also demonstrated in various earlier experiments [17] [22] [23] [41], with a varied nature of the road situation. The changes observed in the experiment confirm the results of previous research, while full verification of the model's operation requires further experiments in this field. The authors plan to conduct such experiments in the future. Another important limitation when interpreting the results is the fact that the limited sample of available experimental data was used. Further work need to be performed to verify and finally validate model usefulness. Such a work is planned by the authors in near future. The results of model verification indicate the possibility of using the model to evaluate the state of the drivers. The detected level of the attention distraction did correlate with the change in the difficulty level of the task despite the visible learning results (no experiments with randomization of the task level were performed). It stands to show the adequacy of the model to the assumed goal. At the same time, the verification method does not allow for dichotomous distinction of the actual driver state, which makes it impossible to perform a full assessment of the system operation. This results both from the complex nature of the phenomenon described, which cannot be assessed in this way, as well as from clear individual differences between drivers. In the authors' opinion, the model has the potential to be applied in vehicles, which may result in using it as a part of a larger driver state assessment system, e.g. using additional signals on the physiological state, etc. To this end, additional work is needed to verify its operation in the wider spectrum of traffic situations. Such works have already been planned. At the same time, large individual differences observed in previous studies indicate that it may be reasonable to use the system personalization mechanisms, which is also the subject of further work planned by the authors.. References 1. Al-Darrab I.A., Khan Z.A., Ishrat S.I.: An experimental study on the effect of mobile phone conversation on drivers’ reaction time in braking response. Journal of Safety Research, 40 (2009), pp. 185–189, 2009. 2. Ahlstrom C., Kircher K., Kircher A.A gaze-based driver distraction warning system and its effect on visual behaviour. IEEE Transactions on Intelligent Transportation Systems 14, no. 2, pp. 965–973, 2013..
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