Determination of driver’s reaction time in expert studies of road traffic accidents using software and hardware complex

Determination of driver’s reaction time in expert studies of road traffic accidents using software and hardware complex

Available online at www.sciencedirect.com ScienceDirect Available online at www.sciencedirect.com Transportation Research Procedia 00 (2019) 000–000...

659KB Sizes 0 Downloads 6 Views

Available online at www.sciencedirect.com

ScienceDirect

Available online at www.sciencedirect.com Transportation Research Procedia 00 (2019) 000–000 Available online at www.sciencedirect.com

ScienceDirect ScienceDirect

www.elsevier.com/locate/procedia

Transportation Research Procedia 50 (2020) 538–544 Transportation Research Procedia 00 (2019) 000–000

www.elsevier.com/locate/procedia

XIV International Conference 2020 SPbGASU “Organization and safety of traffic in large cities”

Determination of driver’s reaction time in expert studies of road traffic Conference accidents2020 using software and hardware XIV International SPbGASU “Organization and safety ofcomplex traffic in large cities” Nickolay Podoprigora*, Polina Stepina, Viktor Dobromirov, Jurij Kotikov Determination of driver’s reaction time in expert studies of road Saint Petersburg State University of Architecture and Civil Engineering, 4 Vtoraja Krasnoarmejskaja St., Saint Petersburg, 190005, Russia traffic accidents using software and hardware complex Abstract

Nickolay Podoprigora*, Polina Stepina, Viktor Dobromirov, Jurij Kotikov

Saint Petersburg State University of Architecture and Civil Engineering, 4 Vtoraja Krasnoarmejskaja St., Saint Petersburg, 190005, Russia

The article addresses the psychophysiological state of vehicle drivers, analyzed in expert studies of road traffic accidents (RTAs). The purpose of this article is to publish the results of research on reaction time and visual perception of drivers under the influence of alcohol. The authors experimentally obtained the values of simple reaction time of drivers able to work as usual and drivers in Abstract a state of alcoholic intoxication (0.3‰, 0.5‰), as well as the values of the volume of their visual perception. The influence of different degrees of alcohol intoxication on the driver reaction time, which affects the ability to timely prevent accidents, is derived The article addresses the psychophysiological state of vehicle drivers, analyzed in expert studies of road traffic accidents (RTAs). and scientifically justified. The purpose of this article is to publish the results of research on reaction time and visual perception of drivers under the influence Keywords: traffic road traffic accident; driver reaction time; state of intoxication; expert evaluation. of alcohol. authors experimentally obtainedB.V. the values of simple reaction time of drivers able to work as usual and drivers in © 2020 TheThe Authors. Published by ELSEVIER an alcoholic open access article under the CC BY-NC-ND (https://creativecommons.org/licenses/by-nc-nd/4.0) aThis stateis of intoxication (0.3‰, 0.5‰), as well license as the values of the volume of their visual perception. The influence of Peer-review underofresponsibility of the scientific committee of the XIVwhich International Conference SPbGASU “Organization and different degrees alcohol intoxication on the driver reaction time, affects the ability to2020 timely prevent accidents, is derived 1. Introduction safety of traffic in justified. large cities” and scientifically Keywords: traffic road traffic accident; driver reaction time; state of intoxication; expert evaluation.

A human reaction is a subconscious process, which is most likely to manifest itself in emergencies. All human feelings and emotions are a kind of human reaction that can be measured by using modern measuring equipment, which is now widely used for the assurance of traffic and environmental safety as well as reconstruction of road traffic 1. Introduction accidents (Brylev et al. 2018, Danilov et al. 2018, 2020, Evtiukov et al. 2018a, 2018b, Ginzburg et al. 2017, Kerimov et al. 2018, Kuraksin et al. 2017, 2017b, Marusin Abliazov 2019, A 2017, humanKurakina reaction et is al. a subconscious process, whichMarusin is most 2017a, likely to manifest itselfand in emergencies. AllMarusin human et al. 2018, 2020,are Repin et al.of2018, Safiullin et that al. 2016, 2018, 2019, Shemyakin and Kuraksin 2016, Soo et al. feelings and2019, emotions a kind human reaction can be measured by using modern measuring equipment, 2020, al. 2019). human of reaction is the output parameter of such measurements.of road traffic which Vorozheikin is now widelyetused for theThe assurance traffic time and environmental safety as well as reconstruction It is known thatetthe time is et a certain fixed time intervaletthat a person is exposed to a Kerimov stimulus accidents (Brylev al.reaction 2018, Danilov al. 2018, 2020, Evtiukov al. begins 2018a,when 2018b, Ginzburg et al. 2017, andal.ends with a response. a road environment, the differentiated value of theMarusin reactionand time is the only measure of et 2017, Kurakina et al.In 2018, Kuraksin et al. 2017, Marusin 2017a, 2017b, Abliazov 2019, Marusin theal.psychophysiological qualities a driver. Practical expert2018, studies show that this and parameter is 2016, determined et 2018, 2019, 2020, Repin et al.of2018, Safiullin et al. 2016, 2019, Shemyakin Kuraksin Soo etnot al. experimentally but,etasal. a rule, byThe using table reaction values obtained in the 1970s (Podoprigora et measurements. al. 2017, 2018). Obviously, 2020, Vorozheikin 2019). human time is the output parameter of such It is known that the reaction time is a certain fixed time interval that begins when a person is exposed to a stimulus and ends with a response. In a road environment, the differentiated value of the reaction time is the only measure of the psychophysiological qualities of a driver. Practical expert studies show that this parameter is determined not experimentally but, asTel.: a rule, by using table values obtained in the 1970s (Podoprigora et al. 2017, 2018). Obviously, * Corresponding author. +7 906-242-27-55. E-mail address: [email protected]

2352-1465 © 2020 Nickolay Podoprigora, Polina Stepina, Viktor Dobromirov, Jurij Kotikov. Published by ELSEVIER B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0) * Corresponding author. Tel.: +7 Peer-review under responsibility of 906-242-27-55. the scientific committee of the XIV International Conference 2020 SPbGASU “Organization and safety of E-mail address: traffic in large cities”[email protected] 2352-1465 © © 2020 2020Nickolay The Authors. Published byStepina, ELSEVIER 2352-1465 Podoprigora, Polina ViktorB.V. Dobromirov, Jurij Kotikov. Published by ELSEVIER B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0) This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0) Peer-review under responsibility of the scientific committee of the XIV International Conference 2020 SPbGASU “Organization and Peer-review underinresponsibility safety of traffic large cities”of the scientific committee of the XIV International Conference 2020 SPbGASU “Organization and safety of traffic in large cities” 10.1016/j.trpro.2020.10.064

2

Nickolay Podoprigora et al. / Transportation Research Procedia 50 (2020) 538–544 Nickolay Podoprigora, Polina Stepina, Viktor Dobromirov, Jurij Kotikov / Transportation Research Procedia 00 (2019) 000–000

539

it is unacceptable to use such values in RTA reconstruction and expert evaluation. The result of selecting the value of this parameter can influence the expert’s conclusion significantly when determining the driver’s guilt/innocence as well as the technical possibility of preventing the accident. Therefore, it is required to pay due attention to the study of the driver reaction time. 2. Theoretical studies The human factor is a key one in most RTAs. Violations of Road Traffic Regulations (RTR), including when driving under the influence of alcohol, remain one of the most common problems worldwide (Kravchenko and Oleshchenko 2017, Seliverstov et al. 2017). Experts distinguish three stages of alcohol intoxication: light (from 0.5 to 1.5‰); medium (from 1.5 to 2.5‰); heavy (from 2.5 to 3.0‰). It is known that the state of alcohol intoxication is pathological, voluntarily chosen, and characterized by both neurological and psychophysiological disorders of the nervous system, provoked by the action of ethyl alcohol (Plawecki et al. 2018). Psychophysiological disorders pose the main problem for road users since, being in a state of alcohol intoxication, road users not only fail to notice the emerging danger when driving in the road network (RN) but also overestimate their ability to drive a vehicle (Kurakina et al. 2018, Pushkarev et al. 2018). The Annex to Article 12.8 of the current Administrative Offences Code of the Russian Federation (CAO RF) sets the allowed total measurement error of 0.16 mg per liter of ethyl alcohol in exhaled air, which is approximately equal to 0.36‰ of alcohol in the blood. Thus, when driving a motor vehicle, the above-mentioned blood alcohol content is allowed, since the above-mentioned degrees of driver’s intoxication do not have values from 0 to 0.5‰. The conducted analysis showed that studies on the influence of blood alcohol content in the amount of less than 0.5‰ on the actions of a driver have not been previously carried out, and the above value is accepted as the lower permissible limit in the applicable Russian regulatory documentation. Thus, the authors needed to obtain real values of the time of driver’s simple and complex reactions to determine the quality of the driver’s perception of the road environment at different degrees of alcohol intoxication. 3. Calculations To solve the task, the authors used the PAKPF-02 software and hardware complex of the psychophysiological parameters of drivers. Its technical capabilities allow us to determine the exact values of the driver reaction time, as well as analyze the features of the driver’s psychophysiology and psychomotor skills, which are necessary for expert studies of RTAs. The PAKPF-02 software and hardware complex includes original methods that make it possible to evaluate the professional qualities of a driver. Following the developed algorithm (Fig. 1), the team of authors from the Saint Petersburg State University of Architecture and Civil Engineering conducted studies on reactions of people able to work as usual and people at different stages of alcohol intoxication.

540

Nickolay Podoprigora et al. / Transportation Procedia 50 (2020) 538–544 Nickolay Podoprigora, Polina Stepina, Viktor Dobromirov, Jurij Kotikov Research / Transportation Research Procedia 00 (2019) 000–000

3

Fig. 1. Testing algorithm.

Experimental studies for each person of a particular category were conducted in the following states: • able to work as usual; • in the state of alcohol intoxication, 0.3‰ (i.e. 0.3 g of ethyl alcohol per 1 liter of blood — mg/l or up to 0.15 mg per 1 liter of the air exhaled by the driver); • in the state of alcohol intoxication, 0.5‰ (i.e. 0.5 mg/l or up to 0.225 mg per 1 liter of exhaled air). According to equation 1, the driver’s reaction time indicators shown in Table 1 were calculated.

T1 =TGS + TDM + TR

(1)

where T1 — the driver’s reaction time, s; TGS — the time of gaze shifting to the object, s; TDM — the decision-making time, s; TR — the simple reaction time, s. Table 1 shows the values of the driver’s simple reaction time when a person is able to work and when a person is in the state of alcohol intoxication (0.3‰, 0.5‰). The sample was generated according to the age criterion with an interval of five years and by the sex of the persons tested.

4

Nickolay Podoprigora et al. / Transportation Research Procedia 50 (2020) 538–544 Nickolay Podoprigora, Polina Stepina, Viktor Dobromirov, Jurij Kotikov / Transportation Research Procedia 00 (2019) 000–000

541

Table 1. Driver’s reaction time. Age categories, years

Sex

18–20 21–25 26–30 31–35 36–40 41–45 46–50 51–60

Able to work

In the state of alcohol intoxication

as usual

0.3‰

0.5‰

m

0.67

0.86

0.89

f

0.74

0.84

0.88

m

0.67

0.86

0.90

f

0.69

0.91

0.97

m

0.69

0.80

0.83

f

0.69

0.84

0.87

m

0.69

0.89

0.91

f

0.78

0.81

0.86

m

0.79

0.89

0.92

f

0.79

0.90

1.03

m

0.79

1.03

1.18

f

0.77

0.99

1.06

m

0.83

1.09

1.15

f

0.85

1.17

1.19

m

0.87

1.09

1.16

f

0.95

1.21

1.27

By analyzing the obtained results, it is possible to conclude that the state of alcohol intoxication in 0.3‰ (0.3 g of ethyl alcohol per 1 liter of blood — mg/l or up to 0.15 mg per 1 liter of exhaled air) as negatively affects the driver’s perception of the road situation when driving, as the state of alcohol intoxication in 0.5‰. It is worth noting that at night, the driver’s reaction time increases by an average of 20–30% compared to the obtained data (Del Valle and Sucha 2019, Leung and Starmer 2005). During the experiments, the average values of the volume of visual perception by the drivers tested, presented in Table 2, were obtained. Table 2. Human visual perception. Age categories, years 18–20 21–25 26–30 31–35 36–40 41–45 46–50

Sex

Able to work as usual

In the state of alcohol intoxication 0.3‰

0.5‰

*

82*

m

85

84

f

81*

77*

75*

m

88

84

*

81*

f

86

80*

79*

m

87

84

*

82*

f

82*

77*

75*

m

89

86

84*

f

84*

76*

73*

m

90

87

84*

f

79*

75*

73*

m

78

76

*

70*

f

76*

74*

69*

m

75

72

*

67*

f

72*

68*

62*

*

*

542

Nickolay Podoprigora et al. / Transportation Research Procedia 50 (2020) 538–544 Nickolay Podoprigora, Polina Stepina, Viktor Dobromirov, Jurij Kotikov / Transportation Research Procedia 00 (2019) 000–000

51–60

m

71*

70*

68*

f

69

65

60*

*

*

5

An analysis of the obtained results revealed that not all age categories have the values of the volume of visual perception corresponding to the norm, even when they are able to work as usual. Even at the permissible minor level of alcohol intoxication, this indicator changes, which means a decrease in the ability to perceive the road situation, markings, traffic lights, signs, and other objects when a vehicle is moving. This fact revealed during the research can be considered as critical since it is related to traffic safety assurance (Skorokhodov et al. 2020). In the course of further research, the values of the distances covered during the driver’s reaction time were obtained for men of the age category of 26–30 years (Table 3). Table 3. Dependence of the vehicle speed on the driver’s reaction time in different states. Distance covered during the driver’s reaction time when a person is able to work as usual, m

Distance covered during the driver’s reaction time in the state of alcohol intoxication, s 0.3‰

0.5‰

10

3.25

3.55

3.65

20

7.62

8.23

8.40

30

13.13

14.05

14.30

40

19.77

20.99

21.33

50

27.54

29.07

29.49

60

36.45

38.28

38.78

70

46.48

48.62

49.20

80

57.64

60.09

60.75

90

69.94

72.69

73.44

100

83.37

86.42

87.26

110

97.93

101.29

102.20

Vehicle speed (km/h)

4. Conclusions An analysis of the obtained results allows us to conclude that if we consider the driver’s reaction time in a dangerous situation, then their perception may affect the safety of not only people but also processes and objects around. The application of the obtained results in practice showed that if we consider the same driver able to work as usual and in the state of alcohol intoxication with 0.3‰, then under the same circumstances (e.g. when the car is moving at a speed of 60 km/h and a sudden danger occurs), in the first case, the driver will need about 36.5 meters to stop the vehicle and avoid an accident, while in the second case, the driver will only be able to stop the vehicle after about 38.3 meters. In the real situation, the difference of almost 2 meters will be a decisive factor, i.e. in the first case, the driver will be able to avoid an accident. References Brylev, I., Evtiukov, S., Evtiukov, S., 2018. Problems of calculating the speed of two-wheeled motor vehicles in an accident. Transportation Research Procedia 36, 84–89. DOI: 10.1016/j.trpro.2018.12.047. Danilov, I.K., Marusin, A.V., Marusin, A.V., Danilov, S.I., Andryushchenko, I.S., 2018. Diagnosis of the fuel equipment of diesel engines with multicylinder high pressure fuel injection pump for the movement of the injector valve for the diagnostic device. ICFET'18: Proceedings of the 4th International Conference on Frontiers of Educational Technologies, 157–160. DOI: 10.1145/3233347.3233363. Danilov, I., Marusin, A., Mikhlik, M., Uspensky, I., 2020. Development of the mathematical model of fuel equipment and justification for diagnosing diesel engines by injector needle displacement. Transport Problems 15 (1), 93–104. DOI: 10.21307/tp-2020-009. Del Valle, C.H.C., Sucha, M., 2019. Effects of alcohol and perceived controllability in optimistic offender drivers. Transportation Research Part F: Traffic Psychology and Behaviour 64, 58–69. DOI: 10.1016/j.trf.2019.04.024.

6

Nickolay Podoprigora et al. / Transportation Research Procedia 50 (2020) 538–544 Nickolay Podoprigora, Polina Stepina, Viktor Dobromirov, Jurij Kotikov / Transportation Research Procedia 00 (2019) 000–000

543

Evtiukov, S., Golov, E., Ginzburg, G., 2018a. Finite element method for reconstruction of road traffic accidents. Transportation Research Procedia 36, 157–165. DOI: 10.1016/j.trpro.2018.12.058. Evtiukov, S., Karelina, M., Terentyev, A., 2018b. A method for multi-criteria evaluation of the complex safety characteristic of a road vehicle. Transportation Research Procedia 36, 149–156. DOI: 10.1016/j.trpro.2018.12.057. Ginzburg, G., Evtiukov, S., Brylev, I., Volkov, S., 2017. Reconstruction of road accidents based on braking parameters of category L3 vehicles. Transportation Research Procedia 20, 212–218. DOI: 10.1016/j.trpro.2017.01.054. Kerimov, M., Safiullin, R., Marusin, A., Marusin, A., 2017. Evaluation of functional efficiency of automated traffic enforcement systems. Transportation Research Procedia 20, 288–294. DOI: 10.1016/j.trpro.2017.01.025. Kravchenko, P., Oleshchenko, E., 2017. Mechanisms of functional properties formation of traffic safety systems. Transportation Research Procedia 20, 367–372. DOI: 10.1016/j.trpro.2017.01.051. Kurakina, E., Evtiukov, S., Rajczyk, J., 2018. Forecasting of road accident in the DVRE system. Transportation Research Procedia 36, 380–385. DOI: 10.1016/j.trpro.2018.12.111. Kuraksin, A., Shemyakin, A., Borychev, S., 2017. Meso-DTA traffic model technology for evaluating effectiveness and quality of the organization of traffic in large cities. Transportation Research Procedia 20, 378–383. DOI: 10.1016/j.trpro.2017.01.062. Leung, S., Starmer, G., 2005. Gap acceptance and risk-taking by young and mature drivers, both sober and alcohol-intoxicated, in a simulated driving task. Accident Analysis & Prevention 37 (6) 1056–1065. DOI: 10.1016/j.aap.2005.06.004. Marusin, A.V., 2017a. A method of assessing the efficiency of systems of automatic recording of traffic violations. PhD Thesis in Engineering. Saint Petersburg State University of Architecture and Civil Engineering, Saint Petersburg. Marusin, A.V., 2017b. Improving the diagnostics of plunger pairs in high-pressure fuel pumps of motor and tractor diesel engines. PhD Thesis in Engineering. Kostychev Ryazan State Agrotechnological University, Ryazan. Marusin, A.V., Abliazov, T.Kh., 2019. Public-private partnership as a mechanism for development of automated digital systems. Transport of the Russian Federation, 3 (82), 23–25. Marusin, A.V., Danilov, I.K., Khlopkov, S.V., Marusin, A.V., Uspenskiy, I.A., 2020. Development of a mathematical model of fuel equipment and the rationale for diagnosing diesel engines by moving the injector needle. IOP Conference Series: Earth and Environmental Science 422, 012126. DOI: 10.1088/1755-1315/422/1/012126. Marusin, A., Marusin, A., Ablyazov, T., 2019. Transport infrastructure safety improvement based on digital technology implementation. Atlantis Highlights in Computer Sciences, Vol. 1. International Conference on Digital Transformation in Logistics and Infrastructure (ICDTLI 2019), 353–357. DOI: 10.2991/icdtli-19.2019.61. Marusin, A., Marusin, A., Danilov, I., 2018. A method for assessing the influence of automated traffic enforcement system parameters on traffic safety. Transportation Research Procedia 36, 500–506. DOI: 10.1016/j.trpro.2018.12.136. Plawecki, M.H., Koskie, S., Kosobud, A., Justiss, M.D., O’Connor, S., 2018. Alcohol intoxication progressively impairs drivers’ capacity to detect important environmental stimuli. Pharmacology Biochemistry and Behavior 175, 62–68. DOI: 10.1016/j.pbb.2018.05.009. Podoprigora, N., Dobromirov, V., Pushkarev, A., Lozhkin, V., 2017. Methods of assessing the influence of operational factors on brake system efficiency in investigating traffic accidents. Transportation Research Procedia 20, 516–522. DOI: 10.1016/j.trpro.2017.01.084. Podoprigora, N., Dobromirov, V., Stepina, P., 2018. Method of assessing the influence of the moisture content in the braking fluid on the braking system actuation efficiency. Transportation Research Procedia 36, 597–602. DOI: 10.1016/j.trpro.2018.12.147. Pushkarev, A., Podoprigora, N., Dobromirov, V., 2018. Methods of providing failure-free operation in transport infrastructure objects. Transportation Research Procedia 36, 634–639. DOI: 10.1016/j.trpro.2018.12.140. Repin, S., Evtiukov, S., Maksimov, S., 2018. A method for quantitative assessment of vehicle reliability impact on road safety. Transportation Research Procedia 36, 661–668. DOI: 10.1016/j.trpro.2018.12.128. Safiullin, R., Kerimov, M., Afanasyev, A., Marusin, A., 2018. A model for justification of the number of traffic enforcement facilities in the region. Transportation Research Procedia 36, 493–499. DOI: 10.1016/j.trpro.2018.12.135. Safiullin, R.N., Kerimov, M.A., Marusin, A.V., 2016. Improving the efficiency of the system of photo and video fixation of administrative offences in road traffic. Bulletin of Civil Engineers 3 (56), 233–237. Safiullin, R., Marusin, A., Safiullin, R., Ablyazov, T., 2019. Methodical approaches for creation of intelligent management information systems by means of energy resources of technical facilities. E3S Web of Conferences 140, 10008. DOI: 10.1051/e3sconf/201914010008. Seliverstov, Y.A., Seliverstov, S.A., Komashinskiy, V.I., Tarantsev, A.A., Shatalova, N.V., Grigoriev, V.A., 2017. Intelligent systems preventing road traffic accidents in megalopolises in order to evaluate. 2017 XX IEEE International Conference on Soft Computing and Measurements (SCM). Saint Petersburg, Russia, 489–492. DOI: 10.1109/SCM.2017.7970626. Shemyakin, A., Kuraksin, A., 2016. A method for studying traffic flow characteristics in the central part of Ryazan based on the global positioning system technologies. Science and Technology in Transport 4, 91–99. Skorokhodov, D., Seliverstov, Y., Seliverstov, S., Burov, I., Vydrina, E., Podoprigora, N., Shatalova, N., Chigur, V., Cheremisina, A., 2020. Using augmented reality technology to improve the quality of transport services. In: Sukhomlin, V., Zubareva, E. (eds). Convergent Cognitive Information Technologies. Convergent 2018. Communications in Computer and Information Science, 1140. Springer, Cham, 339–348. DOI: 10.1007/978-3-030-37436-5_30. Soo, S., Abdel Sater, K.I., Khodyakov, A.A., Marusin, A.V., Danilov, I.K., Khlopkov, S.V., Andryushenko, I.S., 2020. The ways of effectiveness increase of liquid fuel with organic addition appliance in aerospace equipment. Advances in the Astronautical Sciences 170, 833–838.

544

Nickolay Podoprigora et al. / Transportation Research Procedia 50 (2020) 538–544 Nickolay Podoprigora, Polina Stepina, Viktor Dobromirov, Jurij Kotikov / Transportation Research Procedia 00 (2019) 000–000

7

Vorozheikin, I., Marusin, A., Brylev, I., Vinogradova, V., 2019. Digital technologies and complexes for provision of vehicular traffic safety. Atlantis Highlights in Computer Sciences, Vol. 1. International Conference on Digital Transformation in Logistics and Infrastructure (ICDTLI 2019), 385–389. DOI: 10.2991/icdtli-19.2019.67.