Identification of individual problematic driving performance - - PowerPoint PPT Presentation

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Identification of individual problematic driving performance - - PowerPoint PPT Presentation

Sep 09, 2023 243 likes •442 views

26 th ICTCT Workshop in Maribor, Slovenia 24 th October 2013 Identification of individual problematic driving performance parameters in curve taking Seddigheh Babaee Seddigheh.babaee@uhasselt.be Magnitude of the problem, increasing trends

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Identification of individual problematic driving performance parameters in curve taking

Seddigheh Babaee Seddigheh.babaee@uhasselt.be 26th ICTCT Workshop in Maribor, Slovenia 24th October 2013
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Magnitude of the problem, increasing trends

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Road traffic WHO 2004 1.3 Malaria WHO 2008

<1 Tuberculosis WHO 2008 1.8 AIDS-related deaths UNAIDS 2008 Number of deaths (millions) 1.3 Nearly 1.3 million deaths 20-50 million injured

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SLIDE 3 Rank Disease or Injury 1 Ischaemic heart disease 2 Cerebrovascular disease 3 Lower respiratory infections 4 Chronic obstructive pulmonary disease 5 Diarrhoeal diseases 6 HIV/AIDS 7 Tuberculosis 8 Trachea, bronchus, lung cancer 9 Road traffic injuries 10 Prematurity & low-birth weight Rank Disease or Injury 1 Ischaemic heart disease 2 Cerebrovascular disease 3 Chronic obstructive pulmonary disease 4 Lower respiratory infections 5 Road traffic injuries 6 Trachea, bronchus, lung cancer 7 Diabetes mellitus 8 Hypertensive heart disease 9 Stomach cancer 10 HIV/AIDS 2004 2030

Top 10 leading causes of death

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Literature review

  • Bella, F. , 2008. Driving simulator for speed research on two-lane rural roads
  • Boyle & Lee, 2010. Using driving simulators to assess driving safety
  • Montella et al. 2011. Simulator Evaluation of Drivers’ Speed, Deceleration
and Lateral Position at Rural Intersections in Relation to Different Perceptual Cues
  • Auberlet et al. 2012. The Impact of Perceptual Treatments on Driver’s
Behavior: From Driving Simulator Studies to Field Tests-first Results
  • Merat & Jamson 2013. The Effect of Three Low-cost Engineering Treatments
  • n Driver Fatigue: A Driving Simulator Study
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Objective

We aim to investigate the driving behavior of different drivers in and nearby a curve using data from a fixed-based driving simulator in order to:  Identify the best drivers within the sample  To gain insight into the most problematic behavior of each driver

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Methodology: data collection and analysis

Real-world simulator image

The road width is 2.8m and the posted speed limit is 70km/h
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Methodology: participants and simulator

Participants

38 volunteers 4 excluded: 2 simulator sickness, 2 miss values 34 participants in dataset; 23 men, 11 women Age: 18-54 mean age 26.32; SD 10.47

Driving simulator

Fixed-base STISIM M400 with 180° parabollic screen

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Indicators of driving behavior

Speed

The resultant of longitudinal and lateral speed is used

Acceleration

The resultant of longitudinal and lateral acceleration is used

Lateral position

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8 different measurement points

P1: 500m P2: 166m Before curve P3: 50m P4: curve entry P5: middle curve At curve P6: curve end P7: 50m P8: 100m

After curve

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Hierarchically structured driving performance indicators

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Multiple Layer Data Envelopment Analysis- MLDEA

  • The focus of DEA is on individual
  • By solving the model, the best possible indicator weights are

determined

  • An index score between zero and one is obtained for each

driver, with a higher value indicating a better performance

  • Take into account the layered hierarchy of indicators that
  • ften exist in reality
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Results and discussion

Index scores and drivers ranking Weight allocation and required improvement priorities Comparison of typical drivers in terms of driving performance parameters

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Index scores and drivers ranking

Multiple Layer DEA-CI Sensitivity Analysis Driver ID Score Driver ID
  • Ave. Score
1 1.000 1 1.000 13 1.000 33 1.000 14 1.000 14 0.999 33 1.000 13 0.998 6 0.997 6 0.997 34 0.993 5 0.995 5 0.992 34 0.992 9 0.984 9 0.989 2 0.981 2 0.985 32 0.973 32 0.979 . . . . . . . . . . . . 3 0.894 3 0.903 8 0.885 8 0.896 23 0.883 16 0.888 16 0.877 23 0.886 12 0.877 12 0.884 7 0.852 7 0.862 18 0.822 18 0.832 28 0.804 28 0.823 29 0.793 29 0.810 21 0.740 21 0.752
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Sensitivity Analysis:

Boxplot of drivers when eliminating indicators at one point at a time
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SLIDE 15 Acceleration

Required improvement priorities for the case of worst driver

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SLIDE 16 40 50 60 70 80 90 100 110 120 130 140 P1 P2 P3 P4 P5 P6 P7 P8 Speed (Km/h) Best performer Worst performer Our methodology matches the experimental data accurately The speed of the best-performer versus the worst-performer
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The acceleration of the best-performer versus the worst-performer

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1 2 P1 P2 P3 P4 P5 P6 P7 P8 a) Long Acc m/s2
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  • 3.5
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  • 2.5
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0.5 1 2 3 4 5 6 7 8 b) Lat Acc m/s2 Best performer Worst performer
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Conclusion The application of this methodology to simulator data is innovative

and will: Distinguish the best drivers Gain insight into the most problematic behavior of each driver

Future research

Assessment with respect to older drivers Focus on young novice drivers

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Thank you for your attention

seddigheh.babaee@uhasselt.be Babaee, S., Hermans, E., Shen, Y., Wets, G., Brijs, T.