Driving Simulator Validation for Speed Research

Stuart T. Godley, Thomas J. Triggs, Brian N. Fildes

Presented By:Ben Block

Wen Lung Hii

Purpose• The aim of the two

experiments conducted was to validate the MURAC (Monash University Accident Research Centre) driving simulator for research on speeding countermeasures

Literature ReviewThe use of a modern advanced driving simulator for human factors research to take advantage of a simulators natural advantages:

1. Experimental control2. Efficiency3. Expense4. Safety5. Ease of data collection

Theoretical Basis• Proving a simulator is a valid

tool for generating and generalizing relative speed results for experiments involving road based speeding countermeasures aiming to influence deceleration in drivers speeds

Applicability/Practical Contribution

• Automotive fatalities continue to be a major cause of deaths in the United States

• Efforts to make the roads safer through speeding countermeasures research will help reduce these fatalities

Theoretical Contribution

• Through this experiment speed has been clearly validated as a dependent variable for research using a simulator

Data Collection•Data collected at 30Hz, converted to an average speed per meter of track•Measurements are taken along the area as shown below.

Validation Approach

•Averaged relative validity analyzed using a two-factor analysis of variance to determine the impact of the rumble strips.•Interactive relative validity analyzed using a standard correlation approach to display how and when drivers reacted to the rumble strips•Canonical correlation•Assumes that the data from each 1 m segment is independent of each other

Validation Approach• Absolute validity was analyzed using

two one-way ANOVAs• Omega squared statistic (ω2) was

used to estimate effect size because non-significant results could come from inadequate statistical power rather than actual absence of difference

Results: Stop Sign Approach•Treatment site speed significantly slower P < 0.001•No significant interaction between the two driving environments, small ω2 = 0.002. Average relative validity established•Pattern of speed was similar for both sites. A significant correlation, R = 0.40 supports interactive relative validity. •Speed higher in simulator than instrumented car for treatment (P<0.01) and control (P<0.05) sites. No absolute validity.

Results: Right Curve Approach•No difference in mean speed between the treatment and control sites, P = 0.063

•Significant interaction, P < 0.01. Average relative validity not established•Pattern of speed in the first three quarters was similar for both sites. A significant correlation, R = 0.52 supports interactive relative validity.

•Treatment site speeds were not significantly different, P = 0.590 and small ω2 = 0.007. Absolute validity for treatment site only.

Results: Left Curve Approach•Treatment site speed significantly slower P < 0.001

•Speed was significantly different between the experiments P < 0.001. Average relative validity not established

•Similar pattern of speed yields a significant correlation, R = 0.50. This supports interactive relative validity.

•Speed higher in instrumented car than simulator for treatment site (P < 0.001) but not for control site (P = 0.169). Big ω2 = 0.049 suggest a significant result could been produced if there was a larger sample. No absolute validity.

Conclusions• Speed profiles found indicate a speed

reduction when rumble strips were used compared to control roads

• There is evidence concluding that speed is a valid measure to use for experiments on the MUARC driving simulator

• Different characteristics of the road and practice hindered the validation of absolute numerical speed values

Future Research Directions• Driving simulator validation for

different types of vehicle• Driving on the right versus driving on

the left• Effect of driving simulation on real

world driving

Thank You