1

Determining the Direct Effect of Triazolam on the increase of a driver’s steering entropy in

the presence of simulator sickness in a simulated driving environment

Alex Chen and Chris DeitsStatistics 566 – Final Presentation

2

Background

Perspective: Driving as a Complex

Task • Driving is one of the most complex

tasks performed by people • Driving requires close coordination

between different senses − Visual − Haptic (Touch) − Auditory − Vestibular (Balance – Motion)

• Driving is cognitively taxing on people• Requires constant decision making on

the part of the driving due to ever-changing environment conditions

• Knowledge of this decision process is first step to understand the behavior of drivers.

3

• Decision making in driving context can be seen a relatively simple feedback loop.

• Decision making developed as mental model consists of four distinct states • Perception• Recognition • Action Selection • Action Implementation

• Any disruption or inhibiting of these 4 cognitive processes can lead to unfortunate consequences when operating a vehicle.

4

Human Decision Making Process

Receptors Perception

Long-term memory

Response selection

Response execution

Controlled system

Working memory

Attentional resources

Decision making Recognition

5

Human Decision Making Process

Receptors Perception

Long-term memory

Response selection

Response execution

Controlled system

Working memory

Attentional resources

Decision making Recognition

6

Human Decision Making Process

Receptors Perception

Long-term memory

Response selection

Response execution

Controlled system

Working memory

Attentional resources

Decision making Recognition

7

Human Decision Making Process

Receptors Perception

Long-term memory

Response selection

Response execution

Controlled system

Working memory

Attentional resources

Decision making Recognition

Driver Fatigue and Drugs • Drowsy driving plays a role in an

estimated 76,000 – 100,000 crashes each year in the US (Stutts et. al., 2003)

• 50% of the accidents involving commercial drivers is fatigue related (Wickens et al., 2004)

• Benzodiazepines, commonly used as sleep aids, were the 2nd most used psychoactive drug among crash-involved commercial drivers (US DOT, 2010)

8

• Toxicology labs that test for the presence of the drug rate Ambien among the top 10 drugs found in impaired drivers. (“Zombie Drivers,” NY Times , 03/08/06)

• Furthermore, these psychoactive drugs can have even more worrisome effects

• One such is ”Zombie drivers” which when people on these drugs operate vehicle in their sleep or in a near sleep fashion

Project Data

9

Study Design

• The data was sourced from a joint research project between the University of Iowa and the University of Washington.

• Study was randomized, complete cross-over design

• The study’s intention was to investigate effect of a common sleeping pill, Triazolam, on commercial bus driver behavior

• The study was performed in fully immersive driving simulator

• Participant were recruited from the U. Iowa and Iowa City Metro System

10

Background Project Data Characteristics

• 24 total participants• Three Dose levels investigated:

Placebo, 0.125 mg. and 0.250mg of Triazolam

• 3 Drives Investigated• Occurred 2 hrs. after drug

administration • These were chose to

maximize the influence of Triazolam on participants

Project Goals • We want to model the driving

behavior of the driver under the influence of the drug, since we need to know whether or not the drug caused driving impairments (measured indirectly through steering entropy), and if so, if the drug mediated the impairments through certain mechanisms that we tried to measure

• Simulator sickness can affect a person perception abilities, divert attentional and can affect decision response

11

Simulator Sickness

Steering EntropyDose Level

Explanation of Entropy

Basically, entropy is a measure of randomness. The higher the entropy, the higher the randomness of the distribution, and the more unpredictable anygiven next letter will be. Entropy is usually maximized when p(x_i) is evenlysplit among all n possibilities (in other words, when you really cannotpredict what the next step will be any better than chance).

Take a standard example: a coin flip. Here, we just have n = 2 (heads andtails). Entropy is maximized when the probability of heads is equal tothe probability of tails (or ½)

Steering entropy has previously been used as a measure of workload, investigating how much workload increases when performing a secondary task whilst driving. When performing a secondary task, driving becomes less smooth and attention to the driving task decreases. When drivers areimpaired, attention to the driving task reduces in a similar way. In this pre-trial experiment, driving in a purposely erratic manner was used to simulate impairment. This impairment manifests itself in a decrease of attention to the driving task and less smooth driving

Taken from http://www.ectri.org/YRS03/Session-6/Flint.pdf

Dependent Measure: Steering Entropy

• Steering Entropy (SE) is a measure of the randomness in steering control

• Prediction Error: e(n) = qa(n) - qp(n), at time n

• qp(n) - predicted steering angle

• qa(n) - actual steering angle

• qp(n) is calculated using a second order Taylor expansion of steering angle over time

• The prediction error is approximately normally distributed with: e(n) ~ N(0, g).

14

• The est. std. dev. of e(n), a derivative of the traditional measure of SE (Nakayama, 2000), at the 95th quantile, 2 , gwas used as the dependent measure

• Any changes in overall steering entropy will be reflected in the change of the value of the std. dev. of e(n) at the 95th quantile, 2g

• So basically – the higher the entropy, the more unpredictable the vehicle is, which presumably translates to higher accident rates

Steering Entropy Example

15

Independent Measures

• There were 3 Triazolam dose levels: Placebo, 0.125 mg. and 0.250mg

• Peak Plasma levels of Triazolam reach maximum levels within 2 hours of ingestion and has a mean plasma half-life of 1.5 to 5.5 hrs.

16

• Simulator sickness is a common presence in experiments using vehicle simulators

• Simulator sickness can take on a range of symptoms, from general nausea to affecting perception and the vision of the drivers using the simulator.

• Simulator sickness was measured via a post drive questionnaire (Kennedy et al.)

• For each drive, participants will be dichotomized into those who experienced sim sickness and those who did not.

Simulator Sickness Dose Level

Descriptive Statistics

17

Drive 1 Drive 2 Drive 3Placeb

o 0.150 mg

0.250 mg

Placebo

0.150 mg

0.250 mg

Placebo

0.150 mg

0.250 mg

Simulator

Sickness

No 6 5 3 4 5 5 5 6 3

Yes 1 3 6 4 2 4 4 3 3

Incidence of Simulator Sickness

Steering Entropy By Individuals

18

Analysis

19

Dose and Sickness Relationship

20

Entropy Modeling

Factor Df Estimate

Std. Err.

t - value

p-value

Intercept 43 0.581 0.148 3.91 < 0.001

Dose:0.125 43 -0.304 0.164 -1.85 0.071

Dose: 0.250 43 -0.332 0.187 -1.77 0.084

Sim. Sickness 43 -0.147 0.228 -0.65 0.522

Interaction Sim. Sickness and Dose: 0.125 mg

43 0.472 0.286 1.65 0.107

Sim. Sickness and Dose: 0.250 mg

43 0.286 0.289 0.98 0.328

21

• In order to model the repeated measures data, a Linear Mixed effects model was fitted

• The a random intercept for each participant was estimated for the random effect

• An unstructured correlation structure was used for the within subject correlation

• There was no significant effect, at the a =0.5 level, found for any variable

• Both dose levels were marginally significant and showed lower levels of steering entropy

LMM Effects Estimates

Conclusions • While the dose level of Triazolam does seem to have an

effect on the incidence of simulator sickness. • For the effect of Triazolam on steering entropy, while not

statistically significant, does show a decrease for both levels.

• These lower steering entropy levels may indicate a lower incidence of corrective steering than they would normally. This may indicate more

22

Future Work • Development of a Bayesian Model Framework to

help compensate for relatively small sample size. • Development of the a full causal model taking into

account other variables• Investigation of effects of Triazolam on other

behavior outcome measures• Average speed, Standard deviation of speed, Steering wheel

movement speed and reversal frequency, Frequency of accelerator pedal pressures, Frequency of brake pedal pressures

• Define relationship between steering entropy and lane deviation measures

23

References Mourant, R. R. and T. R. Thattacheny (2000). Simulator sickness in a virtual

environments driving simulator. In Proceedings of the IEA 2000/HFES 2000 Congress, pp.  534-537.

Stutts el al (2003) Driver risk factors for sleep-related crashes, Accident Analysis & Prevention, 35, 1, 321-331

US DOT (2010) Federal Motor Carrier Safety Administration - Large Truck Crash Causation Study, accessed on Nov 20, 2010 from http://www -nass.nhtsa.dot.gov/LTCCS_PUB/SEARCHFORM.ASPX

• Nakayama et al. (1999) Development of a steering entropy method for evaluating driver workload, Paper presented at SAE International Congress and Exposition, Detroit, MI.

• Wickens et. al, (2004) “ Introduction to Human Factors Engineering, ” 2nd Edition, Pearson Prentice Hall

• Kennedy et al., “Simulator Sickness Questionnaire: An Enhanced Method for Quantifying Simulator Sickness,”The International Journal of Aviation Psychology, 1993, 3(3): 203-220.

•  Pinherio, J. C., & Bates, D. M. (2000). Mixed-Effect Models in S and S-PLUS. New York: Springer.

• Greenblatt, D. J., D. R. Abernethy, A. Locniskar, J. S. Harmatz, R. A. Limjuco, and R. I. Shader (1984, July). Effect of age, gender, and obesity on midazolam kinetics. Anesthesiology  61 (1), 27-35.

• Laurell, H. and J. Törnros (1986, March). The carry-over effects of triazolam compared with nitrazepam and placebo in acute emergency driving situations and in monotonous simulated driving. Acta pharmacologica et toxicologica  58 (3), 182-186.

24

Supplemental Material

25