B THE EFFECTS OF ALCOHOL ON DRIVER DECISION MAKING AND RISK TAKING. Systems Technology, Inc., S. Hawthorne Blvd., Hawthorne, CA USA

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1987 Elsevier Science Publishers B.V. (Biomedical Division) B 258 7 7 Alcohol, drugs and traffic safety T86 P.C. Noordzij and R. Roszbach, editors.

1 1987 Elsevier Science Publishers B.V. (Biomedical Division) B Alcohol, drugs and traffic safety T86 P.C. Noordzij and R. Roszbach, editors. 177 THE EFFECTS OF ALCOHOL ON DRIVER DECISION MAKING AND RISK TAKING ANTHONY C. STEIN, Ph.D. and R. WADE ALLEN Systems Technology, Inc., S. Hawthorne Blvd., Hawthorne, CA USA INTRODUCTION This paper reports on a study designed to investigate the effects of alcohol on driver decision making and risk taking. It has been a commonly held belief that alcohol makes drivers more willing to take risks and less cautious in their decision making. Anecdotal evidence points to drivers under the influence of alcohol running red lights, speeding, and generally driving in a reckless manner. The epidemiological data makes a strong case for the impairing effects of alcohol. Over the past decade alcohol has been involved in 50 percent of the traffic fatalities in the United States (1). More recent evidence indicates that in addition to contributing to accidents, alcohol increases the severity of injury in a crash <2). In a fairly comprehensive review of the literature, Levine and his colleagues (3) categorized effects according to abilities or behavioral factors required for task performance. They found sensory-perceptual tasks seemed to be the most impaired, while psychomotor tasks were least affected by alcohol and cognitive tasks fell somewhere in between. Moskowitz, in a review of alcohol effects on complex tasks <4), found increased psychological refractory period delay, impaired short term memory, increased reaction time under uncertainty, and increased eye fixation times while driving. He also found divided attention tasks are impaired by alcohol at very low levels (0.17 to 0.8 promi1 w/v). DECISION MAKING AND RISK TAKING Prior to any discussion of risk taking or decision making, we need to have a common set of definitions: Risk Takino - the objective risk level or probability of failure. Risk Perception - the subjective or perceived risk level. Risk Acceptance - the perceived risk threshold. Psvchomotor Skill - physical activity required to accomplish a task. The driver's decision makings and subsequent risk taking, is based on perceptions psychomotor skill and risk acceptance. When these factors are combined, they give rise to a decision having an ob iective probability of success which we define as risk takino. If we were to observe a driver running a red light, for example, we would conclude that the driver exhibited excessive risk taking, but we have no way to determine why the light was run. It could be due to the driver s having misperceived the time interval of the yellow light, distance to the intersection, or vehicle speed, resulting in incorrect risk perception; it could also be because the time used in making the decision was excessive, delaying the reaction time for accelerating or braking to a safe stop <ie., exceeding the driver s psychomotor skill); or the driver may have elected to accept the risk of running the red light to minimize time delays. Prior studies on driver decision making have found an increase in risk taking under alcohol (5, 6, 7), but none of these studies differentiated between the various factors underlying the decision process. They were also inconsistent B 25877

178 in the definition and simulation of driver risk taking, the analysis of the individual behavior components leading to the driver s decision, and in the use of tangible risks.

2 178 in the definition and simulation of driver risk taking, the analysis of the individual behavior components leading to the driver s decision, and in the use of tangible risks. To resolve this confusion, the study herein was designed to: 1) divide driver behavior into perceptual, psychomotor, and cognitive components; 2) use rewards and penalties to simulate real-world risks (e.g., accidents, tickets, lost time); and 3) use tasks which simulated the temporal pressure of normal driving. EXPERIMENTAL METHODS Approach This research was accomplished in two separate experiments, the first a simulator study and the second involving full-scale validation. The two experiments were designed to be as similar as possible to allow direct comparison of the results. Simulation - The driving simulator described more fully in Allen, et al., (8) consists of a cut down car cab with fully interactive controls. The interactive features include complete steering and speed control of the vehicle while viewing a computer generated roadway. The subject was presented with curves, obstacles, and traffic signal events at specific locations on the driving course. Field Validation - The field validation study was conducted on a closed course using an instrumented vehicle described by Klein and his colleagues elsewhere (9). Special equipment was added to the vehicle allowing it to trigger the signal light task and provide data to the experimenters. As in the simulation, the driver was required to negotiate curves and obey a traffic signal, but the obstacle task was not implemented. Driving Scenarios In both experiments the driver was required to complete an approximately 15 minute drive, responding to the various tasks as they were presented. The tasks were presented to the driver in a pseudo random order, and sequence starting points were varied and counterbalanced to avoid learning. Detection circuits recorded each time the driver exceeded the speed limit or ran a red light. A "police officer" was present during the drive approximately 30 percent of the time, and "tickets" were given for these violations. Accidents were recorded each time the driver exceeded the shoulder of the roadway, struck obstacles (in the simulation), or exceeded a preset lateral acceleration on a curve (in the field test). To provide the driver immediate feedback (as in the real world), a siren indicated a ticket, and a crash sound indicated an accident. Signal light timing was controlled similarly in both experiments. When the vehicle approached the intersection, the signal light initially turned green, at a random-appearing time later, the signal turned yellow. This time was controlled by a circuit which compensated for car speed such that the time interval to the intersection was the same for a given intersection timing, regardless of the approach speed, if the driver maintained that speed. The yellow light interval was held constant a 3 seconds, following which the light turned red. Thus, the probability for successfully making the light was controlled without placing artificial speed constraints on the subject. Five signal timings were automatically commanded. One was set to require a sure stop, and another a sure go. The remaining three timings ranged from a probable stop to a probable go. The kinematics for stopping or going are discussed fully in reference 10.

179 In addition to obtaining objective data on the drivers stop and go decisions, the driver was required to tell the experimenter his subjective probability of failure if he were to attempt to

3 179 In addition to obtaining objective data on the drivers stop and go decisions, the driver was required to tell the experimenter his subjective probability of failure if he were to attempt to "make" the light. Thus, the data set included all of the components involved in decision making. Objective risk from the signal timing, subjective risk from the driver s report, and risk acceptance from the driver s behavior. A reward/penalty structure was employed to induce real-world driving behavior (11), and the scaling of the structure was made large enough to be meaningful and comparable to the subjects hourly wages. Rewards were given for completing the run (simulating the real-world motivation of arriving at a destination) and for beating a reference completion time (simulating the realworld motivation of driving with the flow of traffic, at or near the speed limit). Penalties were assessed for having an accident, for route guidance errors (in the field test), and for getting a ticket. Two levels of ticket penalties were used, as this factor is one that could easily be manipulated in the real world. Subject Population, Treatments, and Procedures Subjects were recruited through ads placed in newspapers and on bulletin boards in local businesses. Subjects were required to be male, SI to 65 years of age, licensed drivers, and heavy drinkers (defined as being able to reach a peak BAC of 1.5 promil w/v). The subjects were matched and divided into two groups based on age and scores on a hostility test (IE) and a betting test.(13). Twelve subjects completed the simulator experiments; and at a later date, a different group of 14 subjects completed the field validation study. The alcohol dose was calculated using the Widmark equation (14). In the simulator study the subject was administered two drinks of equal volume, designed to give a peak BAC of 1.0 promil (w/v), over an 80 minute period. The subject then drove the simulator for an ascending BAC trial. Following this trial he was given another drink whose volume was calculated to reach a peak BAC of 1.5 promil (w/v). The ascending BAC trial was dropped for the field tests because minimal differences were observed between ascending and descending portions of the BAC curve. Thus, the subjects in the field tests were given three drinks of approximately equal volume over a two hour period. In both tests the subjects were trained on all aspects of the experiment prior to actual testing. For the signal light task training continued until objective performance and subjective estimates were consistent in the view of the experimenter. RESULTS AND DISCUSSION Overall Performance - Subject performance data measured over the entire driving scenario were compared for both studies, and excellent agreement is seen between the simulator and field tests. Total payoff was used as an indicator of the subject s overall performance. In both studies alcohol significantly impaired the driver s performance (p < 0.01). No significant differences were noted between the two ticket penalty groups, and subject s indicated that they were motivated by the reward/penalty structure. Alcohol induced impairment was indicated by increases on both speeding tickets and accidents (accidents: p < 0.01; tickets: p < 0.05), while run completion time was insensitive to BAC. Since the driving completion time was essentially constant, increased incidence of speeding tickets with increased BAC implies increasing speed variability. As subjects were well aware of the speed limit and the probability of detection, and since speed feedback was available

4 180 both visually and aurally* this increased variability suggests decrements in the driver s perception and/or speedometer monitoring. Signal Light Behavior - The objective probabilities of going* P(G), and of failing given a go, P(F/G), for both the simulation and field test were compared to determine the driver risk taking behavior. Probabilities were computed by dividing the total number of outcomes by the total number of opportunities (e.g.* P(F/G) = Number of go failures/number of go s). For example* in the simulation the subjects always exhibited go behavior at Intersection 2, P(G) = 1, and the timing was such that the probability of failure was zero, P(F/G) = 0. At Intersection 3 the timing was also adequate to allow safe go s, however the drivers did not always make this choice ( P(G) = 0.75 ). This behavior was not sensitive to alcohol, and the subjects appeared to have been behaving conservatively on this intersection. Subjects did not go very often on Intersection 4, and had a high failure rate when they did. At Intersection 4 in the simulation, as well as at all intersections in the field test, there was an indication of increased go behavior under alcohol (p < 0.01). As one explanation for this increased going behavior in spite of increased failures, the variabi1ity of the subjective risk perception, SP(F/G>, increased although the average perception of risk remained relatively constant. Considering a typical switching criterion from "go" to "stop", the increased variability of risk perception with increased alcohol leads to a greater percentage of estimates below the "stop" threshold. The justification for this interpretation was validated via statistical analysis (p < 0.05), a complete description of this analysis and the resulting decision making model are found in Reference 15. Risk acceptance thresholds, SPc(F/G), were computed for each subject and each run. These data were then plotted and a trigonometric function was used to approximate the risk acceptance function for Intersections 2, 3, and The results of this analysis found no alcohol effect on risk acceptance. Brake response time on the signal light task was used as a measure of the driver s signal task psychomotor behavior. The results showed no effect of alcohol on either the mean or variability in response time. SUMMARY AND CONCLUDING REMARKS Overall driving performance was significantly degraded by alcohol, which comes as no surprise. This degradation was evidenced by an increase in accidents and tickets for both speeding and running red lights. An alcohol effect was observed for the driver s subjective probability of failure at the signal light task, while no alcohol effect was observed for run completion. This indicates the driver s perception of speed (for the speeding tickets), and of speed and distance (for the signal tickets), was impaired; and that he was unaware of this impairment. Additionally, drivers were consistent in their risk acceptance behavior (going on signals with low perceived risk and stopping on signals with high perceived risk). This study should put to rest the notion that alcohol impaired drivers are more willing to take risks or less cautious in their decision making. This is unfortunate, for if this were the case we might be able to train drivers to be more cautious. Instead, we find the locus of alcohol on risk taking at the perceptual level, and as a function of increased perceptual variability. A classic case of Garbage In, Garbage Out.

181 REFERENCES 1. Fatal Accident Reporting System 1984 (1984) NHTSA Report DOT HS 806 919 2.

5 181 REFERENCES 1. Fatal Accident Reporting System 1984 (1984) NHTSA Report DOT HS Waller PF, Hansen AR, Stutts JC and Popkin CL (1986) Alcohol: A potentiating factor in motor vehicle crash injury. In Viano DC (ed.) Alcohol, Accidents, and Injuries, Society of Automotive Engineers, Warrendale, PA, pp Levine JM, Greenbaum GD and Notkin ER (1973) The Effect of Alcohol on Human Performance: A Classification and Integration of Research Findings. American Institutes for Research 4. Moskowitz H (1974) Alcohol Influences Upon Sensory Motor Function, Visual Perception, and Attention. In Perrine MB: Alcohol, Drugs, and Driving, NHTSA Report DOT HS , pp Cohen J, Dearnaley EJ and Hansel CEM (1958) The Risk Taken in Driving Under the Influence of Alcohol. British Medical Journal, 21 June 1958, pp Lewis EM and Sarlanis K (1969) The Effects of Alcohol on Decision Making With Respect to Traffic Signals. Department of Health, Education and Welfare Report ICRL RR Ellingstad VS, McFarling LH and Struckman LL (1973) Alcohol, Marihuana and Risk Taking. NHTSA Report DOT HS Allen RW, Hogge JR and Schwartz SH (1975) An Interactive Driving Simulation for Driver Control and Decision-Making Research. Proceedings of the 11th Annual Conference on Manual Control, NASA TM X-62,464, pp Klein RH, Allen RW and Peters RA (1976) Driver Performance Measurement and Analysis System (DPMAS) Descriptions and Operations Manual. Systems Technology, Inc., Hawthorne, CA, (TM ) 10. Allen RW, Schwartz SH, Hogge JR and Stein AC (1978) The Effects of Alcohol on the Driver s Decision Making Behavior: Volume I: Executive Summary and Technical Report. NHTSA Report DOT HS Stein AC, Schwartz SH and Allen RW (1978) Use of Reward-Penalty Structures in Car-Driving Research. Proceedings of the 14th Annual NASA-University Conference on Manual Control, Los Angeles, (NASA, CP-2060) 12. Pelz DC (1970) Hostility Questionnaire. University of Michigan Institute for Social Research, Survey Research Center 13. Hurst PM and Seigel S (1956) Prediction of Decisions From a Higher Ordered Metric Scale of Utility. J. Experimental Psychology, Vol. 2, pp Widmark EMP (1932) Die Theoretichen Grundlagen und Die Praktische Verwendbarkeit Der Gerichtlich-Medizineschen Alkoholbestimmung. Berlin, Urban and Schwarzenberg, Schwartz SH and Allen RW 1979) A Decision Model Applied to Alcohol Effects on Driver Signal Light Behavior. Presented at the 17th IEEE Conference on Decision and Control, San Diego, CA, Jan , 1979 (STI Paper 224R).

6 1 S W I 1 I n s i

Accident Rates of Novice Drivers with Simulator Training

Accident Rates of Novice Drivers with Simulator Training R. Wade Allen*, George D. Park*, Marcia L. Cook*, Dary Fiorentino** *STI, **SCRI 2007 TRB Annual Meeting Human Factors Workshop on Teen Drivers