Driver behavior

When enrichment episodes occur in the real world, but not in the laboratory under federal certification tests, real-world emissions are significantly higher than predicted. Further complicating emissions prediction is that aggressive driver behavior and complex traffic flow characteristics play a large role in enrichment occurrence. Current vehicle activity simulation models can predict average speeds and traffic volumes very well, but poorly predict the hard-accel-eration events that lead to enrichment. 

In a study of the effect of fexofenadine on driving and psychomotor behavior there were no differences between fexofenadine and placebo on reaction times, decisionmaking, or driver behavior (72). However, one criticism of this study was the failure to include a positive control, such as diphenhydramine. 

Potter PC, Schepers JM, Van Niekerk CH. The effects of fexofenadine on reaction time, decision-making, and driver behavior. Ann Allergy Asthma Immunol 2003 91 177-81. 

In order to reduce the probability of a traffic accident, the safety of vehicles is continually being improved, for example, by improving braking or by lowering the center of gravity of tanks. Attempts are also being made to improve driver behavior by awareness and reflex training. 

The two models given in this example cooperate and are closely linked. Thus the transition T8 from OS to RV mode shall not be possible unless the second model is executed which means that the driver needs to show his intention to reverse and then acknowledge the command. Of course, one of the advantages of such modeling is the automatic construction of paths in the model. And from paths can be derived the required preconditions on the driver behavior. 

X Assessing applicants — Seventy percent of all companies (90 percent of large carriers) use safety-related criteria to evaluate driver applicants. Ninety percent or more of carriers use drug testing, past traffic records, on-road tests for evaluating driver behavior, and license qualification checks as effective means of assessing the safety risk of driver applicants. 

The rationale for this distinction between stand alone and non-stand alone BASICs is that, although each of the BASICs applies to both carriers and drivers compliance and safety, the stand alone BASICs are more directly related to driver behavior that contributes to crashes. In particular, an effectiveness study on the SMS has shown that carriers with past poor performance in the Unsafe Driving or Fatigue Driving/HOS Compliance BASICs were subsequently involved in crashes at a considerably higher rate than the overall crash rate of the motor carrier population. 

Predicting which driver will be involved in a future crash has always been one of the more difficult tasks of the safety pro. The American Transportation Research Institute (ATRl) may have provided some help in this matter. In 2005 ATRl released the results of a study entitled Predicting Truck Crash Involvement Developing a Driver Behavior-Based Model and Recommended Countermeasures. ATRl provides insight into which driving behaviors tend to lead to an increased chance of future crash involvement. The study also determined what companies with low crash rates are doing to prevent behaviors that lead to crashes. 

The other portion of the study that pertains to the safety pro was a survey of carriers that had low crash rates. These carriers maintain low crash rates because they are successfid at intervening and changing driver behaviors before crashes occur. The carriers involved in the study had these traits in common ... 

Remember, it will be up to you to assign the values. If you choose to use citations rather than violations, that is your decision. If you decide that crashes will remain at full value for three years that is also up to you. Just remember, what you establish will apply to all drivers. In the name of fairness you cannot establish a system that charges certain drivers extra. Also remember to include all negative behaviors. One of the problems with many tracking and measuring systems is they are one-dimensional. They look only at crashes or only at citations. You are trying to assess your drivers behavior, so you will want to use all components of their performance in yom system. 

Whether you have six drivers or 60 drivers, set up a tracking and measuring system. Do not leave something as important as driver behavior to memory or feelings. 

Instituting a tracking and measuring system that is driver behavior based will allow you to see the patterns and trends... 

Remember the frequency factor — You won t change or maintain anyone s behavior with a one-time reinforcer. In order for your rewards to effectively change your drivers behavior, they must be given often. This is why, in many instances, large incentive programs (with potentially big pay-outs) often fail to achieve desired results — the rewards, when and if the/re achieved, are simply too infrequent. 

Immediate supervisors are in the best position to observe imusual driver behavior. 

Objective To prevent accidents by tr3dng to anticipate hazardous situations and adjusting driver behavior to compensate. 

Actual knowledge means actual knowledge by an employer that a driver has used alcohol or controlled substances based on the employer s direct observation of the driver, information provided by the driver s previous employer(s), a traffic citation for driving a CMV while under the influence of alcohol or a controlled substance, or a driver s admission of alcohol or controlled substance use under the provisions of Sec. 382.121. Direct observation as used in this definition means observation of alcohol or controlled substance use and does not include observation of driver behavior or physical characteristics sufficient to warrant reasonable suspicion testing under Sec. 382.307. 

The driving state is normally continually monitored (by the driver and/or a system) in order to make corrections on any or all of these levels if required. Detailed applications, variations and refinements of this model can be found in the literature [4, 10-12]. Classically, active safety systems, e.g.. Dynamic Stability Control (DSC), have been designed to provide support at the stabilization level. At this level, the target quantities are generally well defined in terms of vehicle physics. Preventive pedestrian protection, which is in the focus of this thesis, addresses primarily the maneuvering level and thus involves additional eomplexities in control—particularly those involving the interpretation of driver behavior and the interaction of system actions with the driver. 

The importance of controlling confounders and interaction effects shall be pointed out by an example. Using the retrospective approach, one study evaluated the effects of xenon headlamps on accidents in Germany [20] on basis of the federal accident statistics. As a result, introducing xenon in 100 % of passenger vehicles would lead to a decrease in 6 % of all accidents and 18 % of all fatalities. The study claims that all possible confounders were taken into account and do not bias the results [20], The possible confounders cited, such as exposure time of vehicles, driver behavior... 

The importance of subject experiments (or behavioral studies) is founded on the fact that for active safety, driver behavior is more important than the driving characteristics of the vehicle [17]. However, human behavior is subject to a large variability [43], which can be modeled, e.g., on the basis of experiments [87]. The findings from... 

The driver and his behavior are of high relevance for the genesis of an accident (see Sect. 1.1, p. 1) as well as for the evaluation of changes in traffic safety by means of simulation (see Chap. 3). The change in driver behavior (e.g., particular reaction times) due to an active safety system can be derived by comparing use versus non-use of a system and serves as input for simulations as described in the previous chapter. To this end, driver behavior in response to a preventive pedestrian protection system (short system) and his acceptance of specific system actions (especially false-positive responses) are investigated. The contents of this chapter are also partly included in [1, 2]. 

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