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The risks for teenagers driving with peer passengers have been widely acknowledged since a landmark study conducted in 2000, Mr. Kissinger said, but the current report, based on more recent data, analyzed driver involvement somewhat differently, and included the positive influence of adult passengers.
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However, when we analyzed drivers' average Z-distribution, it was not possible to see significant differences, as shown in Fig. 16.
As automakers rush to assign more driving chores to computers, researchers who analyze driver workload could be left shaking their heads.
This paper reports the results of a multi-factorial experiment that was aimed at the following: (a) analyzing driver's speed behavior while approaching zebra crossings under different conditions of vehicle pedestrian interaction and with respect to several safety measures and (b) comparing safety measures and identifying the most effective treatment for zebra crossings.
It uses the fuzzy rule-based system to analyze driver's eyes and head motion.
To analyze driver drowsiness several systems have been built in recent years.
The results demonstrate the effectiveness of the automated analytical framework in analyzing driver behavior, as well as evaluating the impact of adverse winter weather conditions on driver behavior.
Driver behavior and surrogate measures were defined for this study as follows: Several previous studies have used the number of lane changes to analyze driver behavior [36, 37].
This popularity has resulted in research on many different innovative techniques and measures used to quantify and analyze driver behavior [1, 2, 3].
In [34], the authors presented a method which relied on such surrogate measures to analyze driver behavior on highway ramps for which crash data were not very reliable.
To analyze driver behavior, two alternative approaches can be found in the literature to collect driving data, namely manual observation and vehicle-based tracking.
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