Mobile phone use while driving has become one of the leading causes of traffic accidents and poses a significant threat to public health. This study investigated the impact of speech-based texting and handheld texting (two difficulty levels in each task) on car-following performance in terms of time headway and collision avoidance capability; and further examined the relationship between time headway increase strategy and the corresponding accident frequency. Fifty-three participants completed the car-following experiment in a driving simulator. A Generalized Estimating Equation method was applied to develop the linear regression model for time headway and the binary logistic regression model for accident probability. The results of the model for time headway indicated that drivers adopted compensation behavior to offset the increased workload by increasing their time headway by 0.41 and 0.59 s while conducting speech-based texting and handheld texting, respectively. The model results for the rear-end accident probability showed that the accident probability increased by 2.34 and 3.56 times, respectively, during the use of speech-based texting and handheld texting tasks. Additionally, the greater the deceleration of the lead vehicle, the higher the probability of a rear-end accident. Further, the relationship between time headway increase patterns and the corresponding accident frequencies showed that all drivers’ compensation behaviors were different, and only a few drivers increased their time headway by 60% or more, which could completely offset the increased accident risk associated with mobile phone distraction. The findings provide a theoretical reference for the formulation of traffic regulations related to mobile phone use, driver safety education programs, and road safety public awareness campaigns. Moreover, the developed accident risk models may contribute to the development of a driving safety warning system.
The use of mobile phones while driving is a very common phenomenon that has become one of the main causes of traffic accidents. Many studies on the effects of mobile phone use on accident risk have focused on conversation and texting; however, few studies have directly compared the impacts of speech-based texting and handheld texting on accident risk, especially during sudden braking events. This study aims to statistically model and quantify the effects of potential factors on accident risk associated with a sudden braking event in terms of the driving behavior characteristics of young drivers, the behavior of the lead vehicle (LV), and mobile phone distraction tasks (i.e., both speech-based and handheld texting). For this purpose, a total of fifty-five licensed young drivers completed a driving simulator experiment in a Chinese urban road environment under five driving conditions: baseline (no phone use), simple speech-based texting, complex speech-based texting, simple handheld texting, and complex handheld texting. Generalized linear mixed models were developed for the brake reaction time and rear-end accident probability during the sudden braking events. The results showed that handheld texting tasks led to a delayed response to the sudden braking events as compared to the baseline. However, speech-based texting tasks did not slow down the response. Moreover, drivers responded faster when the initial time headway was shorter, when the initial speed was higher, or when the LV deceleration rate was greater. The rear-end accident probability respectively increased by 2.41 and 2.77 times in the presence of simple and complex handheld texting while driving. Surprisingly, the effects of speech-based texting tasks were not significant, but the accident risk increased if drivers drove the vehicle with a shorter initial time headway or a higher LV deceleration rate. In summary, these findings suggest that the effects of mobile phone distraction tasks, driving behavior characteristics, and the behavior of the LV should be taken into consideration when developing algorithms for forward collision warning systems.
This paper proposes a method for visibility detection based on the recognition of the preceding vehicle's taillight signals using in-vehicle camera and millimeter-wave (mm-W) radar. First, we design two methods of vehicle identification. One is to use Haar-like features and an AdaBoost algorithm to train the vehicle classifier, which is mainly used to identify vehicles without turning on the taillights. The other is to identify vehicles with taillights on by means of taillight segmentation. The two identification methods are combined with a Discriminative Scale Space Tracker (DSST) to track the vehicle in the image acquired by vehicle camera and to measure anthropic visibility with mm-W radar. In addition, we drove a test vehicle on a foggy highway and collected experimental data through in-vehicle camera and mm-W radar. The experimenter observed the movement of the vehicle in front until it disappeared from the field of vision and recorded the distance of the vehicle in front measured by radar at that time as human visibility, which was also used as the ground truth to verify the accuracy of the proposed visibility detection method. The experimental results show that the visibility measured by the proposed algorithm is essentially consistent with the visibility obtained by human eyes, that is, the visibility of vehicles with no taillights, clearance lamp, emergency flasher, or fog lamp tends to rise, with an average accuracy of 88%, 91%, 90%, and 95%, respectively. In contrast to the traditional visibility measurement, this method mainly measures the maximum distance that the driver can observe when the front vehicle is not turned on or different taillights are turned on.
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