Abstract
This paper proposes a measurement of risk (MOR) method to recognize risky driving behavior based on the trajectory data extracted from surveillance videos. Three types of risky driving behavior are studied in this paper, i.e., speed-unstable driving, serpentine driving, and risky car-following driving. The risky driving behavior recognition model contains an MOR-based risk evaluation model and an MOR threshold selection method. An MOR-based risk evaluation model is established for three types of risky driving behavior based on driving features to quantify collision risk. Then, we propose two methods, i.e., the distribution-based method and the boxplot-based method, to determine the threshold value of the MOR to recognize risky driving behavior. Finally, the trajectory data extracted from UAV videos are used to validate the proposed model. The impact of vehicle types is also taken into consideration in the model. The results show that there are significant differences between threshold values for cars and heavy trucks when performing speed-unstable driving and risky car-following driving. In addition, the difference between the proportion of recognized risky driving behavior in the testing dataset compared with that in the training dataset is limited to less than 3.5%. The recognition accuracy of risky driving behavior with the boxplot- and distribution-based methods are, respectively, 91% and 86%, indicating the validation of the proposed model. The proposed model can be widely applied to risky driving behavior recognition in video-based surveillance systems.
Highlights
Driving safety is influenced by different factors, with the driver being one of the most important
The threshold value of the measurement of risk (MOR) needs to be determined as a criterion to classify risky driving behavior
The upper boundary and lower boundary of the MOR boxplot can be determined with the interquartile range (IQR), Q3, and Q1
Summary
Driving safety is influenced by different factors (e.g., drivers, traffic environment, vehicle types), with the driver being one of the most important. Li [7] classified risky driving behavior under snow and ice conditions into four types, i.e., overspeed driving, near car-following, illegal overtaking, and driving on the central lines by analyzing the features of roads and the environment. Various data collection methods exist, such as naturalistic driving experiments, vehicle-based sensors, and driving simulation. Some studies used vehicle-based sensors such as gyroscopes [16] and accelerometers [17] to extract longitudinal and lateral speed and acceleration as data sources to analyze driving behavior. Trajectory data extracted from video surveillance systems have been widely applied in risky driving behavior recognition research [21,22,23]. The research results can be applied to the real-time detection of risky driving behavior in video surveillance systems and provide support for accidents prevention and traffic management
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