scholarly journals Risky Driving Behavior Recognition Based on Vehicle Trajectory

2021 ◽  
Vol 18 (23) ◽  
pp. 12373
Author(s):  
Shengdi Chen ◽  
Qingwen Xue ◽  
Xiaochen Zhao ◽  
Yingying Xing ◽  
Jian John Lu
Keyword(s):  
Risk Evaluation ◽  
Evaluation Model ◽  
Driving Behavior ◽  
Trajectory Data ◽  
Behavior Recognition ◽  
Risky Driving ◽  
Car Following ◽  
Proposed Model ◽  
Behavior Based ◽  
Risk Evaluation Model

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.

scholarly journals Driving Behavior Based Relative Risk Evaluation Using a Nonparametric Optimization Method

2021 ◽  
Vol 18 (23) ◽  
pp. 12452
Author(s):  
Qiong Bao ◽  
Hanrun Tang ◽  
Yongjun Shen
Keyword(s):  
Relative Risk ◽  
Risk Evaluation ◽  
Absolute Risk ◽  
Evaluation Model ◽  
Optimization Method ◽  
Driving Behavior ◽  
Risky Driving ◽  
Driving Behaviors ◽  
Proposed Model ◽  
Risky Driving Behaviors

Evaluating risks when driving is a valuable method by which to make people better understand their driving behavior, and also provides the basis for improving driving performance. In many existing risk evaluation studies, however, most of the time only the occurrence frequency of risky driving events is considered in the time dimension and fixed weights allocation is adopted when constructing a risk evaluation model. In this study, we develop a driving behavior-based relative risk evaluation model using a nonparametric optimization method, in which both the frequency and the severity level of different risky driving behaviors are taken into account, and the concept of relative risk instead of absolute risk is proposed. In the case study, based on the data from a naturalistic driving experiment, various risky driving behaviors are identified, and the proposed model is applied to assess the overall risk related to the distance travelled by an individual driver during a specific driving segment, relative to other drivers on other segments, and it is further compared with an absolute risk evaluation. The results show that the proposed model is superior in avoiding the absolute risk quantification of all kinds of risky driving behaviors, and meanwhile, a prior knowledge on the contribution of different risky driving behaviors to the overall risk is not required. Such a model has a wide range of application scenarios, and is valuable for feedback research relating to safe driving, for a personalized insurance assessment based on drivers’ behavior, and for the safety evaluation of professional drivers such as ride-hailing drivers.

A NOVEL FIVE-CATEGORY LOAN-RISK EVALUATION MODEL USING MULTICLASS LS-SVM BY PSO

2012 ◽  
Vol 11 (04) ◽  
pp. 857-874 ◽  
Author(s):  
JIE CAO ◽  
HONGKE LU ◽  
WEIWEI WANG ◽  
JIAN WANG
Keyword(s):  
Financial Regulation ◽  
Evaluation Model ◽  
Least Square ◽  
Support Vector ◽  
Accuracy Rate ◽  
Rural Credit ◽  
Proposed Model ◽  
Loan Risk ◽  
Regulation Approach ◽  
Risk Evaluation Model

Five-category loan classification (FCLC) is an international financial regulation approach. Recently, the application and implementation of FCLC in the Chinese microfinance bank has mostly relied on subjective judgment, and it is difficult to control and lower loan risk. In view of this, this paper is dedicated to researching and solving this problem by constructing the FCLC model based on improved particle-swarm optimization (PSO) and the multiclass, least-square, support-vector machine (LS-SVM). First, LS-SVM is the extension of SVM, which is proposed to achieve multiclass classification. Then, improved PSO is employed to determine the parameters of multiclass LS-SVM for improving classification accuracy. Finally, some experiments are carried out based on rural credit cooperative data to demonstrate the performance of our proposed model. The results show that the proposed model makes a distinct improvement in the accuracy rate compared with one-vs.-one (1-v-1) LS-SVM, one-vs.-rest (1-v-r) LS-SVM, 1-v-1 SVM, and 1-v-r SVM. In addition, it is an effective tool in solving the problem of loan-risk rating.

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