Huarong Xu

Bio: Huarong Xu is an academic researcher from Xiamen University of Technology. The author has an hindex of 1, co-authored 1 publications receiving 6 citations.

Driving Behavior Analysis of Multiple Information Fusion Based on AdaBoost

Abstract: With the increase in the number of private cars as well as the non-professional drivers, the current traffic environment is in urgent need of driving assist equipment to timely reminder and to rectify the incorrect driving behavior. In order to meet this requirement, this paper proposes an innovative algorithm of driving behavior analysis based on AdaBoost with a variety of driving operation and traffic information. The proposed driving behavior analysis algorithm will mainly monitor driver’s driving operation behavior, including steering wheel angle, brake force, and throttle position. To increase the accuracy of driving behavior analysis, the proposed algorithm also takes road conditions into account. The proposed will make use of AdaBoost to create a driving behavior classification model in various different road conditions, and then could determine whether the current driving behavior belongs to safe driving. Experimental results show the correctness of the proposed driving behavior analysis algorithm can achieve average 80% accuracy in various driving simulations. The proposed algorithm has the potential of applying to real-world driver assistance system.

Online classifier adaptation for cost-sensitive learning

Abstract: In this paper, we propose the problem of online cost-sensitive classifier adaptation and the first algorithm to solve it. We assume that we have a base classifier for a cost-sensitive classification problem, but it is trained with respect to a cost setting different to the desired one. Moreover, we also have some training data samples streaming to the algorithm one by one. The problem is to adapt the given base classifier to the desired cost setting using the steaming training samples online. To solve this problem, we propose to learn a new classifier by adding an adaptation function to the base classifier, and update the adaptation function parameter according to the streaming data samples. Given an input data sample and the cost of misclassifying it, we update the adaptation function parameter by minimizing cost-weighted hinge loss and respecting previous learned parameter simultaneously. The proposed algorithm is compared to both online and off-line cost-sensitive algorithms on two cost-sensitive classification problems, and the experiments show that it not only outperforms them on classification performances, but also requires significantly less running time.

Analysis of the Performance of Machine Learning Models in Predicting the Severity Level of Large-Truck Crashes

Abstract: Large-truck crashes often result in substantial economic and social costs. Accurate prediction of the severity level of a reported truck crash can help rescue teams and emergency medical services take the right actions and provide proper medical care, thereby reducing its economic and social costs. This study aims to investigate the modeling issues in using machine learning methods for predicting the severity level of large-truck crashes. To this end, six representative machine learning (ML) methods, including four classification tree-based ML models, specifically the Extreme Gradient Boosting tree (XGBoost), the Adaptive Boosting tree (AdaBoost), Random Forest (RF), and the Gradient Boost Decision Tree (GBDT), and two non-tree-based ML models, specifically Support Vector Machines (SVM) and k-Nearest Neighbors (k-NN), were selected for predicting the severity level of large-truck crashes. The accuracy levels of these six methods were compared and the effects of data-balancing techniques in model prediction performance were also tested using three different resampling techniques: Undersampling, oversampling, and mix sampling. The results indicated that better prediction performances were obtained using the dataset with a similar distribution to the original sample population instead of using the datasets with a balanced sample population. Regarding the prediction performance, the tree-based ML models outperform the non-tree-based ML models and the GBDT model performed best among all of the six models.