Book ChapterDOI
Driving Behavior Analysis of Multiple Information Fusion Based on AdaBoost
Shi-Huang Chen,Jeng-Shyang Pan,Kaixuan Lu,Huarong Xu +3 more
- pp 277-285
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TLDR
An innovative algorithm of driving behavior analysis based on AdaBoost with a variety of driving operation and traffic information to monitor driver’s driving operation behavior, including steering wheel angle, brake force, and throttle position is proposed.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.read more
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Journal ArticleDOI
The application of machine learning techniques for driving behavior analysis: A conceptual framework and a systematic literature review
TL;DR: A conceptual framework is outlined whereby DB is viewed in terms of different dimensions established within the Driver–Vehicle–Environment (DVE) system, and an interpretive framework incorporating multiple dimensions influencing the driver’s conduct is identified.
Journal ArticleDOI
Online classifier adaptation for cost-sensitive learning
Junlin Zhang,Jose Garcia +1 more
TL;DR: 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.
Journal ArticleDOI
Data fusion for ITS: A systematic literature review
TL;DR: In this paper , a systematic literature review on recent data fusion methods and extracts the main issues and challenges of using these techniques in intelligent transportation systems (ITS) is presented, and the review outcomes are a description of the current Data fusion methods that adopt multi-sensor sources of heterogeneous data under different evaluation strategies, identifying several research gaps, current challenges, and new research trends.
Journal ArticleDOI
Analysis of the Performance of Machine Learning Models in Predicting the Severity Level of Large-Truck Crashes
TL;DR: In this paper , 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), were selected for predicting the severity level of large-truck crashes.
References
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Proceedings ArticleDOI
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