Browsing by Author "Na, Xiaoxiang"

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    Identification and analysis of driver postures for in-vehicle driving activities and secondary tasks recognition
    (IEEE, 2018-12-25) Xing, Yang; Lv, Chen; Zhang, Zhaozhong; Wang, Huaji; Na, Xiaoxiang; Cao, Dongpu; Velenis, Efstathios; Wang, Fei-Yue
    Driver decisions and behaviors regarding the surrounding traffic are critical to traffic safety. It is important for an intelligent vehicle to understand driver behavior and assist in driving tasks according to their status. In this paper, the consumer range camera Kinect is used to monitor drivers and identify driving tasks in a real vehicle. Specifically, seven common tasks performed by multiple drivers during driving are identified in this paper. The tasks include normal driving, left-, right-, and rear-mirror checking, mobile phone answering, texting using a mobile phone with one or both hands, and the setup of in-vehicle video devices. The first four tasks are considered safe driving tasks, while the other three tasks are regarded as dangerous and distracting tasks. The driver behavior signals collected from the Kinect consist of a color and depth image of the driver inside the vehicle cabin. In addition, 3-D head rotation angles and the upper body (hand and arm at both sides) joint positions are recorded. Then, the importance of these features for behavior recognition is evaluated using random forests and maximal information coefficient methods. Next, a feedforward neural network (FFNN) is used to identify the seven tasks. Finally, the model performance for task recognition is evaluated with different features (body only, head only, and combined). The final detection result for the seven driving tasks among five participants achieved an average of greater than 80% accuracy, and the FFNN tasks detector is proved to be an efficient model that can be implemented for real-time driver distraction and dangerous behavior recognition.
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    Levenberg-Marquardt backpropagation training of multilayer neural networks for state estimation of a safety critical cyber-physical system
    (IEEE, 2017-11-24) Lv, Chen; Xing, Yang; Zhang, Junzhi; Na, Xiaoxiang; Li, Yutong; Liu, Teng; Cao, Dongpu; Wang, Fei-Yue
    As an important safety critical cyber-physical system (CPS), the braking system is essential to the safe operation of the electric vehicle. Accurate estimation of the brake pressure is of great importance for automotive CPS design and control. In this paper, a novel probabilistic estimation method of brake pressure is developed for electrified vehicles based on multilayer Artificial Neural Networks (ANN) with Levenberg-Marquardt Backpropagation (LMBP) training algorithm. Firstly, the high-level architecture of the proposed multilayer ANN for brake pressure estimation is illustrated. Then, the standard backpropagation (BP) algorithm used for training of the feed-forward neural network (FFNN) is introduced. Based on the basic concept of backpropagation, a more efficient training algorithm of LMBP method is proposed. Next, real vehicle testing is carried out on a chassis dynamometer under standard driving cycles. Experimental data of the vehicle and the powertrain systems are collected, and feature vectors for FFNN training collection are selected. Finally, the developed multilayer ANN is trained using the measured vehicle data, and the performance of the brake pressure estimation is evaluated and compared with other available learning methods. Experimental results validate the feasibility and accuracy of the proposed ANN-based method for braking pressure estimation under real deceleration scenarios.
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    Milestones in autonomous driving and intelligent vehicles: survey of surveys
    (IEEE, 2022-11-24) Chen, Long; Li, Yuchen; Huang, Chao; Li, Bai; Xing, Yang; Tian, Daxin; Li, Li; Hu, Zhongxu; Na, Xiaoxiang; Li, Zixuan; Teng, Siyu; Lv, Chen; Wang, Jinjun; Cao, Dongpu; Zheng, Nanning; Wang, Fei-Yue
    Interest in autonomous driving (AD) and intelligent vehicles (IVs) is growing at a rapid pace due to the convenience, safety, and economic benefits. Although a number of surveys have reviewed research achievements in this field, they are still limited in specific tasks, lack of systematic summary and research directions in the future. Here we propose a Survey of Surveys (SoS) for total technologies of AD and IVs that reviews the history, summarizes the milestones, and provides the perspectives, ethics, and future research directions. To our knowledge, this article is the first SoS with milestones in AD and IVs, which constitutes our complete research work together with two other technical surveys. We anticipate that this article will bring novel and diverse insights to researchers and abecedarians, and serve as a bridge between past and future.