This paper presents a drowsiness on-line detection system for monitoring driver fatigue level under real driving conditions, based on the data of steering wheel angles (SWA) collected from sensors mounted on the steering lever. The proposed system firstly extracts approximate entropy (ApEn)featuresfromfixedslidingwindowsonreal-timesteeringwheelanglestimeseries. Afterthat, this system linearizes the ApEn features series through an adaptive piecewise linear fitting using a given deviation. Then, the detection system calculates the warping distance between the linear features series of the sample data. Finally, this system uses the warping distance to determine the drowsiness state of the driver according to a designed binary decision classifier. The experimental data were collected from 14.68 h driving under real road conditions, including two fatigue levels: “wake” and “drowsy”. The results show that the proposed system is capable of working online with an average 78.01% accuracy, 29.35% false detections of the “awake” state, and 15.15% false detections of the “drowsy” state. The results also confirm that the proposed method based on SWA signal is valuable for applications in preventing traffic accidents caused by driver fatigue.

Fatigued driving is a major cause of road accidents. For this reason, the method in this paper is based on the steering wheel angles (SWA) and yaw angles (YA) information under real driving conditions to detect drivers’ fatigue levels. It analyzes the operation features of SWA and YA under different fatigue statuses, then calculates the approximate entropy (ApEn) features of a short sliding window on time series. Using the nonlinear feature construction theory of dynamic time series, with the fatigue features as input, designs a “2-6-6-3” multi-level back propagation (BP) Neural Networks classifier to realize the fatigue detection. An approximately 15-h experiment is carried out on a real road, and the data retrieved are segmented and labeled with three fatigue levels after expert evaluation, namely “awake”, “drowsy” and “very drowsy”. The average accuracy of 88.02% in fatigue identification was achieved in the experiment, endorsing the value of the proposed method for engineering applications.

In the field of advanced driver assistance systems (ADAS), effective learning of driver fatigue characteristics representation is a major challenge due to uncertainties of both real roads and drivers. To tackle this problem, this paper proposes a novel model of learning interpretable representations for fatigue features, so as to improve the performance of the monitoring system on real roads through the learned features. First, the approximate entropy of the Steering Wheel Angle (SWA)sequence is used to crop adaptive lengths of the Recurrent Neural Network (RNN) input data. Then, it will learn statistical indexes to discover the random steering characteristics. The Long Short-Term Memory (LSTM) unit can memorize drivers’ long-term operating characteristics and instantaneous change patterns, and mine their fatigue characteristics. Finally, the information gain method is used to discover the strong correlation between potential characteristics and fatigue levels, thereby obtaining the best feature representation for driver fatigue. The proposed method makes use of the advantages of recurrent neural network learning to explicitly capture various potential characteristics of fatigue driving and interactions between non-linear characteristics in driving. Experimental results on real driving data validate the effectiveness of our proposed method.

Inductive power transfer and capacitive power transfer (CPT) are two of the most commonly used near-field wireless power transfer technologies, and these two systems share similarities and symmetries. This article proposes a hybrid wireless power transfer (HWPT) system to achieve load-independent characteristic and anti-misalignment capability. The HWPT system utilizes the compensation inductors of the CPT system to create an additional inductive channel in which the capacitive and inductive channels are compensated for each other. Based on the modeling of the HWPT system, the load-independent and enhanced constant current output is derived. In HWPT system, the inductive coupler is employed with the capacitive coupler, which forms a compact hybrid coupler. As the coupler becomes misaligned, the loss of power transferred through the inductive channel can be compensated by the increase of power transferred through the capacitive channel. A 200 W experimental prototype is built to validate the performance of the HWPT system. Experimental results show that as the load resistance ranges from 20 to 40 $\Omega$ and the coupling misalignment ranges from 0 to 100 mm, the output current remains nearly constant and the input current stays in phase with the input voltage. According to the experimental findings, the output current of the HWPT system is approximately twice as high as that of the CPT system when using the same capacitive coupler.

The study of the robust fatigue feature learning method for the driver’s operational behavior is of great significance for improving the performance of the real-time detection system for driver’s fatigue state. Aiming at how to extract more abstract and deep features in the driver’s direction operation data in the robust feature learning, this article constructs a fuzzy recurrent neural network model, which includes input layer, fuzzy layer, hidden layer, and output layer. The steering-wheel direction sensing time series sends the time series to the input layer through a fixed time window. After the fuzzification process, it is sent to the hidden layer to share the weight of the hidden layer, realize the memorization of the fatigue feature, and improve the feature depth capability of the steering wheel angle time sequence. The experimental results show that the proposed model achieves an average recognition rate of 87.30% in the fatigue sample database of real vehicle conditions, which indicates that the model has strong robustness to different subjects under real driving conditions. The model proposed in this article has important theoretical and engineering significance for studying the prediction of fatigue driving under real driving conditions.

Driver fatigue recognition is a highly challenging issue because of the complexity of road conditions, the dynamics of traffic flow, and the differences between drivers. This article proposes a biologically inspired long short-term memory (LSTM) model with neural plasticity (NP-LSTM) to improve the learning and memory ability of the traditional driver fatigue recognition method, thus improving effectiveness and robustness of monitoring and early-warning systems. First, the approximate entropy (ApEn) of the time series of drivers’ operation behaviors and vehicle status is investigated to explore the features of potential irregularity in fatigue-driving behaviors; then, inspired by the plastic learning mechanism of biological neurons, the intrinsic plasticity and synaptic plasticity are embedded into the LSTM neural network to realize the classified storage of complex road patterns, the dynamics of traffic flow, and the memory of drivers’ individual differences; finally, the dropout technology is introduced to further build a “sparse” neural network, which avoids the repeated training of an unchanged neural network under different conditions and enhances the adaptability and generalization of the whole monitoring and early-warning system. Experimental study on real roads is conducted to demonstrate the effectiveness of the proposed method. The results show that the average recognition accuracy is 88.73%, demonstrating a better recognition performance of the proposed method.