Considering the uncertainty of tyre cornering stiffness due to the frequent variation of running conditions, a new coordination scheme is proposed based on optimal guaranteed cost control technique by coordinating active front steering and direct yaw moment control. A general procedure to develop an optimal guaranteed cost coordination controller (OGCC) is presented, and the effect of uncertainty deviation magnitude on the control system is discussed. An optimal coordination (OC) scheme based on LQR is also presented. Many simulations are carried out on an 8-DOF nonlinear vehicle model for a slalom manoeuvre and a lane-change manoeuvre, respectively. The simulation results show that the OGCC scheme has superior stability and tracking performances at different running conditions compared with the OC scheme.

Driver drowsiness is a frequent cause of traffic accidents. Research on driver drowsiness detection methods is important to improve road traffic safety. Previous driving fatigue detection methods frequently extracted single features such as eye or mouth changes and trained shallow classifiers, which limit the generalisation capability of these methods. This study proposes a framework for recognising driver drowsiness expression by using facial dynamic fusion information and a deep belief network (DBN) to address the aforementioned problem. First, the landmarks and textures of the facial region are extracted from videos captured using a high‐definition camera. Then, a DBN is built to classify facial drowsiness expressions. Finally, the authors’ method is tested on a driver drowsiness dataset, which includes different genders, ages, head poses and illuminations. Certain experiments are also carried out to investigate the effects of different facial subregions and temporal resolutions on the accuracy of driver fatigue recognition. Results demonstrate the validity of the proposed method, which has an average accuracy of 96.7%.

In this paper, a novel adaptive cruise control (ACC) algorithm based on model predictive control (MPC) and active disturbance rejection control (ADRC) is proposed. This paper uses an MPC algorithm for the upper controller of the ACC system. Through comprehensive considerations, the upper controller will output desired acceleration to the lower controller. In addition, to increase the accuracy of the predictive model in the MPC controller and to address fluctuations in the vehicle’s acceleration, an MPC aided by predictive estimation of acceleration is proposed. Due to the uncertainties of vehicle parameters and the road environment, it is difficult to establish an accurate vehicle dynamic model for the lower-level controller to control the throttle and brake actuators. Therefore, feed-forward control based on a vehicle dynamic model (VDM) and compensatory control based on ADRC is used to enhance the control precision and to suppress the influence of internal or external disturbance. Finally, the proposed optimal design of the ACC system was validated in road tests. The results show that ACC with APE can accurately control the tracking of the host vehicle with less acceleration fluctuation than that of the traditional ACC controller. Moreover, when the mass of the vehicle and the slope of the road is changed, the ACC–APE–ADRC controller is still able to control the vehicle to quickly and accurately track the desired acceleration.

The effective detection of driver drowsiness is an important measure to prevent traffic accidents. Most existing drowsiness detection methods only use a single facial feature to identify fatigue status, ignoring the complex correlation between fatigue features and the time information of fatigue features, and this reduces the recognition accuracy. To solve these problems, we propose a driver sleepiness estimation model based on factorized bilinear feature fusion and a long- short-term recurrent convolutional network to detect driver drowsiness efficiently and accurately. The proposed framework includes three models: fatigue feature extraction, fatigue feature fusion, and driver drowsiness detection. First, we used a convolutional neural network (CNN) to effectively extract the deep representation of eye and mouth-related fatigue features from the face area detected in each video frame. Then, based on the factorized bilinear feature fusion model, we performed a nonlinear fusion of the deep feature representations of the eyes and mouth. Finally, we input a series of fused frame-level features into a long-short-term memory (LSTM) unit to obtain the time information of the features and used the softmax classifier to detect sleepiness. The proposed framework was evaluated with the National Tsing Hua University drowsy driver detection (NTHU-DDD) video dataset. The experimental results showed that this method had better stability and robustness compared with other methods.

This paper proposes novel framework for facial expressions analysis using dynamic and static information in video sequences. First, based on incremental formulation, discriminative deformable face alignment method is adapted to locate facial points to correct in‐plane head rotation and break up facial region from background. Then, spatial‐temporal motion local binary pattern (LBP) feature is extracted and integrated with Gabor multiorientation fusion histogram to give descriptors, which reflect static and dynamic texture information of facial expressions. Finally, a one‐versus‐one strategy based multiclass support vector machine (SVM) classifier is applied to classify facial expressions. Experiments on Cohn‐Kanade (CK) + facial expression dataset illustrate that integrated framework outperforms methods using single descriptors. Compared with other state‐of‐the‐art methods on CK+, MMI, and Oulu‐CASIA VIS datasets, our proposed framework performs better.