An integrated control system developed for improving the vehicle handling and stability under critical lateral motions that includes electric-stability-controller (ESC) and rear-wheel-active-steering (RAS) systems and coordinates the ESC and RAS controllers based on the β-β̇ phase plane method is discussed in this paper. The ESC includes a fuzzy logical controller that calculates the yaw moment and an additional rear wheel steering angle based on the vehicle steering states. The RAS control system consists of two parts: a feedback controller and a feed-forward controller. When the vehicle in the normal driving situations, the RAS system provides enhanced handling performance. If the vehicle reaches its handling limits, then both ESC and RAS are integrated to ensure vehicle stability. The simulation results demonstrated that the proposed integrated controller system not only can resist external interference and reduce driver fatigue but also improves both vehicle stability and handling.

Driving while performing secondary tasks is a common phenomenon and can lead to car crashes and human injuries. However, the impact degrees of several secondary tasks have not been determined. To study the importance of several secondary task driving behaviors on driving safety, an eye movement tracking system and a vehicle running state collection system were utilized to determine the laws of behavioral parameters when driving while performing six secondary tasks including Bluetooth calls, cell phone calls, sending text messages, operating car-mounted players, chatting, and singing. Then, an experiment was carried out to collect the evaluation indices of eye movement behaviors and vehicle running statuses. A characteristic extraction method that is based on the gray incidence clustering method was used to extract the characteristic evaluation indices. The generalized gray Euclidean distance was utilized to extract the characteristic evaluation indices. Then, seven characteristic evaluation indices were extracted from 17 evaluation indices. In order to verify the accuracy of the evaluation index system, 17 candidate evaluation indices were screened out to eight by principal component analysis. From this result, the established evaluation index system is all included in eight indices selected by principal component analysis. Furthermore, the characteristic evaluation indices and the six secondary task modes were combined to build the entropy weight decision-making matrix, based on which the weights of each driving behavior mode were determined via the entropy weight method and the impact degrees of the six driving behavior modes on the driving safety were obtained. The results of this research demonstrate that the operation of car-mounted players had the greatest impact on driving safety, whereas making cell phone calls had the least impact.

Distracted driving is a dominant cause of traffic accidents. In addition, with the rapid development of intelligent vehicles, mixed traffic environments are expected to become more complicated with multiple types of intelligent vehicles sharing the road, thereby increasing the opportunities for distracted driving. However, the existing research on detecting driver distraction in mixed traffic environments is limited. Therefore, in this study, we analysed the effect of cognitive distraction on the driver physiological measures and driving performance in traditional and mixed traffic environments and compared the parameters extracted in the two environments. Sixty drivers were involved in the data collection, which included normal driving and two distracting tasks while driving in a simulator. Repeated-measures analysis of variance (ANOVA) was performed to examine the effect of cognitive distraction and traffic environments on all parameters. The results indicate that the effects of the pupil diameter, standard deviations (SDs) of the horizontal and vertical fixation angles, blink frequency, speed, SD of the lane positioning (SDLP), SD of the steering wheel angle (SDSWA), and steering entropy (SE) were significant. These findings provide a theoretical foundation for identifying the most appropriate parameters to detect cognitive distraction in traditional and mixed traffic environments to help reduce traffic accidents.

In this paper, we introduce the preview-follower theory for modeling the trajectory-tracking controller of an automated system using model predictive control (MPC). The primary contribution of this research lies in enhancing tracking accuracy and driving safety when the driver and automated system share similar driving intentions, while also enabling a rapid transfer of driving authority to the human driver in cases of differing intentions. To verify the effectiveness of the proposed driving authority optimization framework, both simulation and driver-in-the-loop experiments were conducted under conditions of consistent and inconsistent driving intentions between the human driver and the autonomous driving system. The results of both experiments demonstrated that the proposed shared steering cooperative control and driving authority optimization framework not only significantly improves vehicle tracking accuracy but also promptly aligns with the driver’s intentions.

Visual-inertial Simultaneous Localization and Mapping (SLAM) technology has received extensive attention in the field of autonomous driving due to its characteristic of not requiring prior information. However, point feature-based SLAM systems are limited in performance in low-texture environments, struggling to extract sufficient reliable feature points, which leads to a decrease in positioning accuracy and even system failure. Although line features have an advantage in low-texture environments, existing line feature matching methods have the following limitations: low matching efficiency, low matching accuracy, and insufficient system robustness. This paper proposes an efficient point-line fusion visual-inertial SLAM system (EPL-VINS) aimed at addressing the aforementioned issues. When applying line features, the following technical challenges need to be overcome: improving the efficiency and accuracy of line feature detection; reducing over-segmentation to decrease mismatching; and enabling line feature tracking to adapt to dynamic scenes. To address these challenges, this paper presents the following innovations: an optimized line feature detector based on short line filtering and similar line fusion techniques, utilizing optical flow features for line feature tracking to enhance tracking speed and accuracy; and the introduction of an adaptive line feature residual to mitigate the negative impact of the sparse number of line features on state estimation. Experiments demonstrate a remarkable 70% improvement in the efficiency of line feature detection and tracking tasks compared to the combined use of the LBD and LSD algorithms on the EuRoc dataset. Furthermore, without loop closure constraints, EPL-VINS outperforms several leading open-source algorithms, showing reduction in RMSE of 25.85% over PL-VINS and 23.82% over PL-VIO on the same dataset. This approach offers a robust solution to the real-time challenges of online feature extraction and tracking, while significantly enhancing the quality and reliability of line features.