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Road traffic accidents, especially those accidents with multiple-vehicle collision usually cause injuries and mortalities. Currently, studies on collision avoidance mainly focus on the control strategies for adjacent two vehicles or multiple vehicles in a single platoon direction. This paper proposes a bi-directional collision avoidance system for multiple vehicles to minimize the collision risk under the model predictive control (MPC) framework through switching the vehicle-following mode based on the inter-vehicular states. A hierarchical structure with an upper layer and a lower layer is designed. A dual-operational mode switching strategy and the vehicle-following model are formulated in the upper layer, together with the development of the acceleration redistribution cooperative strategy for vehicle platoon. While the lower layer is designed to track the desired acceleration received from the upper layer by considering the practical situation of the control commands. To tackle complex transitional operation, a dynamic weighted tuning strategy is proposed and integrated it with the MPC. The numerical results show that the proposed system outperforms the conventional collision avoidance system and is effective to avoid a collision or minimize the total impact of the vehicle platoon when the collision is unavoidable.

Nowadays, with more and more attention being paid to the characteristics and experience of drivers, a large number of driver classification algorithms have emerged. However, these methods basically cannot be adjusted independently to each driver. Therefore, this paper proposes a self-learning lane change motion planning system considering the driver’s personality. Firstly, the method of driver data acquisition and processing is determined to obtain and extract the lane change data. Then, the planning system built in this paper is explained from two aspects: lane change trigger and lane change trajectory. According to the artificial potential field theory, an obstacle driving risk field is established to evaluate the acceptance of environmental risks of different drivers, and to achieve personalized lane change triggers through online statistics. At the same time, the safety of lane change is ensured by establishing the safety distance model of the target lane. On the other hand, the driver characteristic coefficient Jc and the driver reaction and operation time td are introduced into the traditional Gaussian-distributed model to establish a personalized lane change trajectory planning model, in which the parameters are obtained from offline and online learning. Offline learning is based on DTW for trajectory matching, and uses AP clustering to obtain the generalized parameters; Online learning uses LSTM to achieve personalized updates. Finally, this paper selected 15 drivers for verification, and the results show that the motion planning system can well reproduce the lane change behavior of the driver.

This article presents a novel adaptive-event-trigger-based fuzzy nonlinear lateral dynamic control algorithm for autonomous electric vehicles under insecure communication networks. The integration of path following control and direct yaw moment control is considered to acquire a better lateral dynamic performance. To effectively tackle inherent nonlinearities of lateral dynamics, the Takagi–Sugeno fuzzy model approach is employed to approximate the nonlinear lateral dynamics. To avoid the infeasibility of state-feedback control due to the immeasurability of sideslip angle, a fuzzy output feedback control strategy is proposed via a useful transformation. To improve resource utilization of the band-limited communication networks, an adaptive event-triggered communication mechanism is proposed to save communication resource. Moreover, to enhance the robustness of the proposed controller, the insecure communication link is considered, which is described by the Bernoulli random distributed process. Finally, simulation results show the proposed adaptive-event-trigger-based fuzzy controller not only can save communication resource, but also achieve the perfect control performance.

Early studies on intelligent control methods for automatic starting control for vehicles mainly focus on traditional parametric adjustment. However, attempts toward the combination of learning algorithms and models are rare. When drivers’ starting behavior is not considered and tedious parameters are merely used for drive control, the effects result in discomfort for drivers. Therefore, to imitate drivers’ starting behavior when dealing with automatic drive control in vehicles, we must first develop an acceleration fitting based on DFT by analyzing the starting characteristics. Then, we design an iterative learning algorithm to achieve automatic starting control of vehicles. Finally, a simulation test is conducted based on CARSIM to verify the validity and feasibility of the proposed method.

This study presents a biosignal based driving experience analysis of an actual-vehicle experiment. A total of 10 subjects were enrolled during the experimental study. Based on their driving mileages per year, subjects were divided into novice and skilled ones at first, and then electromyography (EMG) signals of upper trapezius and sternocleidomastoid muscles were acquired continuously to evaluate a subject response to dynamic motions of the vehicle during the curve, with a driving speed of 30, 40 and 50 km/h respectively. Meanwhile, an EMG evaluation index of a normalized root mean square (RMS) was proposed to reflect the variance of EMG signals. From the experimental results, the RMS based evaluation of right upper trapezius muscle were significantly different between novice and skilled drivers, while driving with a higher speed on curve road. In addition, the RMS based evaluation of right sternocleidomastoid muscle were significantly different between novice and skilled drivers, while driving with a higher or a lower speed on curved road. It indicate that the skilled driver may have a better driving experience than that of novice ones for most curve driving conditions.