Autonomous driving vehicles have some advantages, such as alleviating tasks of drivers and reducing carbon emissions. With the advancement of intelligent network connection technology, autonomous driving vehicles are showing a trend of practicality and popularization, so it is crucial to study the decision-making mechanism of autonomous driving vehicles. This paper focuses on the lane-changing decision-making behavior of autonomous driving vehicles. Firstly, the objective quantification of lane-changing intention is carried out to reasonably show the lane-changing intention of autonomous driving vehicles as a prerequisite for lane-changing decision-making. Besides, the lane-changing collision probability and the lane-changing dynamic risky coefficient are introduced, and explore the dynamic influencing factors of lane-changing process for autonomous driving vehicles. Based on the game theory, the decision-making behavior model of the lane-changing game for autonomous driving vehicles is established. Analyze the decision-making behavior mechanism of lane-changing, so that the autonomous driving vehicle can change lanes safely and reasonably. Finally, with SUMO software, the traditional LC2013 lane-changing model and the decision-making behavior model of the lane-changing game are used for simulation experiments and comparative analysis. The results show that under the decision-making behavior model of the lane-changing game, the average speed of vehicles increases by 3.6%, and the average number of passed vehicles increases by 10.3%, which has higher stability, safety, speed gains, and lane utilization. The modeling of lane-changing game strategy for autonomous driving vehicles comprehensively considers the dynamic factors in the traffic environment, and scientifically shows the lane-changing decision-making mechanism of autonomous driving vehicles.

During signal transitions at road sections and intersections, pedestrians and vehicles often clash and cause traffic accidents due to unclear right-of-way. To solve this problem, a vehicle safety braking distance model considering human–vehicle characteristics is established and applied to the designed crosswalk safety warning system to enable pedestrians to cross the street intelligently. The model developed to consider human–vehicle characteristics improves the parking sight distance and pedestrian crossing safety psychological distance models by adding consideration of the effect of vehicle size and type on pedestrian psychology. The established model considering human–vehicle characteristics was improved for the stopping sight distance and pedestrian crossing safety psychological distance models. The effects of vehicle size and type on pedestrian psychology were taken into account. The designed warning system can be divided into a detection module, control module, warning module, and wireless communication module. The system detects the position and speed of pedestrians and vehicles and discriminates the conflict situation, executing the corresponding warning plan for three different types of situations. The system provides warning to pedestrians and vehicles through the different color displays of the intelligent crosswalk. The results show that the proposed model, which synergistically couples vehicle speed, driver reaction time, road characteristic correlation coefficients, and the psychological impact of vehicle size and type on pedestrians, is safe and effective. The designed system solves the problem of pedestrian crossing safety from both theoretical and technical aspects.

In order to realize the lane-changing maneuver of Connected Autonomous Vehicles (CAV), a lateral controller based on model predictive control is developed with the three degrees of freedom vehicle dynamic model. Then the controller is synthesized to track the reference trajectory fitted by the quintic Bézier curve. The controller is validated by MATLAB/CarSim under different road adhesion conditions and driving speeds. Results show that for different road adhesion conditions and driving speeds, the controller does not need to adjust the control parameters and can continuously correct the deviation from the expected trajectory. During the tracking process, the front wheel angle, front wheel angle increment, centroid side deflection angle, and front wheel side deflection angle are kept within the limited constraint range. The established control algorithm has good control robustness and tracking driving stability. The research can provide a theoretical basis and technical support for lane-changing safety and control of CAV.

With the technical support of an intelligent networking environment, autonomous driving technology is facing a new stage of development, and the decision-making behavior of autonomous vehicles is changing fundamentally, so it is urgent to explore the lane-changing decision-making behavior mechanism of autonomous driving. Firstly, through the analysis of system similarity, the similarity between autonomous vehicles and moving molecules is sought, and the attraction and repulsion between molecules are applied to the lane-changing process of vehicles to effectively recognize the traffic scene of lane-changing vehicles. Secondly, the molecular interaction potential is introduced to unify the attraction and repulsion, and explore the dynamic influencing factors of lane-changing behavior for vehicles. Moreover, we systematically analyze the interaction relationship in the lane-changing process of Connected and Automated Vehicles, and establish the molecular interaction potential lane-changing model to explore the lane-changing decision-making behavior mechanism. Furthermore, we study the impact of micro lane-changing behavior on macro traffic flow. Finally, the SL2015 lane-changing model and the molecular interaction potential lane-changing model are compared and analyzed by using the SUMO platform. The results show that the speed fluctuation of Connected and Automated Vehicles based on the molecular interaction potential lane-changing model is reduced by 15.5%, and the number of passed vehicles is increased by 3.26% on average, which has better safety, stability, and efficiency. The molecular interaction potential modeling of lane-changing decision-making behavior for Connected and Automated Vehicles comprehensively considers the interaction relationship of dynamic factors in the traffic environment, and scientifically shows the lane-changing decision-making mechanism of Connected and Automated Vehicles.

Along with the rapid development of autonomous driving technology, autonomous vehicles are showing a trend of practicality and popularity. Autonomous vehicles perceive environmental information through sensors to provide a basis for the decision making of vehicles. Based on this, this paper investigates the lane-changing decision-making behavior of autonomous vehicles. First, the similarity between autonomous vehicles and moving molecules is sought based on a system-similarity analysis. The microscopic lane-changing behavior of vehicles is analyzed by the molecular-dynamics theory. Based on the objective quantification of the lane-changing intention, the interaction potential is further introduced to establish the molecular-dynamics lane-changing model. Second, the relationship between the lane-changing initial time and lane-changing completed time, and the dynamic influencing factors of the lane changing, were systematically analyzed to explore the influence of the microscopic lane-changing behavior on the macroscopic traffic flow. Finally, the SL2015 lane-changing model was compared with the molecular-dynamics lane-changing model using the SUMO platform. SUMO is an open-source and multimodal traffic experimental platform that can realize and evaluate traffic research. The results show that the speed fluctuation of autonomous vehicles under the molecular-dynamics lane-changing model was reduced by 15.45%, and the number of passed vehicles was increased by 5.93%, on average, which means that it has better safety, stability, and efficiency. The molecular-dynamics lane-changing model of autonomous vehicles takes into account the dynamic factors in the traffic scene, and it reasonably shows the characteristics of the lane-changing behavior for autonomous vehicles.

The core risks of unmanned truck formations in narrow curved sections of open-pit mines mainly include lateral trajectory deviation risk and longitudinal following coordination risk, which can lead to inaccurate heading angle and improper following control. Therefore, a multi-vehicle formation collaborative control method based on LQR and DMPC is proposed. The research objective is to design a collaborative control strategy that is suitable for narrow curved sections of open-pit mines, achieving precise lateral trajectory tracking and safe longitudinal following. Firstly, a multi-vehicle collaborative control scheme is designed by analyzing the transportation conditions. Secondly, the lateral dynamics model and the preview error model are established. Besides, the lateral controller based on feedforward LQR is designed. Then, based on the longitudinal dynamic characteristics of the vehicle, the multi-vehicle formation longitudinal controller based on DMPC is designed. Next, the multi-vehicle formation collaborative controller is established. Finally, the Simulink/TruckSim joint simulation model is constructed, and the unmanned experimental vehicle and open-pit mining experimental platform are designed for conducting simulations and experiments. The simulation results show that collaborative control reduces the lateral error of the following vehicle by 37.20% and the heading angle error by 36.40% when driving downhill with no load. Furthermore, collaborative control reduces the lateral error of the following vehicle by 21.95% and the heading angle error by 39.54% when fully loaded uphill. At the same time, the longitudinal following vehicle maintains a speed error of no more than 0.35 km/h and a spacing error of less than 0.30 m compared to the leading vehicle. The experimental results show that the designed formation controller controls the lateral error of the following vehicle within ±0.035 m and the heading angle error does not exceed 0.15 rad, verifying the effectiveness and adaptability of the studied formation control method.