Background: Antenatal depression (AD) is a major depressive disorder during pregnancy, which may lead to devastating sequelae for the expectant mothers and infants. This study aimed to investigate the prevalence, to analyze trajectory model based on EPDS score, and to explore the influence factors of AD among pregnant women in Chengdu, China. Methods: Participants from four maternity hospitals in Chengdu, China were recruited when they had their first pregnancy medical check-up during March 2019 to May 2020. All participants were required to fill in Edinburgh Postnatal Depression Scale Chinese version (EPDS) once during three trimesters and provided information about their health status, social-demographic etc. The trajectory model, chi-square test and multivariate binary logistic regression were used to analyze all collected data. Results: A total of 4560 pregnant women were recruited, while 1051 women completed the study. The prevalence of depression symptoms during the first, second and third trimesters were 32.92% (346/1051), 19.79% (208/1051) and 20.46% (215/1051) respectively. According to the results of the latent growth mixture modeling, the trajectory model of three categories based on EPDS score were identified in this study: low-risk group (38.2%, 401/1051), medium-risk group (54.8%, 576/1051) and high-risk group (7%, 74/1051). Good marital relationship (P = 0.007, OR = 0.33, 95% CI 0.147 ~ 0.74), good relationship with parents-in-law (P = 0.011, OR = 0.561, 95% CI 0.36 ~ 0.874), planned pregnancy (P = 0.018, OR = 0.681, 95% CI 0.496 ~ 0.936) were the protective factors while lower education level (P = 0.036, OR = 1.355, 95% CI 1.02 ~ 1.799), fear about dystocia (P = 0.0, OR = 1.729, 95% CI 1.31 ~ 2.283), recent major negative life events (P = 0.033, OR = 2.147, 95% CI 1.065 ~ 4.329) were the risk factors of medium-risk group. Good marital relationship (P = 0.005, OR = 0.2, 95% CI 0.065 ~ 0.615), good relationship with parents-in-law (P = 0.003, OR = 0.319, 95% CI 0.15 ~ 0.679) were also protective factors of high-risk group, but the risk factors for high-risk group were medical history (P = 0.046, OR = 1.836, 95% CI 1.011 ~ 3.334), pregnancy complications (P = 0.022, OR = 2.015, 95% CI 1.109 ~ 3.662), worry about dystocia (P = 0.003, OR = 2.365, 95% CI 1.347 ~ 4.153), recent major negative life events (P = 0.011, OR = 3.661, 95% CI 1.341 ~ 9.993). No protective or risk factors were identified for low-risk group. Conclusion: Even the incidence and levels of depression in the first trimester of pregnancy were the highest, the probability of pregnancy women get depression during gestation period were higher than other population. Therefore, it’s important to monitor the psychological status of pregnant women during the whole pregnancy, especially in the first trimester. The study suggested a good partner relationship and good relations with parents-in-law both protected pregnant women from depression and promoted the well-being of mothers and children.

Vehicle handing stability under the combined acceleration steering and braking steering conditions has a great impact on the vehicle safety. Therefore, the coupled steering and driving/braking stability control is essential. Thus, a novel controller based on differential flatness and vehicle stability region is proposed. First, the reference vehicle state variables are calculated and the vehicle stability region is established based on vehicle dynamics and bifurcation analysis. Then, the flatness-based controller is designed which can ensure the vehicle handing stability by controlling the vehicle states approaching their references. Based on the driver preview model, the vehicle can keep good path tracking effect by tracking the lateral deviation. The simulation results of the double lane change tests show that the proposed control method can ensure both the vehicle handing stability and the path tracking effect. Compared to vehicle equipped with the integration of active front steering and direct yaw moment control, vehicle controlled by flatness-based controller performs better in both handing stability and path tracking.

Environmental perception systems can provide information on the environment around a vehicle, which is key to active vehicle safety systems. However, these systems underperform in cases of sloped roads. Real-time obstacle detection using monocular vision is a challenging problem in this situation. In this study, an obstacle detection and distance measurement method for sloped roads based on Vision-IMU based detection and range method (VIDAR) is proposed. First, the road images are collected and processed. Then, the road distance and slope information provided by a digital map is input into the VIDAR to detect and eliminate false obstacles (i.e., those for which no height can be calculated). The movement state of the obstacle is determined by tracking its lowest point. Finally, experimental analysis is carried out through simulation and real-vehicle experiments. The results show that the proposed method has higher detection accuracy than YOLO v5s in a sloped road environment and is not susceptible to interference from false obstacles. The most prominent contribution of this research work is to describe a sloped road obstacle detection method, which is capable of detecting all types of obstacles without prior knowledge to meet the needs of real-time and accurate detection of slope road obstacles.

This article aims to explore an effective method for reducing vehicle collisions at unsignalized intersections. First, a monocular-binocular vision switching system is built to enable machine vision-based detection of obstacle vehicles in the left and right front directions. Then, the motion state and trajectory of each obstacle vehicle are predicted, and the intersection points of the trajectories of the obstacle vehicle and the ego vehicle are calculated. On this basis, a cross-conflict judgment model based on trajectories and collision times and a safety assessment model based on safety distance are established. Finally, the conflict judgment and safety assessment for the obstacle vehicles are simulated. The results of the simulation demonstrate that the monocular-binocular vision switching system proposed in this article can achieve a detection accuracy of 95%, a ranging accuracy of 96%, and a cross-conflict detection accuracy of 97%, while ensuring a maximum detection area, which can meet the requirements of traffic safety assurance at unsignalized intersections.

As the adoption of electric vehicles (EVs) continues to rise, the pressure on charging station resources has intensified, particularly under high-load conditions, where limited charging infrastructure struggles to meet the growing demand. Issues such as uneven resource allocation, prolonged charging wait times, fairness concerns among different user groups, and inefficient scheduling strategies have significantly impacted the overall operational efficiency of charging infrastructure and the user experience. Against this backdrop, the effective management of charging infrastructure has become increasingly critical, especially in balancing the diverse mobility needs and service expectations of users. Traditional charging scheduling methods often rely on static or rule-based strategies, which lack the flexibility to adapt to dynamic load environments. This rigidity hinders optimal resource allocation, leading to low charging pile utilization and reduced charging efficiency for users. To address this, we propose an Adaptive Charging Priority (ACP) strategy aimed at enhancing charging resource utilization and improving user experience. The key innovations include (1) dynamic adjustment of priority parameters for optimized resource allocation; (2) a dynamic charging station reservation algorithm based on load status and user arrival rates to prioritize high-priority users; (3) a scheduling strategy for low-priority vehicles to minimize waiting times for non-reserved vehicles; and (4) integration of real-time data with the DDPDQN algorithm for dynamic resource allocation and user matching. Simulation results indicate that the ACP strategy outperforms the FIFS and RFWDA strategies under high-load conditions (High-priority vehicle arrival rate: 22 EV/h, random vehicle arrival rate: 13 EV/h, maximum parking duration: 1200 s). Specifically, the ACP strategy reduces charging wait times by 96 s and 28 s, respectively, and charging journey times by 452 s and 73 s. Additionally, charging station utilization increases by 19.5% and 11.3%. For reserved vehicles, the ACP strategy reduces waiting times and journey times by 27 s and 188 s, respectively, while increasing the number of fully charged vehicles by 104. For non-reserved vehicles, waiting and journey times decrease by 213 s and 218 s, respectively, with a 75 s increase in fully charged vehicles. Overall, the ACP strategy outperforms traditional methods across several key metrics, demonstrating its advantages in resource optimization and scheduling.