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Developing a human-like autonomous driving system has gained increasing amounts of attention from both technology companies and academic institutions, as it can improve the interpretability and acceptance of the autonomous system. Planning a safe and human-like obstacle avoidance trajectory is one of the critical issues for the development of autonomous vehicles (AVs). However, when designing automatic obstacle avoidance systems, few studies have focused on the obstacle avoidance characteristics of human drivers. This paper aims to develop an obstacle avoidance trajectory planning and trajectory tracking model for AVs that is consistent with the characteristics of human drivers’ obstacle avoidance trajectory. Therefore, a modified artificial potential field (APF) model was established by adding a road boundary repulsive potential field and ameliorating the obstacle repulsive potential field based on the traditional APF model. The model predictive control (MPC) algorithm was combined with the APF model to make the planning model satisfy the kinematic constraints of the vehicle. In addition, a human driver’s obstacle avoidance experiment was implemented based on a six-degree-of-freedom driving simulator equipped with multiple sensors to obtain the drivers’ operation characteristics and provide a basis for parameter confirmation of the planning model. Then, a linear time-varying MPC algorithm was employed to construct the trajectory tracking model. Finally, a co-simulation model based on CarSim/Simulink was established for off-line simulation testing, and the results indicated that the proposed trajectory planning controller and the trajectory tracking controller were more human-like under the premise of ensuring the safety and comfort of the obstacle avoidance operation, providing a foundation for the development of AVs.

This paper presents a new nonlinear non-intrusive reduced-order model (NL-NIROM) that outperforms traditional proper orthogonal decomposition (POD)-based reduced order model (ROM). This improvement is achieved through the use of auto-encoder (AE) and self-attention based deep learning methods. The novelty of this work is that it uses stacked auto-encoder (SAE) network to project the original high-dimensional dynamical systems onto a low dimensional nonlinear subspace and predict fluid dynamics using an self-attention based deep learning method. This paper introduces a new model reduction neural network architecture for fluid flow problem, as well as, a linear non-intrusive reduced order model (L-NIROM) based on POD and self-attention mechanism. In the NL-NIROM, the SAE network compresses high-dimensional physical information into several much smaller sized representations in a reduced latent space. These representations are expressed by a number of codes in the middle layer of SAE neural network. Then, those codes at different time levels are trained to construct a set of hyper-surfaces using self-attention based deep learning methods. The inputs of the self-attention based network are previous time levels' codes and the outputs of the network are current time levels' codes. The codes at current time level are then projected back to the original full space by the decoder layers in the SAE network. The capability of the new model, NL-NIROM, is demonstrated through two test cases: flow past a cylinder, and a lock exchange. The results show that the NL-NIROM is more accurate than the popular model reduction method namely POD based L-NIROM.

Nowadays, maritime transportation has become one of the most important ways of international trade. However, with the increase in ship transportation, the complex maritime environment has led to frequent traffic accidents, causing huge economic losses and safety hazards. For ships in maritime transportation, collision avoidance and route planning can be achieved by predicting the ship’s trajectory, which can give crews warning to avoid dangers. How to predict the ship’s trajectory more accurately is of great significance for risk avoidance. However, existing ship trajectory prediction models suffer from problems such as poor prediction accuracy, poor applicability, and difficult hyperparameter design. To address these issues, this paper adopts the Bidirectional Long Short-Term Memory (BILSTM) model as the base model, as it considers contextual information of time-series data more comprehensively. Meanwhile, to improve the accuracy and fitness of complex ship trajectories, this paper adds an attention mechanism to the BILSTM model to improve the weight of key information. In addition, to solve the problem of difficult hyperparameter design, this paper optimizes the hyperparameters of the Attention-BILSTM network by fusing the Whale Optimization Algorithm (WOA). In this paper, the AIS data are filtered, and the trajectory is complemented by the cubic spline interpolation method. Using the pre-processed AIS data, this WOA-Attention-BILSTM model is compared and assessed with traditional models. The results show that compared with other models, the WOA-Attention-BILSTM prediction model has high prediction accuracy, high applicability, and high stability, which provides an effective and feasible method for ship collision avoidance, maritime surveillance, and intelligent shipping.

“Port–hinterland synergy” means the development of port and hinterland should promote each other. The “dual circulation” development pattern indicates the requirement of exploring the domestic transportation demand and promoting the integration between ports and hinterlands. However, the current research on the synergy level between ports and hinterlands is not enough to meet the needs of constructing a “dual circulation” development pattern, and few studies have explored the influencing factors of port–hinterland synergy level directly, especially in the context of the new development pattern of “dual circulation”. After investigating the synergetic mechanism between ports and hinterlands, this study proposes to further consider the influence of fixed assets allocation and social commodity circulation on the synergy level under the “dual circulation” pattern. So, fixed asset investment and three different forms of commodity circulation activities are selected to represent the corresponding hinterland’s economic activities and added into the evaluation indices. To assess ports’ responsiveness to different kinds of transport demand, throughputs of each port are divided into those of domestic and foreign countries. Then this paper evaluates the level of port–hinterland synergy by the coupling synergy model, and the influence degree of these activities on the synergy level was studied with the partial least squares regression (PLS). The results show that there is heterogeneity in regional and port positioning in the port–hinterland synergy level, and that four selected economic activities’ improvement can enhance the port–hinterland synergy level. Among them, retail industry has the strongest positive effect, followed by tertiary industry, import and export trade, and fixed asset investment.