Decision-making and planning are the core aspects of autonomous driving systems. These factors are crucial for improving the safety, driving experience, and travel efficiency of autonomous vehicles. Intersections are crucial nodes in urban road traffic networks. The objective of this study is to comprehensively review the latest issues and research progress in decision-making and planning for autonomous vehicles in intersection environments. This paper reviews the research progress in the behavioral prediction of traffic participants in terms of machine learning-based behavioral prediction, probabilistic model behavioral prediction, and mixed-method behavioral prediction. Then, behavioral decision-making is divided into reactive decision-making, learning decision-making, and interactive decision-making, each of which is analyzed. Finally, a comparative analysis of motion planning and its applications is performed from a methodological viewpoint, including search, sampling, and numerical methods. First, key issues and major research progress related to end-to-end decision-making and path planning are summarized and analyzed. Second, the impact of decision-making and path planning on the intelligence level of autonomous vehicles in intersecting environments is discussed. Finally, future development trends and technical challenges are outlined.

It has been proven that exploring how to achieve an efficient transportation system is a crucial component of every sustainable transportation study. Rail-water intermodal transportation is recognized as one of the future transportation methods for being efficient, economical and environmentally friendly. To improve the efficiency, reduce transportation costs and maximize the resource utilization of outbound intermodal container transportation, based on the relationship between the container central station and the port station in the actual problems, the organization of railway container transportation was studied. A multi-objective optimization model was established in order to minimize the total cost in the process of transportation, which means maximizing the resource utilization and ensuring it is environmentally friendly. Additionally, an improved genetic algorithm (GA) was developed to solve the model. The calculation results of the model are obtained by the simulation calculation. The comparison with the conventional fixed axis transportation organization method proves that the model and algorithm can reduce costs by up to 24.57%. The result also shows that the container transport organization should be tried to satisfy the direct loading and discharging condition of “train-ship,” meanwhile reducing the storage time at the high toll central station. In conclusion, the model and algorithm are feasible and effective. Due to the universality of the model, it can be easily used and generalized in or out of China.

A traffic survey using an unmanned aerial vehicle on the Haixia road in Chongqing, China found a significant correlation between the velocity of the vehicle and the distance of the vehicle when moving forward and laterally. To mitigate driving disruptions owing to vehicle intrusion at a desired distance, personal space (PS) is introduced to analyze the car-following behavior of continuous mixed traffic flow formed by human-driven (HD) vehicles and cooperative adaptive cruise control (CACC) vehicles. PS is a virtual boundary that refers to the space where psychological tension is caused by the invasion of others. Further, an intelligent driver model (IDM) was used to establish a mixed traffic car-following PS-IDM. The stability of the disturbance transfer function analytical model in homogeneous and heterogeneous traffic flows was used to calculate the traffic flow stability region under different CACC vehicle permutations. The results show that the PS-IDM-based car-following model effectively improves the stability fitting effect of mixed traffic flow, and the driving comfort is increased by up to 20.7% when compared with that of the single car-following model. In addition, there is a negative correlation between the PS and the unstable velocity range of the traffic flow. Compared with the homogeneous HD traffic flow, the average intrusion rate of homogeneous CACC traffic flow is reduced by 4.6%, and the driving comfort of vehicles is improved by approximately 65%.

To address the low accuracy and inefficiency of current lane-change trajectory prediction methods for human-driven vehicles, this study develops a neural network lane-change trajectory prediction model with hyperparametric optimization capability using Bayesian optimization and gated recurrent units to consider the effect of lane-change intention on vehicle lane-change behavior and to predict it. The proposed model was instantiated using trajectory data of 8,721 vehicles. The results show that the overall recognition accuracy of intention recognition under the optimal input is 93.54%, and the recognition accuracy of keeping straight, left lane-change and right lane-change is 95.59%, 91.72%, and 93.30%, respectively. The root mean square errors of the predicted and actual trajectories to the left and to the right under the optimal input are 0.2582 and 0.2957, respectively. This paper demonstrates that, for the intention recognition module, the low-dimensional input enables the model to obtain high prediction accuracy, while for the trajectory prediction module, the high-latitude input enables the model to obtain a low prediction error. The developed trajectory prediction model can be used to assist in driving decision-making, path planning, and so forth.