For autonomous driving vehicles, there are currently some issues, such as limited environmental awareness and locally optimal decision-making. To increase the capacity of autonomous cars’ environmental awareness, computation, decision-making, control, and execution, intelligent roads must be constructed, and vehicle–infrastructure cooperative technology must be used. The Roadside unit (RSU) deployment, a critical component of vehicle–infrastructure cooperative autonomous driving, has a direct impact on network performance, operation effects, and control accuracy. The current RSU deployment mostly uses the large-spacing and low-density concept because of the expensive installation and maintenance costs, which can accomplish the macroscopic and long-term communication functions but fall short of precision vehicle control. Given these challenges, this paper begins with the specific requirements to control intelligent vehicles in the cooperative vehicle–infrastructure environment. An RSU deployment scheme, based on the improved multi-objective quantum-behaved particle swarm optimization (MOQPSO) algorithm, is proposed. This RSU deployment scheme was based on the maximum coverage with time threshold problem (MCTTP), with the goal of minimizing the number of RSUs and maximizing vehicle coverage of communication and control services. Finally, utilizing the independently created open simulation platform (OSP) simulation system, the model and algorithm’s viability and effectiveness were assessed on the Nguyen–Dupuis road network. The findings demonstrate that the suggested RSU deployment scheme can enhance network performance and control the precision of vehicle–infrastructure coordination, and can serve as a general guide for the deployment of RSUs in the same application situation.

Traffic sign recognition is an important part of intelligent transportation system. It uses computer vision and traffic sign recognition technology to detect and recognize traffic signs on the road automatically. In this paper, we propose a lightweight model for traffic sign recognition based on convolutional neural networks called ConvNeSe. Firstly, the feature extraction module of the model is constructed using the Depthwise Separable Convolution and Inverted Residuals structures. The model extracts multi-scale features with strong representation ability by optimizing the structure of convolutional neural networks and fusing of features. Then, the model introduces Squeeze and Excitation Block (SE Block) to improve the attention to important features, which can capture key information of traffic sign images. Finally, the accuracy of the model in the German Traffic Sign Recognition Benchmark Database (GTSRB) is 99.85%. At the same time, the model has good robustness according to the results of ablation experiments.

Forward-looking sonar is widely used in underwater obstacles and objects detection for navigational safety. Automatic sonar images recognition plays an important role to reduce the workload of staff and subjective errors caused by visual fatigue. However, the application of automatic object classification in forward-looking sonar is still lacking, which is due to small effective samples and low signal-to-noise ratios (SNR). This paper proposed an improved PP-YOLOv2 algorithm for real-time detection, called as PPYOLO-T. Specifically, the proposed method first resegments the sonar image according to different aspect ratio and filters the acoustic noise in various ways. Then, attention mechanism is introduced to improve the ability of network feature extraction. Finally, the decoupled head is used to optimize the multiobjective classification. Experimental results show that the proposed method can effectively improve the accuracy of multitarget detection task, which can meet the requirement of robust real-time detection for both raw and noised sonar targets.

Forest fire has the characteristics of sudden and destructive, which threatens safety of people’s life and property. Automatic detection and early warning of forest fire in the early stage is very important for protecting forest resources and reducing disaster losses. Unmanned forest fire monitoring is one popular way of forest fire automatic detection. However, the actual forest environment is complex and diverse, and the vision image is affected by various factors easily such as geographical location, seasons, cloudy weather, day and night, etc. In this paper, we propose a novel fire detection method called Fire-PPYOLOE. We design a new backbone and neck structure leveraging large kernel convolution to capture a large arrange area of reception field based on the existing fast and accurate object detection model PP-YOLOE. In addition, our model maintains the high-speed performance of the single-stage detection model and reduces model parameters by using CSPNet significantly. Extensive experiments are conducted to show the effectiveness of Fire-PPYOLOE from the views of detection accuracy and speed. The results show that our Fire-PPYOLOE is able to detect the smoke- and flame-like objects because it can learn features around the object to be detected. It can provide real-time forest fire prevention and early detection.

In the field of high accuracy dual-axis rotational inertial navigation system (RINS), the calibration accuracy of the gyroscopes and accelerometers is of great importance. Although rotation modulation can suppress the navigation error caused by scale factor error and bias error in a static condition, it cannot suppress the scale factor errors thoroughly during the maneuvering process of the vehicle due to the two degrees of rotation freedom. The self-calibration method has been studied by many researchers. However, traditional calibration methods need several hours to converge, which is unable to meet the demand for quick response to positioning and orientation. To solve the above problems, we do the following work in this study: (1) we propose a 39-dimensional online calibration Kalman filtering (KF) model to estimate all calibration parameters; (2) Error relationship between calibration parameters error and navigation error are derived; (3) A backtracking filtering scheme is proposed to shorten the calibration process. Experimental results indicate that the proposed method can shorten the calibration process and improve the calibration accuracy simultaneously.

Though effective in theoretical simulation, the established traffic control models and optimization algorithms will result in model mismatch or even control strategy failure in actual application. However, they are commonly adopted in traffic signal control research, resulting in the unavailability of many exceptional control algorithms in practice. Simulation should function as a bridge between theoretical research and actual application, allowing the gap between the two to be communicated and made up for. However, an effective connection between the two has yet to be established to enable simulation methods in existing traffic control research. To this end, we designed and developed a simulation platform for "Online Application—HILS (Hardware-in-the-Loop Simulation)—Practice" integration over traffic signal control. In this paper, the architecture and characteristics of the integrated simulation platform were described. Besides, the function of each module of the platform was detailed, followed by listing simulation examples for six complex scenarios, with the active control scenario being selected for simulation comparison analysis. The findings demonstrated extensive road network simulation with the integrated simulation platform, multidimensional control variables, control strategies with support, as well as stable and reliable operation. It can be used to verify several sorts of traffic control simulation with variable dimensions.