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This paper deals with an integrated motor-transmission (IMT) speed tracking control of the connected vehicle when there are controller area network (CAN)-induced delays and denial of service (DOS)-induced delays. A connected vehicle equipped with an IMT system may be attacked through the external network. Therefore, there are two delays on the CAN of the connected vehicle, which are CAN-induced and cyber-attack delays. A DOS attack generates huge delays in CAN and even makes the control system invalid. To address this problem, a robust dynamic output-feedback controller of the IMT speed tracking system considering event-triggered detectors resisting CAN-induced delays and DOS-induced delays is designed. The event-triggered detector is used to reduce the CAN-induced network congestion with appropriate event trigger conditions on the controller input and output channels. CAN-induced delays and DOS-induced delays are modeled by polytopic inclusions using the Taylor series expansion.

Improving driving safety and freeway capacity is an indispensable research issue for road vehicles, especially for tractor semi-trailers, which on the one hand exhibit unstable motion modes at high speeds due to their articulated configurations and undertake the largest part of freight transportation on freeways. Automatic driving is rated as the ultimate solution of vehicle safety since it can significantly reduce accidents resulting from human driver errors. Proposed in this paper is a gain-scheduled PID controller for automatic path-following of a tractor semi-trailer. The PID controller minimizes the vehicle's predicted lateral deviation and heading error with respect to the desired path at a preview point, and gains of the controller are scheduled with respect to vehicle speed.

Most tractor-semitrailers are fitted with multi-axle trailers which cannot be actively steered, and such vehicles with an articulated configuration are inclined to exhibit instability such as trailer swing, jack-knifing, and rollover at high speed. Proposed in this paper is an optimal control of the yaw stability of tractor-semitrailers at high speed by applying an active trailer's steering angle. An optimal control algorithm is designed by employing a 3-DOF vehicle model in the yaw plane. The optimal linear quadratic regulator (LQR) approach is used with a cost function including sideslip angles, yaw rates of both tractor and trailer, and trailer's steering angle. The yaw stability at the high speed is also quantified by the dynamic performance measurements of lateral path deviation, hitch angle and rearward amplification (RA). The algorithm is evaluated by co-simulations using TruckSim and Matlab/Simulink softwares.

The development of intelligent and networked vehicles has enhanced the demand for precise road information perception. Detailed and accurate road surface information is essential to intelligent driving decisions and annotation of road surface semantics in high-precision maps. As one of the key parameters of road information, road roughness significantly impacts vehicle driving safety and comfort for passengers. To reach a rapid and accurate estimation of road roughness, in this study, we develop a neural network model based on vehicle response data by optimizing a long-short term memory (LSTM) network through the particle swarm algorithm (PSO), which fits non-linear systems and predicts the output of time series data such as road roughness precisely. We establish a feature dataset based on the vehicle response time domain data that can be easily obtained, such as the vehicle wheel center acceleration and pitch rate.

An improved anti-icing design with film heating ejection slot and cover for the inlet part of aero-engine was brought out, which combines the interior jet impingement with the exterior hot air film heating and shows promising application for those parts manufactured with composite materials. A hybrid method based on the combination of the Back Propagation Neural Network (BPNN) and Genetic Algorithm (GA) is developed to optimize the anti-icing design for a typical aero-engine inlet vane in two dimensions. The optimization aims to maximize the heating performance of the hot air film, which is assessed by the heating effectiveness. The film-heating ejection angle and the cover opening angle are the two geometric variables to be optimized. Numerical model was established and validated to generate training and testing samples for BPNN, which was used to predict the objective function and find the optimal design variables in conjunction with the GA.

This paper raises a coupling system of aircraft environmental control and fuel tank inerting based on membrane separation. The system applies a membrane dehumidifier to replace water vapor removal unit of heat regenerator, condenser and water separator, which is widely used in conventional aircraft environmental control system (ECS) nowadays. Water vapor can travel across the membrane wall under its pressure difference without phase change, so the dehumidification process consumes no cooling capacity as traditional ECS and the cooling capacity of the new system increases. This paper first compares the thermodynamic properties of ECS based on membrane dehumidification and the traditional ECS based on condensation. The results show that the membrane dehumidification system has larger cooling capacity and lighter weight. For a given cooling capacity requirement, the membrane dehumidification system can use less bleed air since the enthalpy of the outlet air is lower.

Traffic light detection has great significant for unmanned vehicle and driver assistance system. Meanwhile many detection algorithms have been proposed in recent years. However, traffic light detection still cannot achieve a desirable result under complicated illumination, bad weather condition and complex road environment. Besides, it is difficult to detect multi-scale traffic lights by embedded devices simultaneously, especially the tiny ones. To solve these problems, this paper presents a robust vision-based method to detect traffic light, the method contains main two stages: the region proposal stage and the traffic light recognition stage. On region proposal stage, we utilize lane detection to remove partial background from the original image. Then, we apply adaptive canny edge detection to highlight region proposal in Cr color channel, where red or green color proposals can be separated easily. Finally, extract the enlarged traffic light RoI (Region of Interest) to classify.

Forward obstacles detection is one of the key tasks in the perception system of autonomous vehicles. The perception solution differs from the sensors and the detection algorithm, and the vision-based approaches are always popular. In this paper, an embedding fast obstacles detection algorithm is proposed to efficiently detect forward diverse obstacles from the image stream captured by the monocular camera. Specifically, our algorithm contains three components. The first component is an object detection method using convolution neural networks (CNN) for single image. We design a detection network based on shallow residual network, and an adaptive object aspect ratio setting method for training dataset is proposed to improve the accuracy of detection. The second component is a multiple object tracking method based on correlation filter for the adjacent images.

As a significant control component of vehicles, automatic transmissions should have failure protection function. The failure protection function partly is determined by the hydraulic control system. However traditional design could not cover all of failures, and there is no general design method. A design method is proposed for designing the shift control oil circuit of the hydraulic control system with the failure protection function. The design method is applied to optimize the hydraulic control system of a six-speed automatic transmission. The function of the optimized hydraulic control system is confirmed by the dynamic simulation. The results show that the design method can simplify the hydraulic control system without losing any original functions. The proposed design method is proved to be suitable for all kinds of hydraulic control systems of automatic transmissions.

In recent years, due to its strong plasticity and excellent future potential, the development of automated vehicles in the mining environment is extraordinarily rapid. The application of lidar in automated vehicles has also become more and more popular, and algorithms for point cloud object detection have emerged endlessly. However, due to rough road and dusk-to-dawn operations in the mining scenario and the large size of the truck, traditional detection algorithms fail to meet the requirements of real-time updates. Due to the high precision and efficiency of the deep learning network, its application could break through the limitations of traditional algorithms. In this paper, the algorithm called PointPillars is adapted for object detection in the mining scenario. After converting the point cloud to a sparse pseudo-image and extracting features by a two-dimensional convolutional neural network (2D CNN), the time consumption of the entire algorithm becomes much less.

Unmanned Aerial Vehicles (UAVs) encounter various uncertainties, including unfamiliar environments, signal delays, limited control precision, and other disturbances during task execution. Such factors can significantly compromise flight safety in complex scenarios. In this paper, to enhance the safety of UAVs amidst these uncertainties, a control accuracy prediction model based on ensemble learning abnormal state detection is designed. By analyzing the historical state data, the trained model can be used to judge the current state and obtain the command tracking control accuracy of the UAV at that instant. Ensemble learning offers superior classification capabilities compared to weak learners, particularly for anomaly detection in flight data. The learning efficacy of support vector machine, random forest classifier is compared and achieving a peak accuracy of 95% for the prediction results using random forest combined with adaboost model .

Recognizing road conditions using onboard sensors is significant for the performance of intelligent vehicles, and the road profile is a widely accepted representation both in the temporal and frequency domains, greatly influencing driving quality. In this paper, a recurrent neural network embedded with attention mechanisms is proposed to reconstruct the road profile sequence. Firstly, the road and vehicle sensor signals are obtained in a simulated environment by modeling the road, tire, and vehicle dynamic system. After that, the models under different working conditions are trained and tested using the collected data, and the attention weights of the trained model are then visualized to optimize the input channels. Finally, field experiments on the real vehicle are conducted to collect real road profile data, combined with vehicle system simulation, to verify the performance of the proposed method.