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When automobiles are at the threat of collisions, steering usually needs shorter longitudinal distance than braking for collision avoidance, especially under the condition of high speed or low adhesion. Thus, more collision accidents can be avoided in the same situation. The steering assistance is in need since the operation is hard for drivers. And considering the dynamic characteristics of vehicles in those maneuvers, the real-time and the accuracy of the assisted algorithms is essential. In view of the above problems, this paper first takes lateral acceleration of the vehicle as the constraint, aiming at the collision avoidance situation of the straight lane and the stable driving inside the curve, and trajectory of the collision avoidance is derived by a quintic polynomial.

The fracture of the retainer leads to the failure of the needle rolling bearings in a planetary gear train of a planetary gearbox. In order to solve this engineering problem, the kinematics and kinetics of the failed needle rolling bearings is analyzed with the analytical model and the numerical model. A simple mathematical model is pointed out to analyze the dynamic load of a needle rolling element in the failed bearings. The assembling position of the small sun gear is also found to influence the performance of the needle rolling bearings significantly and therefore a best scheme for modifying the structure of the planetary gearbox is pointed out based on the multi-object optimization theory to reduce the bearing load heavily. Based on the calculated work, a systematic method of choosing the proper bearings for planetary gearbox can be concluded.

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 .

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.

Comparing with traditional braking systems of automobiles, the brake-by-wire (BBW) system has a faster dynamic response and is more suitable for applications that facilitate regenerative braking. As the two main categories of BBW systems, the well-known electro-hydraulic braking system and electro- mechanical braking system are not compact enough and their fail-safe function has always been a worrying aspect. A new BBW system called integrated braking system (IBS) by employing the hydraulic multiplex method was proposed in recent years. The IBS implements power-assisted braking and active braking by means of just an integrated unit. It can certainly be used for ABS, ASR and ESC systems for building up and reducing brake pressure. Presented in the paper is a new structure of IBS, which is mainly composed of a motor, ball screw, master cylinder and four 2/2-way valves.

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 one of advanced driver assistance systems (ADAS), automatic parking system has great market prospect and application value. In this paper, based on an intelligent vehicle platform, an automatic parking system is designed by using multi-point preview theory. The vehicle kinematics model was established, based on Ackermann steering principle. By analyzing working conditions of parallel parking, complex constraint condition of parking trajectory is established and reference trajectory based on sine wave is proposed. In addition, combined with multi-point preview theory, the design of trajectory following controller for automatic parking is completed. The cost function is designed, which consider the trajectory following effect and the degree of easy handling. The optimization of trajectory following control is completed by using the cost function.

This paper investigates the path planning and formation control problem for connected automated vehicles (CAVs) in unstructured road scenarios. A hierarchical framework that integrates path planning and vehicle dynamics control is proposed for multiple CAVs to form specific formations without lane information of the road. In the path planning layer, a virtual leader is used to guide the position and direction of the formation and the decentralized path planning algorithm for multiple CAVs is developed based on the virtual leader's information through the communication network. A spatial and temporal graph (STG) is constructed based on the virtual leader's local path and the specific formation shape. The improved A* method based on the STG is proposed to generate the coarse path for the following vehicles. Then the path is smoothed by the B-spline method.

The slip ratio of vehicle driving wheels is easily beyond a reasonable range in the complex and changeable driving conditions. In order to achieve the adaptive acceleration slip regulation of four-wheel driving (4WD) vehicle, a fuzzy control strategy of Automatic Drive Train Management (ADM) system based on road situation identification was proposed in this paper. Firstly, the influence on the control strategy of ADM system was analyzed from two aspects, which included the different road adhesion coefficients and the vehicle’s ramp driving state. In the meantime several quantitative expressions of relevant control parameters were derived. Secondly, the fuzzy logic control algorithm was adopted to design a road situation identification subsystem and a ramp driving state identification subsystem respectively. The former was based on the μ-S curve model, and the latter was based on the vehicle driving equilibrium equation.

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.

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.

Knowledge of the tire slip ratio can greatly improve vehicle longitudinal stability and its dynamic performance. Most conventional slip ratio observers were mainly designed based on input of non-driven wheel speed and estimated vehicle speed. However, they are not applicable for electric vehicles (EVs) with four in-wheel motors. Also conventional methods on speed estimation via integration of accelerometer signals can often lead to large offset by long-time integral calculation. Further, model uncertainties, including steady state error and unmodeled dynamics, are considered as additive disturbances, and may affect the stability of the system with estimated state error. This paper proposes a novel slip ratio observer based on input-to-state stability (ISS) method for electric vehicles with four-wheel independent driving motors.