This article presents a line segments-based method for calculating the inertia load generated in the cable release system of vehicle latch systems during vehicle collision accidents. The method is proposed based on the theoretical calculation method recommended by GTR No.1 and provides a concise and effective approach for calculating the inertia force produced by the cable release system. It also considers the friction between the wire rope and conduit to prevent redundant design of the latch system. By extracting the spline curve of the cable in CAD software, points on the cable can be effectively identified to construct line segments. This approach saves time in the design and verification process of automobile door latch systems and allows for iterative analysis.
KEYWORDS: Control systems, Mathematical optimization, Roads, Fuzzy logic, Computer simulations, Particle swarm optimization, Detection and tracking algorithms, Data modeling, Systems modeling, Passive control
In order to solve the problem of poor suspension smoothness and poor performance caused by the difficulty in selecting the traditional PID control parameters of active suspension, this paper proposes a PID active suspension control method based on improved honey badger algorithm. In this method, the honey badger algorithm is improved by using the reverse learning strategy and the population mutation strategy, and the PID control of the active suspension is optimized and adjusted through the improved honey badger algorithm. The simulation results of different control strategies are compared and analyzed, and the results show that the proposed method can effectively improve the smoothness of the active suspension, and improve the accuracy and stability of the control.
Analog pressure gauges are widely used in many industries, and such gauges shall be verified no more than every 6 months according to the JJG52-2013 standard in China. Traditionally, the gauges are verified manually, and this is no easy job due to the number of gauges that need to be checked on regular bases. One of the most important but tedious steps during the verification process is reading the outputs of each gauge accurately when it is pressurized at different levels, and the reading accuracy can be affected due to the fatigue of humans. This paper described a comprehensive machine-learning-based analog gauge reading approach to facilitate the verification process and reduce the workload of humans. A semantic segmentation model was implemented for retrieving the masks of the pointer and the scale area to calculate the angular displacement of the pointer. The numbers and the gauge units were recognized using OCR algorithms. Finally, the actual reading of the pressure gauge can be determined based on the angular displacement of the pointer and its corresponding number and unit. The experimental results showed that the method described in this paper could fulfill the required accuracy of the verification standard.
Based on the structural characteristics of semi-trailer trains and the problem of large blind spots in their field of view, this article proposes a pure visual solution to determine the articulation angle technology and build a panoramic view system for semi-trailer trains to solve the problem of blind spots. Firstly, the fisheye camera is corrected for distortion and perspective transformation, and a weighted fusion algorithm is used to generate partial panoramic images of a single vehicle body. Then, the articulation angle algorithm based on feature point matching is used to obtain the articulation angle information, and the panoramic view image of the entire vehicle is generated by combining the articulation angle information. Finally, a simulation scene is built in Prescan to verify the stitching effect. The simulation results show that the proposed panoramic view system can effectively and clearly generate panoramic view images of semi-trailer trains at different articulation angles, effectively eliminating blind spots in their field of view and improving vehicle safety.
Aiming at avoiding the occurrence of collisions between vehicles and pedestrians, who were crossing roads, a fuzzy logic control - based collision risk assessment model, and a corresponding pedestrian AEB control strategy were proposed. The AEB control strategy consists of two levels of early warning outputs, and two levels of braking force outputs, which are based on the collision risk assessment results. First, a vehicle-pedestrian collision model was established. Then, a fuzzy control-based risk assessment model and an AEB longitudinal control strategy model were designed in Matlab/Simulink. Second, a simulation scene in PreScan was set up. Finally, the proposed control strategy was verified by a Simulink and PreScan co-simulation. The results showed that the in-house developed AEB control strategy could accurately assess the collision risk and take effective countermeasures to avoid collisions with pedestrians who were crossing roads at constant speeds. The strategy can accurately identify the change of the movement of the pedestrian. When the relative speed between the vehicle and the pedestrian is high, the proposed AEB control strategy can greatly reduce the collision speed, especially when pedestrians cross roads suddenly.
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