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This paper establishes the kinematic model of the automatic parking system and analyzes the kinematic constraints of the vehicle. Furthermore, it solves the problem where the traditional automatic parking system model fails to take into account the time delay. Firstly, based on simulating calculation, the influence of time delay on the dynamic traj...
Citations
... In the path planning process, accurate calculation of the distance in the raster map is the basis for obtaining the optimal path [24][25][26]. When using path planning algorithms, common methods for calculating the distance in the Cartesian coordinate system include Manhattan, Euclidean, and Chebyshev. ...
Compared to wheeled vehicles, tracked vehicles have unique advantages in disaster relief and engineering sites. The working environment of tracked vehicles is mostly in fixed-point conditions, so high-precision automatic stopping is essential for tracked vehicles. Improved A* and dynamic window approach are proposed in this paper to enhance the accuracy and speed of automatic parking of tracked vehicles. The parking trajectory is significantly optimized within low deviations. Furthermore, a prototype is designed to verify the working conditions of fixed-point parking to improve the work efficiency of engineering rescue. The simulation results based on the robot operating system show that both single-step and two-step parking methods can reach the parking position without collision. More importantly, simulated and experimental parking positions and angles have considerable accuracy within acceptable limits. This study has essential guidance and application value for the high-precision automatic parking of tracked vehicles for various extreme application scenarios.
... The first approach is that the controller follows a reference path, known as path tracking. In this approach, the controller uses the difference between the vehicle's current location and the reference path to bring the car closer to the reference path [12], [13]. However, in the second approach, unlike the first one, the controller tries to build the way and does not use the error between the car's current location and the reference path to follow the referenced path. ...
The applications of intelligent systems have received a growing interest in the automobile industry. The car parking issue is one of the challenges for drivers, which can be solved by these smart systems. In this study, a new method for automatic parallel parking problems is proposed. The suggested method uses fuzzy inference to control, steer, and park the car in a tight space. Two parallel embedded fuzzy controllers have been designed to navigate the vehicle on the desired path. Unlike other studies that have used the error between developed and reference routes, the proposed method only uses the car’s position data, similar to an experienced driver. There is no need to use the sensors to track the desired path in this method, but the car’s kinematic model’s output is the controllers' input. Based on the obtained results, the generated path by controllers is close to the desired track.
... Automatic parking technology refers to the parking process that completes the parking operations safely and quickly without a driver and can effectively improve driving comfort while greatly reducing the probability of accidents during parking. Also, the promotion of automatic parking technology can promote the development and deployment of autonomous driving and intelligent vehicles [4][5][6][7][8][9][10]. ...
... (3) Parking slot length ≥ vehicle length + 0.8 m, parking slot width ≥ vehicle width + 0.3 m; (4) Automatic parking speed does not exceed 3 km/h; (5) The distance between the obstacle on the opposite side of the parking slot and the vehicle is not less than 1.0 m. In order to ensure the effectiveness of the research method introduced in this article on vehicle parking control, some assumptions are made on the parking research in combination with the with actual parking restriction requirements. ...
... (3) Parking slot length ≥ vehicle length + 0.8 m, parking slot width ≥ vehicle width + 0.3 m; (4) Automatic parking speed does not exceed 3 km/h; (5) The distance between the obstacle on the opposite side of the parking slot and the vehicle is not less than 1.0 m. ...
As a key technology for intelligent vehicles, automatic parking is becoming increasingly popular in the area of research. Automatic parking technology is available for safe and quick parking operations without a driver, and improving the driving comfort while greatly reducing the probability of parking accidents. An automatic parking path planning and tracking control method is proposed in this paper to resolve the following issues presented in the existing automatic parking systems, that is, low degree of automation in vehicle control; lack of conformity between segmented path planning and real vehicle motion models; and low success rates of parking due to poor path tracking. To this end, this paper innovatively proposes preview correction which can be applied to parking path planning, and detects the curvature outliers in the parking path through the preview algorithm. In addition, it is also available for correction in advance to optimize the reasonable parking path. Meanwhile, the dual sliding mode variable structure control algorithm is used to formulate path tracking control strategies to improve the path tracking control effect and the vehicle control automation. Based on the above algorithm, an automatic parking system was developed and the real vehicle test was completed, thus exploring a highly intelligent automatic parking technology roadmap. This paper provides two key aspects of system solutions for an automatic parking system, i.e., parking path planning and path tracking control.
... As one of the critical problems of automatic parking technology, a large number of scholars have proposed the related control algorithms. Hua et al. 8 suggested an automatic parking path control method considering time delay, which solved the problem of the traditional APS control model not regarding vehicle control delay. Oetiker et al. 9 put forward a semiautomatic parking assistant system based on navigation area, which is able to perceive environment information in real time via the environment perceptual sensing device and optimize parking routes to avoid collisions. ...
This study examines how to improve the accuracy of auto parking path tracking control; therefore, a linear model predictive control with softening constraints path tracking control strategy is proposed. Firstly, a linear time-varying predictive model of vehicle is established, and the future state of the vehicle can be predicted. The designed objective function fully considers the deviation between the predictor variable and the reference variable. Also, the relaxation factors are added to the optimization process, and the control increment of each cycle is calculated by the quadratic programming. Through rolling optimization and feedback correction, all kinds of deviations in the control process can be corrected in time. Then, the Simulink/CarSim simulation is carried out jointly. Furthermore, the path tracking simulation based on proportion–integration–differentiation control and no control is also carried out to compare with the model predictive control. Finally, a real vehicle test is carried out for model predictive control algorithm, and a comparative experiment based on proportion–integration–differentiation control and no control is carried out.
... It is noteworthy that the convergence time of (x e , y e , θ e ) in ten seconds. In Figures 14 and 15, the curvilinear trend is smooth, so that it can be implemented in reality [26]. ...
This paper discusses an automatic parking control method based on the combination of the sliding mode variable structure control (SMVSC) and fuzzy logical control. SMVSC is applied to drive the vehicle from a random initial position and pose, to the designated parking position and pose. Then, the vehicle is driven from the designated parking position to the target parking slot using the method of fuzzy logical control, whose rules are limited to the range of the effective initial position. To combine SMVSC with the fuzzy logical control, the experimental results demonstrate that effective parking can be guaranteed, even if the initial position is out of the effective parking area of the fuzzy logical control.
... It is easy to describe this process in natural language but difficult to simulate it using computer, and the automatic implementation with uncertainty is still a challenge. We believe that, cloud model, proposed by Li et al. [17,18], can handle such uncertainty in a better way, since it 2 Computational Intelligence and Neuroscience provides us with more design degrees of freedom and realizes the transformation between a qualitative concept described in words and its numerical representation [19,20]. Cloud model is a cognitive model between a qualitative concept and its quantitative instantiations and successfully used in various applications. ...
The process of creating nonphotorealistic rendering images and animations can be enjoyable if a useful method is involved. We use an evolutionary algorithm to generate painterly styles of images. Given an input image as the reference target, a cloud model-based evolutionary algorithm that will rerender the target image with nonphotorealistic effects is evolved. The resulting animations have an interesting characteristic in which the target slowly emerges from a set of strokes. A number of experiments are performed, as well as visual comparisons, quantitative comparisons, and user studies. The average scores in normalized feature similarity of standard pixel-wise peak signal-to-noise ratio, mean structural similarity, feature similarity, and gradient similarity based metric are 0.486, 0.628, 0.579, and 0.640, respectively. The average scores in normalized aesthetic measures of Benford’s law, fractal dimension, global contrast factor, and Shannon’s entropy are 0.630, 0.397, 0.418, and 0.708, respectively. Compared with those of similar method, the average score of the proposed method, except peak signal-to-noise ratio, is higher by approximately 10%. The results suggest that the proposed method can generate appealing images and animations with different styles by choosing different strokes, and it would inspire graphic designers who may be interested in computer-based evolutionary art.
... Automatic parking technology completes parking operations safely and quickly without a driver and can effectively improve driving comfort while greatly reducing the probability of accidents during parking. In addition, the popularization of automatic parking technology can promote the development of automatic and intelligent vehicles [3][4][5][6]. At present, there are two main research methods for studying automatic parking systems: the research methods based on ultrasonic sensors and those based on visual sensors [7]. ...
As a key component of intelligent vehicle technology, automatic parking technology has become a popular research topic. Automatic parking technology can complete parking operations safely and quickly without a driver and can improve driving comfort while greatly reducing the probability of parking accidents. An automatic parking system based on parking scene recognition is proposed in this paper to resolve the following issues with existing automatic parking systems: parking scene recognition methods are less intelligent, vehicle control has a low degree of automation, and the research scope is limited to traditional fuel vehicles. To increase the utilization of parking spaces and parking convenience, machine vision and pattern recognition techniques are introduced to intelligently recognize a vertical parking scenario, plan a reasonable parking path, develop a path tracking control strategy to improve the vehicle control automation, and explore a highly intelligent automatic parking technology roadmap. This paper gives three key aspects of system solutions for an automatic parking system based on parking scene recognition: parking scene recognition, parking path planning, and path tracking and control.
A hierarchical trajectory tracking and yaw stability combined control strategy of autonomous ground vehicle within-wheel motor is proposed in this paper to achieve simultaneous and accurate trajectory tracking and vehicle yaw stability control. A trajectory-tracking controller is designed on the basis of model predictive control algorithm, and the ideal front-wheel steering angle requirement is calculated to follow the referenced trajectory. In order to improve the accuracy of vehicle steering control, a vehicle steering controller is designed based on high-order sliding mode control method, in which the control demand of front-wheel steering angle is satisfied by real-time torque control of vehicle steering motor. Simultaneously, a double power reaching rate-based sliding mode control method is applied to design the vehicle yaw stability controller, in which the yaw moment control requirement is met by an optimal oriented tire force allocation algorithm. The simulation and experiment results show that the presented control method can improve the accuracy and real-time performance of trajectory tracking control while ensuring vehicle yaw stability.
Automated parking system (APS) that explicitly considers the time efficiency of the motion has received large amounts of attention in recent years. Trajectory planning module in these APS delivered parking trajectory, which was expected to be precisely tracked by tracking module. However, the reference points of frequently used trackers were selected in the spatial domain, resulting in significant trajectory tracking errors with temporal information. In this paper, a tracking control method called ILC-MPC, which combined model predictive control (MPC) and iterative learning control (ILC), was proposed to improve the spatiotemporal tracking accuracy of the autonomous vehicle. ILC was utilized for longitudinal compensation using the error signal between historical and expected speed. Accordingly, the error model in the longitudinal direction was simplified to decrease the number of decision variables in MPC. Simulation experiments using CarSim were carried out to compare the proposed method with open-loop control, linear quadratic regulator (LQR), and pure MPC that had a similar computing time with ILC-MPC. ILC-MPC converged in a few iterations of the learning process and achieved the highest tracking accuracy in spatiotemporal domain among the mentioned methods.
