Auxiliary driving multi-environment intelligent parking path planning control method

Abstract

According to the method, the complex, changing and narrow environment is automatically recognized without human intervention, the parking mode with the simplest route, feasible vehicle conditions and comfortable passengers is calculated, and automatic parking is achieved; a current parking environment is collected and input into a system, a corresponding sensor is matched with an accurate positioning system to draw a parking environment map, more environmental factors are comprehensively considered, and automatic parking is achieved in the environment with narrow space, complex environment and moving objects. An actual parking environment is converted into a model map in the system, an automatic parking path is planned according to calculation, a parking line with high difficulty can be designed in a complex and narrow environment, and the problem that automatic parking cannot be started when the difficulty is high is solved; the parking system is controlled in combination with a PID optimization control strategy, parking is carried out under the conditions of no obstacle, obstacle and the like according to the calculated track, and compared with the prior art, the adaptive capacity to the environment is higher, the efficiency of automatic parking is higher, and the experience is better.

Description

technical field [0001] The present application relates to an automatic parking method for assisted driving of an automobile, in particular to a method for planning and control of an assisted driving multi-environment intelligent parking path, which belongs to the technical field of automobile intelligent driving. Background technique [0002] The continuous increase in the number of cars has directly led to the congestion of the parking environment and the shortage of parking spaces. In a tense parking environment, it is even more necessary to quickly park the car into the parking space, but parking in the garage is exactly a headache for many car owners. Even some old drivers may not be able to park the car smoothly in the parking space at one time. . If each car owner needs to make many attempts to park, it is likely to cause road traffic jams. In addition, unskilled driving skills are also likely to lead to traffic accidents during the parking process. Therefore, how t...

AI Technical Summary

Problems solved by technology

[0007] (1) The existing automatic car parking system cannot automatically identify the entire environment without human intervention at all, and the parking environment factors collected and entered into the system are not complete, lack of several corresponding sensors, and cannot cooperate with the precise positioning system to draw parking Environmental map, due to the insufficient grasp of the parking environment, the automatic parking system of the prior art can only realize automatic parking in some relatively simple environments, and cannot automatically park in complex environments. The demand for parking in a complex dynamic environment is stronger, and the existing technology cannot complete automatic parking in a complex dynamic environment

[0008] (2) The automatic parking path planning in the prior art is not a method based on feedback heuristic search, the actual parking environment is not converted into a model map in the system, and an efficient method for planning automatic parking paths is lacking. The matching between map modeling and calculation planning path is low, resulting in the planned parking path not necessarily the optimal path, the selection of parking path is not reasonable enough, the reliability and intelligence of automatic parking are not enough, and the automatic parking system needs to be repeated. Parking can only be completed by trying, and the parking time is long, which ultimately makes this function relatively useless. Due to the lack of practicality, the utilization rate is not high, and it has not been widely promoted and applied.

[0009] (3) The existing technology lacks the design of joint PID optimization control strategy to control the car parking system. Even if the system has a well-calculated parking trajectory, it cannot combine the actual dynamic fine control of the car to complete automatic parking. Parking control strategy, because the path planning of automatic parking needs to be dynamically adjusted and corrected in combination with the actual situation of the car and the environment, but the parking control methods in the prior art are generally carried out strictly according to the planned path, lack of flexibility, and do not combine the car In the motion model when parking, Matlab is not used to correct the combination of various control links. In practical applications, the parking environment is complex and changeable, and the existing technology cannot adapt to the complex and changeable parking environment. There are certain security risks

[0010] (4) The existing technology can only complete automatic parking in a simple environment with sufficient space, and cannot achieve automatic parking in an environment with narrow spaces, complex environments, and moving objects, and lacks automatic parking based on feedback heuristic search. Parking path planning, unable to design difficult parking routes in complex and narrow environments, lack of dynamic grid time method to establish a parking environment map, in the parking environment with obstacles and complicated movement of obstacles, it is impossible to find the most suitable The automatic parking path; the lack of a fitting function to curve the calculated trajectory, the smooth parking path curve cannot be obtained, and the minimum turning radius and mechanical performance of the car cannot be matched, making the planned trajectory unfeasible, or the planned parking The trajectory is more harmful to the car, and at the same time brings obvious discomfort to the occupants of the car, which eventually leads to rigid and mechanical parking routes, automatic parking is time-consuming and labor-intensive, and the experience of the occupants in the car is poor

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