Automatic Vertical Parking Reference Trajectory Based on Improved Immune Shark Smell Optimization
Abstract
:1. Introduction
- A novel optimization model of a reference trajectory for AVP is constructed. In view of the problems with the existing AVP reference trajectory optimization, such as poor obstacle avoidance, low trajectory smoothness, a long path, and a large parking incline, a novel, reasonable, and feasible optimization model of the reference trajectory for automatic parking is established using cubic spline.
- A novel improved immune shark optimization algorithm is put forward to address the issue of local convergence in the existing shark optimization algorithm. This method organically incorporates refraction, Gaussian variation, and immunity mechanisms, which can effectively improve the global optimization performance and solve the problem of not being able to escape local convergence.
2. Model of Reference Trajectory Optimization and Related Principles for Automatic Vertical Parking
2.1. Basic Principles of Automatic Vertical Parking
2.2. Feasibility Conditions for AVP
2.3. Coordinate Transformation Principle for the Parking Plane
2.4. Reference Trajectory Optimization Model for AVP
2.5. Solution Principle of the Parking Trajectory Length
- Curve segment division: We divide the sampling points’ set into several curve segments. Every curve segment represents consecutive sampling points. The division of the curve segments should ensure that the length of every curve segment is roughly equal, to achieve equal-length quantization.
- Curve segment endpoints extraction: For every curve segment, we extract its two endpoints as the endpoints of the curve segment, and the coordinates of the two endpoints are calculated to determine the length of the curve segment.
- Curve segment length calculation: We calculate the distance between the two endpoints of every curve segment. In the two-dimensional plane coordinate system, the distance between the two endpoints is .
- Result verification: We verify the length of every curve segment to determine whether the length of the curve segment meets the expected error range.
- Quantization result recording: We record the length of every curve segment for the subsequent trajectory length calculation.
- Selection of the encoding method: We select the appropriate encoding method according to the data type to be encoded. For the decimal index value of the parking position reference point set, binary code can be used instead.
- Data conversion: We convert the original data to the encoding format. The index values of the parking position reference points’ set need to be converted into binary numbers.
- Encoding processing: The converted data are encoded according to a fixed length. We fill every binary number to the specified length to ensure that every coding length is the same.
- Verification of the encoding results: We verify whether every encoding length is the same and whether every coding can correctly represent the original data.
- Result recording: We record the encoding results and prepare them for subsequent intelligent algorithm optimization.
3. Improved Shark Optimization Algorithm
3.1. Shark Optimization Algorithm
3.2. Refraction Mechanism
3.3. Gaussian Variation Mechanism
- It can effectively expand and strengthen the local search scope and intensity of the shark optimization algorithm, which is conducive to improving its local search ability.
- If the shark optimization algorithm is trapped, risking local convergence, the powerful local perturbation in the region will significantly help it to escape.
3.4. Immune Mechanism
4. Experimental Verification
4.1. Description of the AVP Experiment Scenes
4.2. Overall Design of the Automatic Vertical Parking Experiments
4.3. Results and Analysis of the Automatic Parking Experiment
5. Conclusions
- Real-world parking scenes include dynamic obstacles (e.g., pedestrians and other vehicles). When there are sudden obstacles during parking, the AVP system will trigger the stop command; however, it does not have the ability to continue tracking control and avoid obstacles.
- Our research was limited to ordinary vehicles; hence, it is not applicable to special vehicles, such as trucks, heavy-duty vehicles, large vehicles, and small vehicles.
- There is still room for further improvement of the results, although compared with the existing results, our research results were improved.
- The research presented was still in the experimental stage, which required the configuration of a relatively complex and expensive AVP system; hence, we remain far from having a product that can be mass-produced.
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
Abbreviations
AVP | automatic vertical parking |
IISSO | improved immune shark smell optimization |
SSO | shark smell optimization |
IIMFO | improved immune moth flame optimization |
MFO | moth flame optimization |
IPSO | improved particle swarm optimization |
PSO | particle swarm optimization |
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Name | Symbol | Value |
---|---|---|
Vehicle coverage area collected times | 10 | |
Parking inclination angle threshold | ||
Parking process period | s | |
Parking interval threshold | m |
Algorithm | Parking Point Vector (m, m) | Path Length (m) | Parking Position Error (m) | Realization Time (s) |
---|---|---|---|---|
IISSO | (1.47, 2.50, 0.33) | 9.524 | 0 | 10.11 |
IIMFO | (1.40, 2.49, 0.67) | 9.640 | −0.01 | 10.38 |
SSO | (1.35, 2.48, 0.74) | 9.665 | −0.02 | 11.20 |
MFO | (1.32, 2.49, 2.12) | 9.701 | −0.01 | 10.89 |
IPSO | (1.28, 2.49, 1.82) | 9.708 | −0.01 | 10.67 |
PSO | (1.19, 2.46, 1.79) | 9.799 | −0.04 | 10.46 |
Algorithm | Parking Point Vector (m, m) | Path Length (m) | Parking Position Error (m) |
---|---|---|---|
IISSO | (1.44, 2.50, 0.48) | 9.647 | 0 |
IIMFO | (1.40, 2.49, 1.01) | 9.710 | −0.01 |
SSO | (1.32, 2.54, 1.53) | 9.725 | 0.04 |
MFO | (1.31, 2.49, 3.01) | 9.737 | −0.01 |
IPSO | (1.20, 2.49, 2.65) | 9.758 | −0.01 |
PSO | (1.16, 2.45, 2.62) | 9.804 | −0.05 |
Algorithm | Parking Point Vector (m, m) | Path Length (m) | Parking Position Error (m) | Realization Time (s) |
---|---|---|---|---|
IISSO | (1.55, 2.39, 0.40) | 9.035 | −0.01 | 9.47 |
IIMFO | (1.49, 2.43, 1.57) | 9.182 | 0.03 | 9.88 |
SSO | (1.41, 2.44, 2.82) | 9.293 | 0.04 | 10.62 |
MFO | (1.33, 2.39, 2.44) | 9.411 | −0.01 | 10.09 |
IPSO | (1.24, 2.35, 2.07) | 9.498 | −0.05 | 10.44 |
PSO | (1.17, 2.35, 1.79) | 9.553 | −0.05 | 10.12 |
Algorithm | Parking Point Vector (m, m) | Path Length (m) | Parking Position Error (m) |
---|---|---|---|
IISSO | (1.53, 2.40, 0.53) | 9.109 | 0 |
IIMFO | (1.47, 2.44, 2.72) | 9.292 | 0.04 |
SSO | (1.40, 2.46, 2.85) | 9.358 | 0.06 |
MFO | (1.32, 2.41, 1.51) | 9.443 | 0.01 |
IPSO | (1.23, 2.38, 1.29) | 9.502 | −0.02 |
PSO | (1.20, 2.41, 0.72) | 9.544 | 0.01 |
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Chen, Y.; Liu, G.; Wang, L.; Xia, B. Automatic Vertical Parking Reference Trajectory Based on Improved Immune Shark Smell Optimization. Algorithms 2024, 17, 308. https://doi.org/10.3390/a17070308
Chen Y, Liu G, Wang L, Xia B. Automatic Vertical Parking Reference Trajectory Based on Improved Immune Shark Smell Optimization. Algorithms. 2024; 17(7):308. https://doi.org/10.3390/a17070308
Chicago/Turabian StyleChen, Yan, Gang Liu, Longda Wang, and Bing Xia. 2024. "Automatic Vertical Parking Reference Trajectory Based on Improved Immune Shark Smell Optimization" Algorithms 17, no. 7: 308. https://doi.org/10.3390/a17070308
APA StyleChen, Y., Liu, G., Wang, L., & Xia, B. (2024). Automatic Vertical Parking Reference Trajectory Based on Improved Immune Shark Smell Optimization. Algorithms, 17(7), 308. https://doi.org/10.3390/a17070308