Parking Space Reasoning Model for Complex Scenarios

doi:10.16182/j.issn1004731x.joss.25-0409

Abstract

Abstract:

In the industrialization process of the combined driving assistance system, complex parking environments bring many challenges, such as occlusion of parking spaces, uneven lighting, and missed and false detections. To address these issues, a parking space reasoning model named PIPS-Net was proposed through PINet optimization. In terms of network architecture design, the model deeply integrated the stacked hourglass network with the recurrent feature-shift aggregator (RESA) to construct a context feature extraction architecture, which enhanced the feature reasoning ability in complex scenarios. Meanwhile, it reconstructed the output to meet the requirements of parking space detection tasks, thereby jointly improving the accuracy of parking space perception in complex scenarios. In terms of loss function design,based on the characteristics of parking space detection tasks, targeted loss functions were innovatively designed, and knowledge distillation was introduced for supervision. Through the collaborative optimization of multi-task losses, the model's ability to jointly model the geometric structure and state information of parking spaces was enhanced. In terms of algorithm optimization, a post-processing algorithm based on two-dimensional parking space reasoning was proposed, which effectively solved the problem of detection integrity when parts of the parking space are invisible. Experimental results show that the model performs excellently, and its lightweight version still maintains a high level of detection accuracy, providing an innovative solution with both technical advancement and engineering practicality for automatic parking systems.

Key words: combined driving assistance system, automatic parking system, parking space reasoning, recurrent feature-shift aggregator, knowledge distillation

CLC Number:

TP391.9

Zhou Congling, Wang Chunpeng, Xie Qiwei, Wang Yongqiang, Shen Lijun. Parking Space Reasoning Model for Complex Scenarios[J]. Journal of System Simulation, 2025, 37(11): 2724-2740.

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名称	版本
操作系统	Ubuntu22.04
CPU	AMD Ryzen™ 7 5800H CPU @3.20 GHz
GPU	NVIDIA® GeForce RTX™ 3050
Python	3.8.20
Pytorch	2.4.1
CUDA	11.8

模块	精确率	召回率	F1分数
RESA+4H	95.90	92.70	94.27
4H	93.12	90.07	91.89
3H	92.15	89.00	90.58
2H	90.34	88.45	89.38
1H	90.28	88.21	89.23
D*1H	95.47	91.80	93.60

模型	精确率	召回率	F1分数
SPFCN^[15]	98.01	97.31	97.65
DMPR-PS^[20]	99.42	99.37	99.39
GCNN^[21]	99.56	99.42	99.49
PIPS-Net	99.46	99.90	99.68
D*PIPS-Net	99.31	99.53	99.41

模型	精确率	召回率	F1分数
SPFCN^[15]	95.09	81.36	87.69
PIPS-Net	95.90	92.70	94.27
D*PIPS-Net	95.47	91.80	93.60

标准差	SPFCN^[15]	PIPS-Net	D*PIPS-Net
σ=0.00	87.69	94.27	93.60
σ=0.50	87.01	94.22	93.05
σ=1.00	86.23	93.05	92.38
σ=1.50	85.57	92.41	91.76