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Construction Method for Multimodal Rainy Scene Fusion in Autonomous Driving Sample Library
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Zhaolong Dong1, 2, He Huang1, 2, Zhanyi Li1, Lan Yang3, Huifeng Wang2
Automotive Engineering | 2025, 47(2) : 211 - 221
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Automotive Engineering | 2025, 47(2): 211-221
Construction Method for Multimodal Rainy Scene Fusion in Autonomous Driving Sample Library
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Zhaolong Dong1, 2, He Huang1, 2, Zhanyi Li1, Lan Yang3, Huifeng Wang2
Affiliations
  • 1 School of Electronic and Control Engineering,Chang’an University,Xi’an 710064
  • 2 Key Laboratory of Intelligent Expressway Information Fusion and Control,Xi’an 710064
  • 3 School of Information Engineering,Chang’an University,Xi’an 710064
Published: 2025-02-25 doi: 10.19562/j.chinasae.qcgc.2025.02.002
Outline
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For the problems of difficult and uncontrollable data acquisition, as well as limited quantity of available rainy day scene samples in the process of unmanned driving perception performance training, a multimodal fusion-based algorithm for constructing rainy day traffic scenes is proposed. Firstly, the rainy day scenes are analyzed and categorized into two models of rain line models and raindrop models for reconstruction. Secondly, a stochastic multisource fusion-based rain line model is proposed, which integrates rain effect from multiple directions and densities. Next, a heterogeneous mapping-based raindrop model is proposed to achieve realistic convex transparency mapping for individual raindrops, coupled with collision prevention design to mitigate cumulative errors of multiple raindrops in the same area. Finally, the two models are integrated to realize reconstruction of rainy day scenes by using various foundational forms. The experimental results show that as rainfall intensity increases, detailed information in the constructed scenes becomes richer initially, with metrics such as entropy and average gradient showing an initial increase followed by a decrease, while image quality continuously decreases, approaching realistic rainy day conditions. With higher rainfall intensity, both interference and detail in the images notably increase, with higher entropy and average gradient, as well as decreased PSNR and SSIM parameters, indicating significant image quality degradation.

rainy scene samples  /  rain line model  /  raindrop model  /  heterogeneous mapping
Zhaolong Dong, He Huang, Zhanyi Li, Lan Yang, Huifeng Wang. Construction Method for Multimodal Rainy Scene Fusion in Autonomous Driving Sample Library[J]. Automotive Engineering, 2025 , 47 (2) : 211 -221 . DOI: 10.19562/j.chinasae.qcgc.2025.02.002
Year 2025 volume 47 Issue 2
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doi: 10.19562/j.chinasae.qcgc.2025.02.002
  • Receive Date:2024-07-23
  • Online Date:2025-07-09
  • Published:2025-02-25
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  • Received:2024-07-23
  • Revised:2024-08-29
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    1 School of Electronic and Control Engineering,Chang’an University,Xi’an 710064
    2 Key Laboratory of Intelligent Expressway Information Fusion and Control,Xi’an 710064
    3 School of Information Engineering,Chang’an University,Xi’an 710064
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https://castjournals.cast.org.cn/joweb/qcygc/EN/10.19562/j.chinasae.qcgc.2025.02.002
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表12种不同金属材料的力学参数

Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
total species (%)

Genus
种数
Number of
species
占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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