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Multimodal Fusion Method Based on Confidence Estimation Network and Improved D-S Theory
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Teng CHENG1, 2, 3, Ligang GUO1, 2, 3, Qiang ZHANG1, 2, 3, 4, Wenchong WANG4, Qin SHI1, 2, 3, Dengchao HOU1, 2, 3
Chinese Journal of Automotive Engineering | 2025, 15(2) : 137 - 146
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Chinese Journal of Automotive Engineering | 2025, 15(2): 137-146
• Intelligent & Connected Technologies Section/Editor in Chief：GAO Zhenhai •
Multimodal Fusion Method Based on Confidence Estimation Network and Improved D-S Theory
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Teng CHENG1, 2, 3, Ligang GUO1, 2, 3, Qiang ZHANG1, 2, 3, 4, Wenchong WANG4, Qin SHI1, 2, 3, Dengchao HOU1, 2, 3
Affiliations
  • 1 Key Laboratory for Automated Vehicle Safety Technology of Anhui Province，Hefei University of Technology，Hefei 230009，China
  • 2 Engineering Research Center for Intelligent Transportation and Cooperative Vehicle-Infrastructure of Anhui Province，Hefei 230009，China
  • 3 School of Automotive and Transportation Engineering，Hefei University of Technology，Hefei 230009，China
  • 4 Chery Automobile Co.，Ltd.，Wuhu 241007，Anhui，China
Published: 2025-03-20 doi: 10.3969/j.issn.2095‒1469.2025.02.02
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Neural networks lack interpretability and the D-S theory is prone to paradoxes in high-conflict scenarios of multimodal fusion. In response, this paper proposes a result-level multimodal fusion method that integrates a confidence estimation network with an improved D-S theory. The method consist of two key components. First, a confidence estimation network reframes the classification problem in target detection as a confidence estimation task, providing confidence scores for the detection results of individual unimodal networks. Second, a fusion method with improved D-S theory uses confidence scores and class information to construct evidence, achieving final fusion of detection data from different modalities. Evaluation experiments on the KITTI dataset show that the proposed fusion method improves mAP by up to 6.64% compared to image-based detection and up to 15.43% compared to point cloud-based detection. In the comparison of fusion methods, the proposed fusion method achieves an mAP improvement 0.81% higher than the classical D-S fusion. It effectively reduces classification conflicts and addresses the limitations of the classical D-S theory.

confidence estimate  /  environment perception  /  multimodal target detection  /  dempster-shafer evidence theory  /  result-level multimode fusion
Teng CHENG, Ligang GUO, Qiang ZHANG, Wenchong WANG, Qin SHI, Dengchao HOU. Multimodal Fusion Method Based on Confidence Estimation Network and Improved D-S Theory[J]. Chinese Journal of Automotive Engineering, 2025 , 15 (2) : 137 -146 . DOI: 10.3969/j.issn.2095‒1469.2025.02.02
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Year 2025 volume 15 Issue 2
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Article Info
doi: 10.3969/j.issn.2095‒1469.2025.02.02
  • Receive Date：2023-12-02
  • Online Date：2025-07-20
  • Published：2025-03-20
Article Data
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History
  • Received：2023-12-02
  • Revised：2024-02-21
Funding
Affiliations
    1 Key Laboratory for Automated Vehicle Safety Technology of Anhui Province，Hefei University of Technology，Hefei 230009，China
    2 Engineering Research Center for Intelligent Transportation and Cooperative Vehicle-Infrastructure of Anhui Province，Hefei 230009，China
    3 School of Automotive and Transportation Engineering，Hefei University of Technology，Hefei 230009，China
    4 Chery Automobile Co.，Ltd.，Wuhu 241007，Anhui，China
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