mlsanityData to 14 Oct 2026

Multi-modal Fusion Technology based on Vehicle Information: A Survey

arXiv 2211.06080 ↗First posted 11 Nov 2022cs.RO cs.CVcited by 13

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Abstract (arXiv)

Multi-modal fusion is a basic task of autonomous driving system perception, which has attracted many scholars' interest in recent years. The current multi-modal fusion methods mainly focus on camera data and LiDAR data, but pay little attention to the kinematic information provided by the bottom sensors of the vehicle, such as acceleration, vehicle speed, angle of rotation. These information are not affected by complex external scenes, so it is more robust and reliable. In this paper, we introduce the existing application fields of vehicle bottom information and the research progress of related methods, as well as the multi-modal fusion methods based on bottom information. We also introduced the relevant information of the vehicle bottom information data set in detail to facilitate the research as soon as possible. In addition, new future ideas of multi-modal fusion technology for autonomous driving tasks are proposed to promote the further utilization of vehicle bottom information.

Key facts. Cited by 13 papers in the map (data through 14 Oct 2026). Builds on 34 of its 104 references that are in the map. First posted on arXiv 11 Nov 2022.

Builds on

34 of 104 references.

References · 34

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22 more
  1. 2018Efficient Low-rank Multimodal Fusion with Modality-Specific Factorscited by 270
  2. 2017Objects that Soundcited by 256
  3. 2018The ApolloScape Open Dataset for Autonomous Driving and its Applicationcited by 190
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 3; 2024: 1; 2025: 8; 2026: 1.

13 papers.

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  1. 2025MMTL-UniAD: A Unified Framework for Multimodal and Multi-Task Learning in Assistive Driving Perceptioncited by 10
  2. 2025Collaborative Perception Datasets for Autonomous Driving: A Reviewcited by 8
  3. 2023SkipcrossNets: Adaptive Skip-cross Fusion for Road Detectioncited by 3
  4. 2025TEM^3-Learning: Time-Efficient Multimodal Multi-Task Learning for Advanced Assistive Drivingcited by 3
  5. 2023Autonomous Driving using Residual Sensor Fusion and Deep Reinforcement Learningcited by 2
  6. 2024Tran-GCN: A Transformer-Enhanced Graph Convolutional Network for Person Re-Identification in Monitoring Videoscited by 2
  7. 2025VM-BHINet:Vision Mamba Bimanual Hand Interaction Network for 3D Interacting Hand Mesh Recovery From a Single RGB Imagecited by 2
  8. 2025DiffPixelFormer: Differential Pixel-Aware Transformer for RGB-D Indoor Scene Segmentationcited by 2
  9. 2025VALISENS: A Validated Innovative Multi-Sensor System for Cooperative Automated Drivingcited by 1
  10. 2025Temporal Misalignment Attacks against Multimodal Perception in Autonomous Drivingcited by 1
  11. 2026Multi-Modal Attention for Automated Disaster Damage Assessment Using Remote Sensing Imagery and Deep Learningcited by 1
  12. 2023Lane Change Intention Recognition and Vehicle Status Prediction for Autonomous Vehiclescited by 0
1 more
  1. 2025CrossRay3D: Geometry and Distribution Guidance for Efficient Multimodal 3D Detectioncited by 0