Emerging trends in autonomous vehicle perception: Multimodal fusion for 3D object detection
Open accessAbstract
The pursuit of autonomous driving relies on developing perception systems capable of making accurate, robust, and rapid decisions to interpret the driving environment effectively. Object detection is crucial for understanding the environment at these systems’ core. While 2D object detection and classification have advanced significantly with the advent of deep learning (DL) in computer vision (CV) applications, they fall short in providing essential depth information, a key element in comprehending driving environments. Consequently, 3D object detection becomes a cornerstone for autonomous driving and robotics, offering precise estimations of object locations and enhancing environmental comprehension. The CV community’s growing interest in 3D object detection is fueled by the evolution of DL models, including Convolutional Neural Networks (CNNs) and Transformer networks. Despite these advancements, challenges such as varying object scales, limited 3D sensor data, and occlusions persist in 3D object detection. To address these challenges, researchers are exploring multimodal techniques that combine information from multiple sensors, such as cameras, radar, and LiDAR, to enhance the performance of perception systems. This survey provides an exhaustive review of multimodal fusion-based 3D object detection methods, focusing on CNN and Transformer-based models. It underscores the necessity of equipping fully autonomous vehicles with diverse sensors to ensure robust and reliable operation. The survey explores the advantages and drawbacks of cameras, LiDAR, and radar sensors. Additionally, it summarizes autonomy datasets and examines the latest advancements in multimodal fusion-based methods. The survey concludes by highlighting the ongoing challenges, open issues, and potential directions for future research.
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Some recent surveys focus primarily on traditional perception tasks such as semantic segmentation or 3D object detection, lacking a systematic review of deep learning-driven cross-modal modeling.
“Some surveys primarily focus on traditional perception tasks such as semantic segmentation [27] or 3D object detection [28], lacking a systematic review of deep learning-driven cross-modal modeling approaches.”
From Multimodal fusion and vision–language models: A survey for robot vision (2026) · cites this paper as [28]
The Abstract and Related Work sections identify this paper as a survey focused on 3D object detection, supporting the description attached to [28]. But the Abstract says it provides an exhaustive review of multimodal fusion methods focused on CNN- and Transformer-based models, and Section 4.2 reviews such methods in detail. The claim that it lacks a systematic review of deep-learning-driven cross-modal approaches is therefore not supported without a narrower qualification about scope. (LLM, conf=0.86)
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Cited by
Sorted by citations
Multimodal fusion and vision–language models: A survey for robot vision
Han, X., Chen, S., Fu, Z., Feng, Z., Fan, L., An, D., Wang, C., Guo, L., Meng, W., Zhang, X., et al. (2026) · Information Fusion · doi:10.1016/j.inffus.2025.103652 · Cited by 41 · see citing works
“On one hand, some surveys, such as Zhang et al.[27] and Alaba et al.[28], focus on specific traditional tasks (e.g., semantic segmentation, 3D detection), lacking systematic coverage of critical robotic tasks such as SLAM, embodied navigation, multimodal 3D perception, and manipulation.”
- Authors
- Alaba, S. Y., Gurbuz, A. C., & Ball, J. E.
- Affiliations
- Mississippi State University (US)
- Department of Electrical and Computer Engineering, James Worth Bagley College of Engineering, Mississippi State University, Starkville, MS 39762, USA
- Corresponding author
- Alaba, Simegnew Yihunie
- Venue
- World Electric Vehicle Journal
- Publisher
- MDPI AG
- Published
- 2024-01-07
- Volume
- 15
- Issue
- 1
- Pages
- 20
- ISSN
- 2032-6653 (ISSN-L)
- OpenAlex
- W4390663385
- Cited by
- 82
- References
- 168
- Citations by year
- 2026: 29 · 2025: 39 · 2024: 14
- FWCI
- 8.84
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- World Electric Vehicle Journal (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Physical Sciences › Engineering › Automotive Engineering › Autonomous Vehicle Technology and Safety
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Vision and Imaging
- Keywords
- 3D object detection, autonomous driving, multimodal fusion, sensor fusion, LiDAR, radar, camera, convolutional neural networks, transformer models, occlusion, object scale variation, autonomous driving datasets
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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