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Multi-modal Sensor Fusion for Auto Driving Perception: A Survey

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arxiv 2202.02703 v3 pith:IG7SLVP4 submitted 2022-02-06 cs.CV

Keli Huang , Botian Shi , Xiang Li , Xin Li , Siyuan Huang , Yikang Li This is my paper
classification cs.CV
keywords fusionperceptiondrivingmulti-modalautonomousmethodstasksclasses
verification ladder T0 review T1 audit T2 compute T3 formal
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Multi-modal fusion is a fundamental task for the perception of an autonomous driving system, which has recently intrigued many researchers. However, achieving a rather good performance is not an easy task due to the noisy raw data, underutilized information, and the misalignment of multi-modal sensors. In this paper, we provide a literature review of the existing multi-modal-based methods for perception tasks in autonomous driving. Generally, we make a detailed analysis including over 50 papers leveraging perception sensors including LiDAR and camera trying to solve object detection and semantic segmentation tasks. Different from traditional fusion methodology for categorizing fusion models, we propose an innovative way that divides them into two major classes, four minor classes by a more reasonable taxonomy in the view of the fusion stage. Moreover, we dive deep into the current fusion methods, focusing on the remaining problems and open-up discussions on the potential research opportunities. In conclusion, what we expect to do in this paper is to present a new taxonomy of multi-modal fusion methods for the autonomous driving perception tasks and provoke thoughts of the fusion-based techniques in the future.

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Cited by 5 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. RAF: Reliability-Aware Fusion of Camera, LiDAR, and 4D RADAR for Robust 3D Object Detection in Adverse Weather

    cs.CV 2026-07 conditional novelty 6.0 of 10

    Weakly supervised per-pixel reliability gating of camera features improves LiDAR–4D RADAR 3D detection under adverse weather by up to +6.5 AP_BEV / +7.4 AP_3D on K-Radar.

  2. Evaluation of an Uncertainty-Aware Late Fusion Algorithm for Multi-Source Bird's Eye View Detections Under Controlled Noise

    cs.RO 2025-07 conditional novelty 6.0 of 10

    UniKF, a Kalman-filter-based late fusion for BEV detections, achieves lower errors than IoU-based baselines on synthetic noise, but only marginal gains over the authors' own WLS method.

  3. Rethink 3D Object Detection from Physical World

    cs.RO 2025-06 conditional novelty 5.0 of 10

    Latency-aware and planning-aware AP metrics re-rank 3D object detectors for autonomous driving, showing that faster, safer models can beat higher-mAP ones.

  4. Cooperative Perception: A Resource-Efficient Framework for Multi-Drone 3D Scene Reconstruction Using Federated Diffusion and NeRF

    cs.AI 2025-08 reject novelty 4.0 of 10

    The framework claims drone swarms can reconstruct 3D scenes by sharing semantic labels and poses, with a federated diffusion model generating missing views for NeRF training.

  5. AWML: An Open-Source ML-based Robotics Perception Framework to Deploy for ROS-based Autonomous Driving Software

    cs.RO 2025-05 conditional novelty 4.0 of 10

    AWML is a new MLOps integration that connects MMDetection/MMDetection3D models to Autoware/ROS 2 and couples deployment with pseudo-label active learning, demonstrated on private taxi and bus datasets.

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