ReviewSensors (Basel, Switzerland)2025
A Survey of Deep Learning-Based 3D Object Detection Methods for Autonomous Driving Across Different Sensor Modalities.
Review in Sensors (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
5 citing papers in PubMed.
- Efficient Multitask Onboard Vision Sensing for Open-Pit Mining Advanced Driver Assistance System with Classification-Guided Adaptive Temporal Inference.Sensors (Basel, Switzerland) · 2026Article
- MVDFusion: Multimodal Vehicle Detection in Foggy Weather Using LiDAR and Radar Fusion.Sensors (Basel, Switzerland) · 2026Article
- A Three-Dimensional LiDAR Observability Framework for Pedestrian Representation: Sensor Placement and Multi-View Fusion on a Compact Autonomous Vehicle.Sensors (Basel, Switzerland) · 2026Article
- Sensors in Self-Driving Vehicles: A Detailed Literature Review and New Trends.Sensors (Basel, Switzerland) · 2026Review
- Weighted Copula Entropy for Structural Pruning in Long-Tailed Autonomous Driving Object Detection.Entropy (Basel, Switzerland) · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
This paper presents a comprehensive survey of deep learning-based methods for 3D object detection in autonomous driving, focusing on their use of diverse sensor modalities, including monocular cameras, stereo vision, LiDAR, radar, and multi-modal fusion. To systematically organize the literature, a structured taxonomy is proposed that categorizes methods by input modality. The review also outlines the chronological evolution of these approaches, highlighting major architectural developments and paradigm shifts. Furthermore, the surveyed methods are quantitatively compared using standard evaluation metrics across benchmark datasets in autonomous driving scenarios. Overall, this work provides a detailed and modality-agnostic overview of the current landscape of deep learning approaches for 3D object detection in autonomous driving. Results of this work are available in a github open repository.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.