mlsanityData to 14 Oct 2026

Emergent Visual Sensors for Autonomous Vehicles

arXiv 2205.09383 ↗First posted 19 May 2022cs.CV cs.ROcited by 19

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Abstract (arXiv)

Autonomous vehicles rely on perception systems to understand their surroundings for further navigation missions. Cameras are essential for perception systems due to the advantages of object detection and recognition provided by modern computer vision algorithms, comparing to other sensors, such as LiDARs and radars. However, limited by its inherent imaging principle, a standard RGB camera may perform poorly in a variety of adverse scenarios, including but not limited to: low illumination, high contrast, bad weather such as fog/rain/snow, etc. Meanwhile, estimating the 3D information from the 2D image detection is generally more difficult when compared to LiDARs or radars. Several new sensing technologies have emerged in recent years to address the limitations of conventional RGB cameras. In this paper, we review the principles of four novel image sensors: infrared cameras, range-gated cameras, polarization cameras, and event cameras. Their comparative advantages, existing or potential applications, and corresponding data processing algorithms are all presented in a systematic manner. We expect that this study will assist practitioners in the autonomous driving society with new perspectives and insights.

Key facts. Cited by 19 papers in the map (data through 14 Oct 2026). Builds on 70 of its 247 references that are in the map. First posted on arXiv 19 May 2022.

Builds on

70 of 247 references.

References · 70

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38 more
  1. 2018Unsupervised Event-based Learning of Optical Flow, Depth, and Egomotioncited by 307
  2. 2021DSEC: A Stereo Event Camera Dataset for Driving Scenarioscited by 303
  3. 2018The Multi Vehicle Stereo Event Camera Dataset: An Event Camera Dataset for 3D Perceptioncited by 254
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 2; 2024: 6; 2025: 5; 2026: 6.

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  1. 2023Enhancing Traffic Object Detection in Variable Illumination with RGB-Event Fusioncited by 16
  2. 2024Distilling Diffusion Models to Efficient 3D LiDAR Scene Completioncited by 10
  3. 2024MULi-Ev: Maintaining Unperturbed LiDAR-Event Calibrationcited by 7
  4. 2024Talk to Parallel LiDARs: A Human-LiDAR Interaction Method Based on 3D Visual Groundingcited by 5
  5. 2024Fisheye Camera and Ultrasonic Sensor Fusion For Near-Field Obstacle Perception in Bird's-Eye-Viewcited by 2
  6. 2024Optical Flow Matters: an Empirical Comparative Study on Fusing Monocular Extracted Modalities for Better Steeringcited by 2
  7. 2025Multi-Timescale Motion-Decoupled Spiking Transformer for Audio-Visual Zero-Shot Learningcited by 2
  8. 2023EANet: Enhanced Attribute-based RGBT Tracker Networkcited by 0
  9. 2024Camera-Based Localization and Enhanced Normalized Mutual Informationcited by 0
  10. 2025Planar Velocity Estimation for Fast-Moving Mobile Robots Using Event-Based Optical Flowcited by 0
  11. 2025EIFNet: Leveraging Event-Image Fusion for Robust Semantic Segmentationcited by 0
  12. 2025A Survey on Deep Multi-Task Learning in Connected Autonomous Vehiclescited by 0
7 more
  1. 2025MSGFusion: Multimodal Scene Graph-Guided Infrared and Visible Image Fusioncited by 0
  2. 2026No Calibration, No Depth, No Problem: Cross-Sensor View Synthesis with 3D Consistencycited by 0
  3. 2026PatchScene: Patch-based Voxel Diffusion for Large-Scale Scene Completioncited by 0
  4. 2026DeLux: Cross-Modal Local Artifact Restoration in Video Using Neuromorphic Datacited by 0
  5. 2026Image Quality Dependent Degradation for AI Systemscited by 0
  6. 2026CORAL: Curriculum-Optimized Reward Adaptation for LiDAR-Based Goal-Directed Urban Drivingcited by 0
  7. 2026SenseWeather: Multimodal Sensing for Robust Robotic Perception under Extreme Conditionscited by 0