Advanced Imaging in Autonomous Vehicle and Intelligent Driving
A special issue of Journal of Imaging (ISSN 2313-433X).
Deadline for manuscript submissions: 30 June 2025 | Viewed by 118
Special Issue Editors
Interests: autonomous driving; embodied intelligence; large models; representation learning
Special Issue Information
Dear Colleagues,
The integration of advanced imaging technologies plays a pivotal role in shaping the future of autonomous vehicles and intelligent driving systems. These technologies encompass diverse cutting-edge methods, including high-resolution cameras, LiDAR (Light Detection and Ranging), radar systems, event cameras, spike cameras, and emerging sensor fusion techniques. Their seamless integration enhances perception capabilities and fundamentally shapes the safety, efficiency, and reliability of autonomous vehicles on our roads.
Perceiving the 3D surroundings accurately and comprehensively from different sensors equips the autonomous driving system with spatial intelligence to reason about spatial relations, where the core of 3D scene perception lies in how to effectively represent a 3D scene, e.g., voxels, bird’s eye view, tri-perspective view, or 3D Gaussians. This Special Issue seeks to consolidate the latest advancements and research breakthroughs in scene understanding from different sensors for autonomous vehicles. By exploring data perception algorithms, the intricate interplay between sensors, and real-world applications, this collection aims to provide a comprehensive overview of how advanced imaging transforms the landscape of autonomous vehicle development.
We are pleased to invite researchers, engineers, and experts from academia and the industry to contribute original research articles, reviews, and perspectives that explore the diverse facets of advanced imaging and scene perception in the context of autonomous vehicle development.
We look forward to receiving your contributions.
Dr. Wenzhao Zheng
Dr. Linqing Zhao
Guest Editors
Manuscript Submission Information
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Keywords
- 3D scene representation for autonomous driving
- 3D scene reconstruction in outdoor scenes
- 3D occupancy prediction
- end-to-end autonomous driving
- world models for 3D scene evolution modeling
- multi-sensor fusion for perception and environment understanding
- high-resolution imaging for object detection and recognition
- real-time imaging processing and computational efficiency
- simultaneous localization and mapping (SLAM) using imaging sensors
- 3D imaging technologies and their applications in navigation
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