Computer Vision Applications in Autonomous Vehicles
Special Issue Information
Dear Colleagues,
Computer vision has become a core enabling technology for autonomous vehicles, providing critical capabilities for perception, understanding, and decision-making in complex traffic environments. With the rapid advancement of sensing hardware, deep learning architectures, and large-scale data-driven methods, vision-based systems are increasingly responsible for detecting, tracking, and interpreting dynamic road users, infrastructure elements, and environmental conditions.
This Special Issue aims to present recent theoretical advances, methodological innovations, and practical applications of computer vision in autonomous driving systems. Topics of interest include, but are not limited to, visual perception under adverse and corner scenarios, multi-task learning for scene understanding, vision-based localization and mapping, and robust perception for safety-critical autonomous driving. Particular attention will be given to methods that improve generalization, interpretability, and reliability, as well as approaches that integrate vision with multi-modal sensing, simulation, and scenario-based testing frameworks.
By bringing together contributions from academia and industry, this Special Issue seeks to highlight cutting-edge research that advances the deployment of reliable and scalable autonomous vehicle technologies, and to foster cross-disciplinary collaboration between computer vision, robotics, and intelligent transportation systems.
Dr. Peixing Zhang
Dr. Jin Chen
Dr. Yuande Jiang
Prof. Dr. Xin Cheng
Guest Editors
Manuscript Submission Information
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Keywords
- vision-based perception for autonomous driving
- object detection and tracking in traffic scenes
- semantic and panoptic scene understanding
- visual localization and mapping (V-SLAM)
- corner and extreme scenario perception
- multi-modal sensor fusion with vision
- learning-based lane and road structure detection
- vision-driven motion prediction and intent recognition
- simulation and synthetic data for vision systems
- safety, robustness, and reliability of vision-based system
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