Advances in Autonomous Driving: Detection and Tracking

Dear Colleagues,

Autonomous driving technologies have developed rapidly over the past decade, yet their perception of dynamic environments remains one of the most critical challenges. The detection and tracking of surrounding objects—such as vehicles, pedestrians, cyclists, and obstacles—are essential for safe and efficient autonomous navigation. Recent developments in computer vision, deep learning, sensor fusion, and 3D scene understanding have significantly enhanced perception systems, enabling more accurate and robust decision-making in complex environments.

This Special Issue aims to gather cutting-edge research related to object detection, multi-object tracking, sensor-based environmental perception, and the integration of these technologies into autonomous driving systems. Topics of interest include, but are not limited to, novel algorithmic approaches, real-time implementation, dataset development, domain adaptation, robustness under challenging conditions (e.g., adverse weather, occlusion), and cross-modal sensor fusion (e.g., LiDAR, radar, camera). Through this Special Issue, we seek to foster academic and industrial collaboration, promoting new ideas that drive the future of autonomous driving forward.

Dr. Jhonghyun An
Prof. Dr. Dimitri Konstantas
Guest Editors

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