Object Detection for Autonomous Vehicles Applications

Dear Colleagues,

Autonomous vehicles, like most intelligent and autonomous systems, heavily rely on their perception capabilities to understand their surroundings and effectively and safely operate in highly dynamic and potentially unpredictable environments. The decision-making and control processes of autonomous vehicles demand robust and reliable perception performance to ensure the success and mainstream adoption of these systems.

Object detection is one of the critical tasks of an autonomous vehicle perception stack. It serves as the foundation for many safety-critical functions such as obstacle detection, collision avoidance, autonomous emergency braking, pedestrian and vehicle detection, lane changing, traffic monitoring and control, path planning, and trajectory estimation, among others. Given the paramount importance of object detection in these applications, advancements in this field are crucial for enhancing the safety and efficiency of autonomous vehicles. This Special Issue aims to present novel methods and advancements in object detection methodologies tailored to the specific needs of autonomous vehicles.

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but not limited to) the following:

• Robust object detection under adverse weather conditions;
• Robust object detection under varying lighting conditions and highly dynamic range scenes;
• Novel imaging modalities for robust object detection;
• Zero-shot, one-shot, and few-shot object detection for autonomous vehicles;
• Vision–language models for object detection;
• Multi-modal object detection for autonomous vehicle applications;
• Two/three-dimensional (2D/3D) object detection;
• Multi-view object detection;
• Pedestrian detection;
• Vehicle detection;
• Road and traffic sign detection;
• Real-time object detection;
• Energy-efficient object detection methods for autonomous vehicles;
• Augmentation methods for object detection;
• Object detection datasets for autonomous vehicles;
• Benchmarking and evaluation of object detection algorithms;
• Multi-object tracking;
• Sensor fusion techniques for improved object detection.

We look forward to receiving your valuable contributions.

Dr. Zaid A. El Shair
Dr. Samir A. Rawashdeh
Guest Editors

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• object detection
• autonomous vehicles
• adverse weather conditions
• multi-modal perception
• sensor fusion
• robust detection
• artificial intelligence
• deep learning