Recent Applications of Computer Vision for Advanced Driver Assistance System (ADAS)
A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Transportation and Future Mobility".
Deadline for manuscript submissions: 30 June 2025 | Viewed by 507
Special Issue Editors
Interests: intelligent perception for UAS; computer vision; vision-based intelligent surveillance
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
As one of the key technologies used in autonomous vehicles, advanced driver assistance systems (ADASs) are designed to automate, adapt, and enhance vehicle technology for safety and better driving. Computer vision-based ADASs utilize vision sensors to capture images with rich information and extract significant information through advanced and complicated image processing. Over the last three decades, several computer vision tasks have been applied to existing ADASs: depth estimation, object/obstacles detection and tracking, traffic state sensing, and traffic behavior understanding. In recent years, with the rapid development of deep learning technology, the algorithmic performances of vision-based ADASs have been further improved. However, several challenges and difficulties need to be addressed, such as creating real-time and lightweight deep learning networks for computer vision-based ADASs, risk assessments for vison algorithms for ADASs, the robustness of vision algorithms under different driving conditions, and hardware platforms for vision algorithms for ADASs. Therefore, this Special Issue is interested in articles, reviews, and reports that present the algorithms, theories and applications of computer vision for ADASs. Potential topics include, but are not limited to, the following:
- Computer vision-based enviromment perception;
- Information fusion for ADASs;
- Object detection for autonomous driving;
- Depth estimation via deep learning;
- Lightweight network design for ADASs;
- Behavior understanding;
- Image processing for autonomous driving;
- Risk assessment of computer vison for ADASs;
- System integration of computer vision-based ADASs.
We encourage the submission of original works highlighting the latest research and technical developments. Moreover, review papers and comparative studies are also welcome.
Prof. Dr. Meng Ding
Prof. Dr. Yunfeng Cao
Guest Editors
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Keywords
- computer vision
- advanced driver assistance system
- environment perception
- object detection
- depth estimation
- behavior understanding
- risk assessment
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