Intelligent Vehicles: Advanced Technology and Development
A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Transportation and Future Mobility".
Deadline for manuscript submissions: closed (20 December 2022) | Viewed by 8106
Special Issue Editor
Interests: intelligent machine learning and artificial neural network; intelligent fault diagnosis technology; weak signal identification and intelligent health diagnosis; intelligent vehicle design and lightweight technology
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
The development of intelligent vehicles is essential for improving urban mobility and contributing to the development of smart cities. Both industry and academy have made tremendous advancements in the last decade in this field. Within the area of intelligent vehicles research, there are still many challenges/areas for improvement: perception systems, scene understanding, localization and mapping, navigation, path planning, trajectory planning, vehicle control, etc.
The current scene-understanding technologies and methodologies depend on multiple sensor systems, and there is a broad variety of sensors. GPS, IMU, cameras, radars, and lidars are the most common, and they are based on highly complex and sophisticated algorithms, which include artificial intelligence. These sensors are used for localization (visual odometry, lidar odometry, 3D maps, map matching, etc.), perception (trajectory planning, scene understanding, traffic sign detection, drivable space detection, obstacle avoidance, etc.), and so on.
The aim of this Special Issue is to achieve an insightful perspective of the latest works in these fields, and to provide the reader with a clear picture of the advances that are on the horizon. Welcomed topics include, but are not strictly limited to, the following:
- Vehicle intelligent detection algorithms and control;
- Multi-sensor information and heterogeneous data fusion;
- Computer vision and image processing;
- Lidar and 3D sensors;
- Radar and other proximity sensors;
- Advanced driver assistance systems onboard vehicles;
- Self-driving car perception and navigation systems;
- Intelligent navigation and path planning;
- Intelligent-network vehicles;
- Intelligent interaction integrating driver’s attention and emotions;
- Automatic vehicle trajectory planning and control;
- Human factors and human-machine interactions;
- Fail-safe, fail-aware, and fail-operational systems.
Prof. Dr. Shunming Li
Guest Editor
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Keywords
- computer vision
- lidar
- radar
- 3D perception systems
- convolutional neural networks
- traffic light detection
- collision mitigation brake systems
- driving monitoring system
- visual odometry
- lidar odometry
- 3D maps construction and localization
- scene understanding
- traffic sign detection
- drivable space detection
- obstacle detection
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