Sensors and Sensor Fusion Technology in Autonomous Vehicles
A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Vehicular Sensing".
Deadline for manuscript submissions: 30 November 2024 | Viewed by 116
Special Issue Editor
Interests: high-performance computing; formal methods; autonomous vehicles; SIMD and SIMT architectures; algorithms for path planning and connectivity; software applications; algorithms and data structures (divide-and-conquer, optimization, estimation, etc.)
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Special Issue Information
Dear Colleagues,
Autonomous (self-driving) vehicles are one of the most significant trends in research and development, with several major automotive companies, research centers, and academic institutions regularly contributing to this field. Sensors (such as cameras, radars, lidars, sonars, global positioning systems or GPSs, inertial measurement units or IMUs, and wheel odometry) collect data that are analyzed by an on-board processor and used to control the speed, steering, and brakes of vehicles. Vehicle control systems may also use information collected by other cars and from environmental maps to make decisions.
As sensors are critical components, the fusion of the information from them and their proper interpretation, followed by the control of the vehicle, are paramount in autonomous driving. Researchers also understand that including more sensors in the sensor fusion system allows for the generation of more performing and robust solutions, but higher costs and reliability problems are the disadvantages of this approach.
The current state of the art in this area includes several aspects, such as 3D object detection methods, moving object detection and tracking systems, occupancy grid mappings for navigation and localization in dynamic environments, data fusion, etc.
The main target of this Special Issue is to collect papers reviewing the state of the art and all significant breakthroughs occurring in these areas.
Dr. Stefano Quer
Guest Editor
Manuscript Submission Information
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Keywords
- sensor fusion
- multi-sensor fusion
- autonomous vehicles
- autonomous driving
- intelligent vehicles
- self-driving cars
- perception
- environmental reconstruction
- data integration
- deep learning
- multi-view
- obstacle detection
- target tracking
- camera
- lidar
- radar
