Cooperative Perception for Intelligent Vehicles
Deadline for manuscript submissions: closed (15 June 2021) | Viewed by 43051
Interests: V2X communications; congestion control; heterogeneous wireless networks; industrial wireless networks; 5G; LTE-V; C-V2X
Interests: V2X; C-ITS; Cooperative Automated driving; Vehicular Ad Hoc Networks; Wireless Sensor Networks
Interests: Image processing and data fusion for applications in intelligent transportation systems
Automated vehicles are expected to have a significant impact in the transport sector in the next decades, improving road safety and traffic efficiency, as well as reducing energy consumption and improving user comfort. Automated vehicles make use of a set of onboard sensors installed in the vehicle (e.g. camera, radar and lidar) that are responsible for perceiving the surrounding environment, and a set of actuators that control its longitudinal and lateral movements. One of the development objectives is to automatically perform driving tasks with less or even without driver intervention. Several studies have already shown that the sensors used in the perception process have limitations that might degrade the performance of automated vehicles. For example, in adverse weather conditions (such as rain, snow and fog), the cameras will not be able to adequately capture the environment, and in situations where the sensor’s field of vision is blocked (by other vehicles or buildings) none of the current sensors can detect beyond the position of the obstacle. To overcome these limitations and improve the perception capabilities of the vehicles, cooperative perception enables the wireless exchange of sensor information between vehicles and between vehicles and infrastructure nodes. Cooperative perception, also known as cooperative sensing or collective perception, enables vehicles and infrastructure nodes to detect objects (e.g. non-connected vehicles, pedestrians, obstacles) beyond their local sensing capabilities. Cooperative perception can be key for extended and timely detection of the surrounding environment and can also enable cooperative applications by compensating low penetration rates of connected road users, thus facilitating the future deployment of automated vehicles.
The purpose of this Special Issue is to present and discuss major research challenges, latest developments, and recent advances on cooperative perception. This Special Issue solicits the submission of high-quality papers from academia and industry that aim to solve open technical problems or challenges in the context of cooperative perception. Original and innovative contributions on all aspects, both theoretical and experimental, are all welcome.
Potential topics include, but are not limited to, the following:
- Application development and validation based on cooperative perception
- V2X communication algorithms and protocols for cooperative perception
- Communication technologies for cooperative perception
- Radio resource allocation for cooperative perception
- Congestion control for cooperative perception
- Infrastructure-assisted solutions for cooperative perception
- Security analysis and algorithms for cooperative perception
- Artificial intelligence and machine learning-based cooperative perception
- Sensor design and configuration for cooperative perception
- Sensor architectures and technologies for cooperative perception
- The impact of sensor data and sensor data fusion quality on the effectiveness of cooperative perception
- Sensor data fusion problems, algorithms and architectures in the context of cooperative perception
- Simulation platforms and experimental testbeds for cooperative perception
Dr. Miguel Sepulcre
Dr. Michele Rondinone
Dr. Andreas Leich
Dr. Meng Lu
Manuscript Submission Information
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- Cooperative perception (also known as cooperative sensing or collective perception)
- Connected automated vehicles
- V2X communications
- Simulation platforms
- Experimental testbeds