Sensors: Cooperative Perception for Intelligent Vehicles Call for Papers

CALL FOR PAPERS - 
Special Issue "*Cooperative Perception for Intelligent Vehicles*" - 
Sensors (Impact Factor: 3.031)


Deadline for manuscript submissions: 15 May 2020.

https://www.mdpi.com/journal/sensors/special_issues/Intelli_Vechicles

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 sensorís field of vision is blocked (by other vehicles
 or buildin!  gs) 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.

Accepted papers will be published continuously in the journal (as soon
as accepted) and will be listed together on the special issue website.

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

Keywords:

   - Cooperative perception (also known as cooperative sensing or
   collective perception)
   - Connected automated vehicles
   - V2X communications
   - Simulation platforms
   - Experimental testbeds

*Special Issue Editors*

   - Dr. Miguel Sepulcre
   - Dr. Michele Rondinone
   - Dr. Andreas Leich
   - Dr. Meng Lu