Road Vehicles Surroundings Supervision: On-Board Sensors and Communications

Dear Colleagues,

New assistance systems, cooperative and autonomous driving of road vehicles, imply an accurate knowledge of vehicle surroundings. This knowledge can come from different sources, such as on-board sensors, sensors in the infrastructure, and communications.

Among onboard sensors, short- and long-range sensors can be distinguished. In the first case, ultrasonic, infrared, and capacitive sensors can be cited. Among the second group, laser scanners and computer vision technologies appear to provide the best performance, although there are many others that can complement the information using data fusion processes. In any case, the goal is to have a representation of vehicle surroundings that is as complete as possible. In any case, sensor fusion algorithms are a common solution to overcome the single sensors limitations.

Vehicle positioning is also essential. For this purpose, satellite positioning is commonly used, but when it is not sufficiently reliable or the signal is lost, the same technologies as those used for the recognition of the surroundings can provide an acceptable solution. In this regard, it should be noted that the SLAM (Simultaneous Localization and Mapping) problem, tries to build or update the map of an environment that is not known a priori, and positioning the vehicle on that map simultaneously. This technique, widely used and proven in robotics, has also been implemented in the vehicular field for the perception of the environment in real time, support, and accuracy improvements in autonomous GNSS navigation, and generation of digital maps or calculation of trajectories followed when no GPS signal is received.

Vehicle-to-vehicle (V2V) communications and vehicle-to-infrastructure (V2I) communications allow the vehicle to have greater knowledge of surroundings and to obtain information that is far away from the onboard sensors. These communications provide additional data that could be used for driver information purposes or for decision modules in autonomous driving, for example. In this sense, connected and cooperative driving appears as a catalyst of autonomous driving, because it enables real deployment under complex driving situations.

Apart from original research related to the topic, studies on the state-of-the-art in relation to previous works are also welcome.

In conclusion, the aim of this Special Issue is to bring together innovative developments related to the use of technologies in road vehicles to obtain information about vehicle surroundings, mainly for vehicle surroundings supervision and reconstruction, including, but not limited to:

• Laser scanner for automotive uses
• Computer vision for automotive uses
• Vehicle surroundings supervision
• Vehicle surroundings reconstruction
• Obstacles detection
• Trajectory estimation
• Data fusion
• Visual SLAM
• Driver assistance systems
• Autonomous driving
• V2X communications
• Connected vehicles
• Cooperative driving

Authors are invited to contact the Guest Editor, prior to submission, if they are uncertain whether their work falls within the general scope of this Special Issue.

Dr. Felipe Jimenez
Guest Editor

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