Towards Autonomous Driving: Technologies and Data for Vehicles-to-Everything Communication.

Area of Science:

• Intelligent Transportation Systems
• Autonomous Systems Engineering

Background:

• Autonomous systems are increasingly integrated into daily life, particularly in transportation.
• Smart city concepts introduce complex challenges for autonomous systems, especially regarding inter-object communication.
• Effective communication and decision-making are crucial when dynamic objects and multiple autonomous systems interact in traffic.

Purpose of the Study:

• To review vehicular communication systems for autonomous applications.
• To identify existing gaps in sensors, communication standards, devices, and machine learning methods for future development.
• To analyze vehicular-related data to inform advancements in communication systems.

Main Methods:

• Literature review of vehicular communication systems.
• Analysis of sensors, communication standards, and machine learning methods used in autonomous vehicles.
• Examination of vehicular-related data and existing research gaps.

Main Results:

• The study reviews current vehicular communication technologies and methodologies.
• It highlights the importance of sensor fusion and AI machine learning in integrating autonomous systems.
• Key research gaps in vehicular communication systems are identified.

Conclusions:

• Further research is needed to address identified gaps in vehicular communication systems.
• Advancements in sensors, standards, and AI are critical for enhancing autonomous transportation.
• Developing functional and trustworthy communication is essential for safe and efficient smart city mobility.