Towards Autonomous Driving: Technologies and Data for Vehicles-to-Everything Communication
View abstract on PubMed
Summary
This summary is machine-generated.Autonomous systems in smart cities require robust communication between traffic objects. This review examines vehicular communication systems, identifying gaps for future development in sensors, standards, and AI.
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.
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