Crowdsourcing-Assisted Radio Environment Database for V2V Communication.

Area of Science:

• Wireless Communication Systems
• Autonomous Driving Technology
• Radio Propagation Modeling

Background:

• Reliable Vehicle-to-Vehicle (V2V) communication is crucial for autonomous driving.
• Accurate radio propagation estimation is challenging due to environmental factors like buildings and geography.
• Current wireless systems struggle with the localized nature of radio propagation.

Purpose of the Study:

• To propose a novel measurement-based radio environment database.
• To enhance the accuracy of radio environment estimation for V2V communication systems.
• To address the limitations of existing methods in predicting radio propagation characteristics.

Main Methods:

• Gathering measurement datasets of received signal strength indicator (RSSI) from V2V systems, including transmission and reception locations.
• Generating average received power maps based on collected V2V datasets.
• Conducting real-world V2V communication measurement campaigns to collect RSSI data for database construction.

Main Results:

• The proposed database successfully gathered and utilized V2V measurement data.
• Average received power maps were generated, correlating signal strength with transmitter and receiver locations.
• The measurement-based database demonstrated higher accuracy in radio propagation estimation compared to conventional path loss models.

Conclusions:

• The developed measurement-based radio environment database significantly improves radio propagation estimation accuracy for V2V systems.
• This approach offers a more reliable method for understanding and predicting radio environments in autonomous driving scenarios.
• The findings suggest a practical solution for overcoming the challenges of localized radio propagation in complex environments.