Automated Driving with Cooperative Perception Based on CVFH and Millimeter-Wave V2I Communications for Safe and...

Area of Science:

• Automotive Engineering
• Wireless Communications
• Intelligent Transportation Systems

Background:

• Automated driving systems rely on advanced safety services like cooperative perception.
• Connected cars enable vehicle-to-network communication for enhanced services, particularly safety.
• Cooperative perception visualizes blind spots by sharing sensor data, improving intersection safety.

Purpose of the Study:

• To analyze the required sensor data rate for cooperative perception in automated driving at intersections.
• To ensure safe and reliable automated driving through effective blind spot visualization.
• To evaluate wireless communication performance against cooperative perception data demands.

Main Methods:

• Calculated the required sensor data rate based on realistic recognition and crossing decisions for automated vehicles.
• Utilized the Cyclical Feature Histogram (CVFH) to establish stringent data rate requirements.
• Simulated minimum required braking to assess impact on traffic congestion.

Main Results:

• Determined the necessary sensor data rate for cooperative perception in intersection scenarios.
• Compared the required data rate with achievable rates from conventional and millimeter-wave communications.
• Demonstrated that millimeter-wave communications meet the data rate demands for safe crossing at realistic speeds.

Conclusions:

• Millimeter-wave communication technology is viable for supporting cooperative perception in connected automated vehicles.
• Adequate wireless communication performance is critical for the success of safety services in connected car environments.
• This research provides insights into the data rate requirements for reliable automated driving at intersections.