Angle-Awareness Based Joint Cooperative Positioning and Warning for Intelligent Transportation Systems

Area of Science:

• Intelligent Transportation Systems
• Vehicle-to-Infrastructure Communication
• Sensor Fusion

Background:

• Accurate vehicle self-positioning is crucial for intelligent vehicle-infrastructure cooperation frameworks, impacting traffic safety and efficiency.
• Existing cooperative positioning systems often face challenges in computational complexity and accuracy.
• The proposed system leverages angle information for enhanced positioning capabilities.

Discussion:

• A novel joint cooperative positioning and warning (JCPW) system is presented, utilizing angle-of-departure (AOD) measurements.
• The system employs sequential task allocation for cooperative positioning and warning functions.
• A new AOD estimation algorithm based on truncated signal subspace is introduced to improve computational efficiency by avoiding eigen decomposition and spectrum searching.

Key Insights:

• The JCPW system effectively transforms multiple 2D AOD measurements into precise vehicle positions through cooperation with Road Side Units (RSUs).
• A distance-based weighting strategy is utilized to fuse independent AOD estimations, enhancing overall positioning robustness.
• Numerical simulations validate the proposed method's capability for sub-lane level positioning.

Outlook:

• The developed JCPW system offers a promising solution for enhancing the accuracy and efficiency of intelligent vehicle positioning.
• Further research could explore real-world deployment and integration with other intelligent vehicle functions.
• This approach has the potential to significantly advance the development of future cooperative driving systems.