Robust Inter-Vehicle Distance Measurement Using Cooperative Vehicle Localization

Area of Science:

• Automotive Engineering
• Geomatics Engineering
• Robotics

Background:

• Precise localization is crucial for connected and automated vehicle (CAV) safety.
• Global Navigation Satellite Systems (GNSS) are primary positioning tools, but inter-vehicle distance (IVD) measurement is increasingly important for advanced CAV applications.

Purpose of the Study:

• To propose and evaluate a cooperative localization framework for accurate IVD estimation in CAVs.
• To compare the performance of four IVD estimation methods: Absolute Position Differencing (APD), Pseudorange Differencing (PD), Single Differencing (SD), and Double Differencing (DD).

Main Methods:

• A cooperative localization framework utilizing V2X communication to share absolute position or pseudorange data.
• Implementation and experimental evaluation of APD, PD, SD, and DD methods under static and dynamic conditions.

Main Results:

• The Double Differencing (DD) method demonstrates superior accuracy in static or open-sky dynamic experiments where uncorrelated errors are minimal.
• The Pseudorange Differencing (PD) method proves more effective in mitigating multipath errors caused by blocked GNSS signals, as differential techniques cannot eliminate these uncorrelated errors.

Conclusions:

• The choice of IVD estimation method depends on environmental conditions and error characteristics.
• Cooperative localization via V2X enhances IVD accuracy, with DD and PD offering distinct advantages under different operational scenarios for CAVs.