Abstract
The rapid development of science and technology has created favorable conditions for Connected and Automated Vehicles (CAVs). Accurate localization is one of the fundamental functions of CAV to realize some advanced operations such as vehicle platooning. However, complicated urban traffic environments, such as the flyover, significantly influence vehicular positioning accuracy. The inability of CAV to accurately perceive self-localization information has become an urgent issue to be addressed. This paper proposed a novel cooperative localization method by introducing the relative Direction-of-Arrival (DOA) and Relative Distance (RD) into CAV to improve the localization accuracy of CAV in the multivehicle environment. First, the three-dimensional positioning error model of the host vehicle concerning adjacent vehicles in azimuth angle and pitch angle and intervehicle distances under the vehicle-to-vehicle communication was established. Second, two least-squares estimation algorithms, linear and nonlinear, are established to decrease the position errors by combining relative DOA and RD measurement information. To verify the proposed algorithm's effect, the PreScan-Simulink joint simulation is carried out. The results show that the host vehicle's localization accuracy by the proposed method can be improved by 25% compared with direct linearization. Besides, by combining relative DOA and relative RD measurement, the locating capability of the least-square-based nonlinear optimization method can be enhanced by 22%.
Highlights
Connected and automated vehicles (CAV) are promising methods worldwide to improve traffic safety, enhance driving comfort, and reduce energy consumption [1,2,3]
As an essential CAV function, accurate localization technology is all-important for CAV to realize high-grade performances such as vehicle platooning [4] and cooperative merging on highway ramps [5, 6] and has been widely studied. e most common localization technique used in CAV is realized by the global navigation satellite system (GNSS)
When cars drive as a group, the small intervehicle distance demands that the vehicular positioning accuracy is centimeter-level [10], which is much higher than the current GNSS road-positioning accuracy level
Summary
A Cooperative Positioning Method of Connected and Automated Vehicles with Direction-of-Arrival and Relative Distance Fusion. Accurate localization is one of the fundamental functions of CAV to realize some advanced operations such as vehicle platooning. Is paper proposed a novel cooperative localization method by introducing the relative Direction-of-Arrival (DOA) and Relative Distance (RD) into CAV to improve the localization accuracy of CAV in the multivehicle environment. Two least-squares estimation algorithms, linear and nonlinear, are established to decrease the position errors by combining relative DOA and RD measurement information. E results show that the host vehicle’s localization accuracy by the proposed method can be improved by 25% compared with direct linearization. By combining relative DOA and relative RD measurement, the locating capability of the least-square-based nonlinear optimization method can be enhanced by 22%
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