A Cooperative Localization Method based on V2I Communication and Distance Information in Vehicular Networks

Abstract

Relative positions are recent solutions to overcome the limited accuracy of GPS in urban environment. Vehicle positions obtained using V2I communication are more accurate because the known roadside unit (RSU) locations help predict errors in measurements over time. The accuracy of vehicle positions depends more on the number of RSUs; however, the high installation cost limits the use of this approach. It also depends on nonlinear localization nature. They were neglected in several research papers. In these studies, the accumulated errors increased with time due to the linearity localization problem. In the present study, a cooperative localization method based on V2I communication and distance information in vehicular networks is proposed for improving the estimates of vehicles’ initial positions. This method assumes that the virtual RSUs based on mobility measurements help reduce installation costs and facilitate in handling fault environments. The extended Kalman filter algorithm is a well-known estimator in nonlinear problem, but it requires well initial vehicle position vector and adaptive noise in measurements. Using the proposed method, vehicles’ initial positions can be estimated accurately. The experimental results confirm that the proposed method has superior accuracy than existing methods, giving a root mean square error of approximately 1 m. In addition, it is shown that virtual RSUs can assist in estimating initial positions in fault environments.

Adaptive Kalman Filter for Free GPS Localization with Fuzzy Intersection Method

“…Furthermore, the fuzzy model is used to update the noise in the measurements. The experimental results show that the proposed method against other related research papers [9,25,26]. In complex road network configurations, the proposed method gives a positional accuracy of about 2 m compared to 5, 7, and 10 m provided in other studies.…”

“…The purpose of this section is to study the effect of KF-adapted parameters in the proposed method on vehicle position accuracy and compares to our previous work [9]. At the same time, studying the integration between KF and fuzzy logic effect in adapting noise covariance matrixes instead of using them constant.…”

“…Additionally, proposed method also compares to other related research papers [25,26] to test the efficiency in handling nonlinear problems on position accuracy. In [9], the authors used classical formula of the intersection method to provide a good initial state vector. EKF linearize the distance function and reduce noise in measurement.…”

“…They were placed at (600, 10) and (400, 800) in [25,26], respectively. They were also placed at (0, 1500) and (12, 1500) in [9] and in the proposed cooperative localization, respectively. Fig 4 shows the RMSE values for distance errors ranging from 0.5 to 3 m. The RMSE of the proposed cooperative localization is lower than those of the localization algorithms in [9,25,26].…”

“…The accuracy of KF is reduced owing to the lack of priori knowledge of how to adapt noise covariance. In our previous study [9], EKF was used to improve the initial vehicle positions estimated by the V2I communication and intersection method. Vehicles received beacon messages from at least two RSUs.…”