Abstract
Vehicle positioning plays an important role in the design of protocols, algorithms, and applications in the intelligent transport systems. In this paper, we present a new framework of spatiotemporal local-remote sensor fusion (ST-LRSF) that cooperatively improves the accuracy of absolute vehicle positioning based on two state estimates of a vehicle in the vicinity: a local sensing estimate, measured by the on-board exteroceptive sensors, and a remote sensing estimate, received from neighbor vehicles via vehicle-to-everything communications. Given both estimates of vehicle state, the ST-LRSF scheme identifies the set of vehicles in the vicinity, determines the reference vehicle state, proposes a spatiotemporal dissimilarity metric between two reference vehicle states, and presents a greedy algorithm to compute a minimal weighted matching (MWM) between them. Given the outcome of MWM, the theoretical position uncertainty of the proposed refinement algorithm is proven to be inversely proportional to the square root of matching size. To further reduce the positioning uncertainty, we also develop an extended Kalman filter model with the refined position of ST-LRSF as one of the measurement inputs. The numerical results demonstrate that the proposed ST-LRSF framework can achieve high positioning accuracy for many different scenarios of cooperative vehicle positioning.
Highlights
An accurate estimation of real-time vehicle position is a crucial requirement for the success of many protocols, algorithms, and applications of vehicle safety and intelligent transportation systems (ITS) [1,2,3,4].For example, in the medium-access control (MAC) layer, a vehicle at a longer distance from the sending vehicle is likely to be chosen as a relay node for message dissemination [2]
We focus on the vehicle positioning scheme that can reduce the uncertainty of Global Positioning System (GPS) position estimate
We present the framework of spatiotemporal local-remote sensor fusion scheme that can reduce the random GPS error by comparing the relative state measured at the on-board radar with the absolute state received from neighbor vehicles
Summary
An accurate estimation of real-time vehicle position is a crucial requirement for the success of many protocols, algorithms, and applications of vehicle safety and intelligent transportation systems (ITS) [1,2,3,4].For example, in the medium-access control (MAC) layer, a vehicle at a longer distance from the sending vehicle is likely to be chosen as a relay node for message dissemination [2]. An accurate estimation of real-time vehicle position is a crucial requirement for the success of many protocols, algorithms, and applications of vehicle safety and intelligent transportation systems (ITS) [1,2,3,4]. The Galileo system, is widely adopted for vehicle positioning due to the cost efficiency, the global coverage, and the availability of commodity GPS receivers in the marketplace. In this system, both the position and the reference time of a vehicle are estimated by real-time processing of radio-frequency (RF) signals sent from at least four different satellites [5]. We focus on the vehicle positioning scheme that can reduce the uncertainty of GPS position estimate
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