Cooperative vehicle positioning with multi-sensor data fusion and vehicular communications

Hossain, Akram; Elshafiey, Ibrahim; Alsanie, Abdulhameed

doi:10.1007/s11276-018-1772-6

Cited by 30 publications

(17 citation statements)

References 17 publications

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“…From Equation (22), it can be seen that the position error does not affect the distance measurement distribution between cooperative nodes. According to the approximate result of the Taylor-expanded distance measurement shown in Equation (17), d AD , d BD , d BC , and d AC represent the ideal measurement distances between cooperative nodes as follows:…”

Section: Information-geometry-assisted Cooperative Positioning (Ig-cp)mentioning

confidence: 99%

“…Therefore, the centralized cooperative positioning technology usually ignores the stability of node positioning accuracy. A multisensor data fusion cooperative position (MSDFC) algorithm is proposed in [22]. The cooperative node transfers the positioning data to the central node; then, optimal estimation of the positioning results of all nodes in the whole cooperative network is achieved.…”

Section: Introductionmentioning

confidence: 99%

“…These methods can effectively improve the positioning accuracy of the participating nodes, but they have higher requirements for ranging and communication performance. In [22][23][24]. Centralized cooperative positioning technology has reached the bottleneck stage.…”

Section: Introductionmentioning

confidence: 99%

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Geometric-Manifold-Assisted Distributed Navigation Probabilistic Information Fusion Cooperative Positioning Algorithm

et al. 2021

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Positioning information is the cornerstone of a new generation of electronic information technology applications represented by the Internet of Things and smart city. However, due to various environmental electromagnetic interference, building shielding, and other factors, the positioning source can fail. Cooperative positioning technology can realize the sharing of positioning information and make up for the invalid positioning source. When one node in the cooperative positioning network has error, the positioning stability of all nodes in the whole cooperative network will be significantly reduced, but the positioning probability information technology can effectively reduce the impact of mutation error. Based on this idea, this paper proposes an information-geometry-assisted distributed algorithm for probabilistic cooperative fusion positioning (IG-CP) of navigation information. The position information of different types of navigation sources is utilized to establish a probability density model, which effectively reduces the influence of a single position error on the whole cooperative position network. Combined with the nonlinear fitting characteristics of the information geometric manifold, mapping and fusion of the ranging information between cooperative nodes on the geometric manifold surface are conducted to achieve cooperative positioning, which can effectively improve the stability of the positioning results. The proposed algorithm is simulated and analyzed in terms of the node positioning error, ranging error, convergence speed, and distribution of the cooperative positioning network. The simulation results show that our proposed cooperative positioning algorithm can effectively improve the positioning stability and display better positioning performance.

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Section: Information-geometry-assisted Cooperative Positioning (Ig-cp)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Geometric-Manifold-Assisted Distributed Navigation Probabilistic Information Fusion Cooperative Positioning Algorithm

et al. 2021

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“…For what concerns the GPS positioning error, the target value for Collision Avoidance services is set to less than 1 m [13]. This value can be achieved through modern positioning systems, by exploiting the fusion of data coming from multiple sensors (e.g., GPS receiver as well as automotive radar [14,Fig. 7]).…”

Section: Reference Scenario and Simulationsmentioning

confidence: 99%

Edge-Based Collision Avoidance for Vehicles and Vulnerable Users: An Architecture Based on MEC

Malinverno

Avino

Casetti

et al. 2020

IEEE Veh. Technol. Mag.

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Collision avoidance is one of the most promising applications for vehicular networks, dramatically improving the safety of the vehicles that support it. In this paper, we investigate how it can be extended to benefit vulnerable users, e.g., pedestrians and bicycles, equipped with a smartphone. We argue that, owing to the reduced capabilities of smartphones compared to vehicular on-board units, traditional distributed approaches are not viable, and that multi-access edge computing (MEC) support is needed. Thus, we propose a MEC-based collision avoidance system, discussing its architecture and evaluating its performance. We find that, thanks to MEC, we are able to extend the protection of collision avoidance, traditionally thought for vehicles, to vulnerable users without impacting its effectiveness or latency.

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“…And the results showed that the method exhibits significant improvement in terms of positioning accuracy. A systematic approach [ 13 ] to the cooperation of connected vehicles at unsignalized intersections without global coordination. A task-area partition framework is proposed to decompose the mission of cooperative passing into three main tasks, i.e., vehicle state observation, arriving time optimization, and trajectory tracking control.…”

Section: Introductionmentioning

confidence: 99%

Development of a conflict-free unsignalized intersection organization method for multiple connected and autonomous vehicles

Shu

Zhou

2021

PLoS ONE

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An effective traffic control strategy will improve travel reliability in urban transportation networks. Lack of coordination between vehicles, however, exacerbates congestion due mainly to frequent stops at unsignalized intersections. It is beneficial to develop a conflict-free cooperation method that collects basic safety message from multiple approaching Connected and Autonomous Vehicles (for short, CAVs) and guarantees efficient unsignalized intersection operations with safe and incident free vehicle maneuvers. This paper proposes an interspersed traffic organization method under controlled constraints. Firstly, relied on shared location technology and considered the operating characteristics of CAVs at unsignalized intersections to detect and analyze traffic conflicts to establish a right-of-way judgment model for CAVs. In order to further ensure the safety and operating efficiency of the vehicle, based on the judgment results of right-of-way judgment model, a vehicle speed guidance model is established for different traffic conditions. Taking the real city standard intersection as the experimental analysis object, through data collection and simulation experiment, the signal control method and the organization method proposed in this paper are compared and analyzed. The results showed that the traffic organization method proposed in this paper improves the operational efficiency of 46%, the average travel time is reduced by 6.54s, which is not only better than the signal control method, but also supports the development of car networking technology.

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Cooperative vehicle positioning with multi-sensor data fusion and vehicular communications

Cited by 30 publications

References 17 publications

Geometric-Manifold-Assisted Distributed Navigation Probabilistic Information Fusion Cooperative Positioning Algorithm

Geometric-Manifold-Assisted Distributed Navigation Probabilistic Information Fusion Cooperative Positioning Algorithm

Edge-Based Collision Avoidance for Vehicles and Vulnerable Users: An Architecture Based on MEC

Development of a conflict-free unsignalized intersection organization method for multiple connected and autonomous vehicles

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