Implicit Cooperative Positioning in Vehicular Networks

Abstract

Absolute positioning of vehicles is based on Global Navigation Satellite Systems (GNSS) combined with on-board sensors and high-resolution maps. In Cooperative Intelligent Transportation Systems (C-ITS), the positioning performance can be augmented by means of vehicular networks that enable vehicles to share location-related information. This paper presents an Implicit Cooperative Positioning (ICP) algorithm that exploits the Vehicle-to-Vehicle (V2V) connectivity in an innovative manner, avoiding the use of explicit V2V measurements such as ranging. In the ICP approach, vehicles jointly localize non-cooperative physical features (such as people, traffic lights or inactive cars) in the surrounding areas, and use them as common noisy reference points to refine their location estimates. Information on sensed features are fused through V2V links by a consensus procedure, nested within a message passing algorithm, to enhance the vehicle localization accuracy. As positioning does not rely on explicit ranging information between vehicles, the proposed ICP method is amenable to implementation with off-the-shelf vehicular communication hardware. The localization algorithm is validated in different traffic scenarios, including a crossroad area with heterogeneous conditions in terms of feature density and V2V connectivity, as well as a real urban area by using Simulation of Urban MObility (SUMO) for traffic data generation. Performance results show that the proposed ICP method can significantly improve the vehicle location accuracy compared to the stand-alone GNSS, especially in harsh environments, such as in urban canyons, where the GNSS signal is highly degraded or denied.

Distributed Direct Localization Suitable for Dense Networks

“…The agent a u has a uniformly distributed a priori belief of its location b u by the received signals r uv , ∀a v ∈ V u . As mentioned in Section III, the updated logarithmic belief can be expressed with the summation of the SCFs of all links as log b (1) u…”

“…Ubiquitous realtime location information in wireless networks is essential for a wide range of applications, from mass-market location-aware services like autonomous driving [1], crowd sensing [2], communication enhancement [3] and internet of things (IoT) [4], [5], to professional multi-agent collaborations such as disaster management [6], environmental sensing [7] and extraterrestrial robotic swarm exploration [8], [9]. Many of these applications are global navigation satellite system (GNSS)-impaired, which leads to challenges in localization.…”

Beyond GNSS: Highly accurate localization for cooperative-intelligent transport systems

“…This problem is challenging as vehicles may not be able to perceive the complete s k and g(•, •) may be invariant to permutations of the objects (as objects have no natural identifier). The ICP determines approximations of p(x i,k |y k , z k ) through Bayesian filtering, as described in [3], [4].…”

Heterogeneous Network Localization with a Distributed Phased Array Composed of Cooperative Vehicles

“…Therefore, agents can exploit the targets' location information to further improve their own position estimation, especially when multiple targets have been localized. A similar concept is introduced in [15], where external features are exchanged to improve network localization. Interestingly, even though a target does not actively cooperate with other vehicles, it indirectly benefits from other targets through an improved agent formation estimation.…”