期刊
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
卷 24, 期 8, 页码 8188-8208出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TITS.2023.3264608
关键词
Sensors; Sensor fusion; Data models; Computational modeling; Location awareness; Correlation; Target tracking; Cooperative perception; consensus-based distributed estimation; V2X; connected and automated vehicles
This paper proposes a novel cooperative perception solution based on consensus theory to improve the accuracy and consistency for the detection and tracking of non-connected targets by combining V2X information.
Cooperative perception techniques incorporating Vehicle-to-Everything (V2X) information offer new possibilities for enhancing the perception capability of automated vehicles (AVs), but also present a new challenge of how to maximize the benefits of connected information with limited communication burden. In this context, this paper proposes a novel cooperative perception solution based on consensus theory to improve the accuracy and consistency for the detection and tracking of non-connected targets by combining V2X information. Given the common multi-sensor configurations of AVs, we design a consensus-based distributed cooperative perception (DCP) algorithm in the framework of multi-layer information fusion for local sensors and connected nodes, and give a nonlinear form based on cubature rules to include more accurate nonlinear system models and nonlinear sensors. Considering the high maneuverability of vehicle targets, we then extend the DCP algorithm to a multi-model form (DMMCP) to improve the model uncertainty of maneuvering targets via combining the prior knowledge of multiple models, which also gives a calculation method for model probability and its average consensus in the context of multiple local sensors. Besides, a new consensus information weight strategy and the properties from different consensus information weights are discussed. The simulation results demonstrate the superiority of both our algorithms over the traditional algorithms in accuracy and consistency, moreover, the DMMCP algorithm, which takes into account model uncertainty, shows better performance than DCP in complex conditions.
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