Multi-Vehicle Cooperative Target Tracking with Time-Varying Localization Uncertainty via Recursive Variational Bayesian Inference

Abstract

Cooperative target tracking by multiple vehicles connected through inter-vehicle communication is a promising way to improve the estimation of target state. The effectiveness of cooperative tracking closely depends on the accuracy of relative localization between host and cooperative vehicles. However, the localization signal usually provided by the satellite-based navigation system is rather susceptible to dynamic driving environment, thus influencing the effectiveness of cooperative tracking. In order to implement reliable cooperative tracking, especially when the statistical characteristic of the relative localization noise is time-varying and uncertain, this paper presents a recursive Bayesian framework which jointly estimates the state of the target and the cooperative vehicle as well as the localization noise parameter. An online variational Bayesian inference algorithm is further developed to achieve efficient recursive estimate. The simulation results verify that our proposed algorithm can effectively boost the accuracy of target tracking when the localization noise dynamically changes over time.

Multi-Layered Local Dynamic Map for a Connected and Automated In-Vehicle System

“…Significant contributions to CP research include the following: Seong-Woo Kim et al [16][17][18], who created a framework to extend perception beyond line-of-sight, a cooperative driving system using CP, and methods for improving AD safety and smoothness; Pierre Merdiganc et al [19], who integrated perception and vehicle-to-pedestrian communication to enhance Vulnerable Road Users' (VRUs) safety; Aaron Miller et al [20], who developed a perception and localization system allowing vehicles with basic sensors to leverage data from those with advanced sensors, thus elevating AD capabilities; Xiaboo Chen et al [21,22], who proposed a recursive Bayesian framework for more reliable cooperative tracking, and a robust framework for multi-vehicle tracking under inaccurate self-localization; Adamey et al [23], who introduced a method for collaborative vehicle tracking in mixed-traffic settings; Francesco Biral et al [24], who demonstrated how the SAFE STRIP EU project technology aids in deploying the LDM for Cooperative ITS safety applications; and Stefano Masi et al [25], who developed a cooperative roadside vision system to enhance the perception capabilities of an AV; Sumbal Malik et al [26], who highlight the need for advanced CP to overcome challenges in achieving level 5 AD; Tania Cerquitelli et al [27], who discussed in a special issue the integration of machine learning and artificial intelligence technologies to empower network communication, analysing how computer networks can become smarter; Andrea Piazzoni et al [28], who discuss how to model CP errors in AD, focusing on the impact of occlusion on safety and how CP may address it; Zhiying Song et al [29], who presented a framework for evaluating CP in connected AVs, emphasizing the importance of CP in increasing vehicle awareness beyond sensor FoV; Mao Shan et al [30], who introduced a novel framework for enhancing CP in Connected AVs by probabilistically fusing V2X data, improving perception range and decision-making in complex environments.…”

Pose Determination of the Disc Cutter Holder of Shield Machine Based on Monocular Vision

“…The visual measurement method includes measurement based on cooperative target [2,3] and that based on non-cooperative target [4], the former is to locate the object by artificial markers fixed on it, and the latter is generally to locate the object by its features. The artificial marks processed on the disc cutter or its holder are easy to be worn; thus, the noncooperative target method is more suitable for the actual situation.…”

Analysis of Cooperative Perception in Ant Traffic and Its Effects on Transportation System by Using a Congestion-Free Ant-Trail Model

“…With the rise in communication and sensing technology, cooperative perception for intelligent transportation systems (ITS) is attracting attentions of various researchers in the field [ 1 , 2 , 3 , 4 , 5 , 6 ]. Cooperative perception allows vehicles to collect and share information with other vehicles and infrastructure, enabling vehicles and infrastructure to detect beyond their local capabilities [ 1 , 2 ].…”

“…Research has shown that cooperative perception can increase autonomous driving systems’ robustness by increasing the perception’s accuracy and detecting objects beyond their local capabilities [ 7 , 8 , 9 ]. Moreover, the research has also shown that cooperative perception can allow individual vehicles in ITS to collaborate on a transportation system level to increase the ITS system’s efficiency and safety [ 3 , 4 , 5 , 7 , 8 , 9 , 10 ].…”