Robust Cooperative Multi-Vehicle Tracking with Inaccurate Self-Localization Based on On-Board Sensors and Inter-Vehicle Communication

Abstract

The fusion of on-board sensors and transmitted information via inter-vehicle communication has been proved to be an effective way to increase the perception accuracy and extend the perception range of connected intelligent vehicles. The current approaches rely heavily on the accurate self-localization of both host and cooperative vehicles. However, such information is not always available or accurate enough for effective cooperative sensing. In this paper, we propose a robust cooperative multi-vehicle tracking framework suitable for the situation where the self-localization information is inaccurate. Our framework consists of three stages. First, each vehicle perceives its surrounding environment based on the on-board sensors and exchanges the local tracks through inter-vehicle communication. Then, an algorithm based on Bayes inference is developed to match the tracks from host and cooperative vehicles and simultaneously optimize the relative pose. Finally, the tracks associated with the same target are fused by fast covariance intersection based on information theory. The simulation results based on both synthesized data and a high-quality physics-based platform show that our approach successfully implements cooperative tracking without the assistance of accurate self-localization.

Survey on Cooperative Perception in an Automotive Context

“…In Providentia project [44], [49], authors based their tracking methods using Gaussian Mixture Probability Hypothesis Density (GMPHD). Similarly, Chen et al in [83] used a GMPHD based method to extract the tracks of multiple vehicles. The authors perform a SLAT using a Bayes inferencebased algorithm optimizing relative pose estimation and fusing the matched tracks using fast covariance intersection based on information theory (IT-FCI).…”

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.…”

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

“…In [ 27 ], a cooperative pedestrian tracking algorithm was presented in the GPS-denied environments. In [ 28 ], a robust cooperative tracking algorithm was proposed for the situation where the localization information is not available.…”