Addressing data association by message passing over graph neural networks

Tedeschini, Bernardo Camajori; Brambilla, Mattia; Barbieri, Luca; Nicoli, Monica

doi:10.23919/fusion49751.2022.9841233

Cited by 12 publications

(9 citation statements)

References 40 publications

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“…Furthermore, the developed method does not rely on maps of the driving environment, as in [39], making the cooperative approach potentially applicable to any driving situation, especially when high-definition maps are unavailable. The proposed solution improves upon all our previous works, which provided an initial proof of concept using only single-stage detectors [57], [58], or focused on studying the DA problem alone [37], [38]. Herein, we provide a more complete and robust solution that jointly addresses the DA problem and improves the localization accuracy thanks to the cooperation among vehicles.…”

Section: B Contributionsmentioning

confidence: 58%

“…The integration of LiDAR sensors within cooperative positioning schemes is therefore expected to bring significant advantages in terms of robustness and accuracy. Despite the LiDAR potentials, few cooperative approaches based on this technology have been studied [37]- [45]. The works in [37], [38] investigate the association issue across multiple vehicles, whereas [39] develops a decentralized cooperative localization method to fuse LiDAR, GNSS and high-definition maps.…”

Section: A Related Workmentioning

confidence: 99%

“…Despite the LiDAR potentials, few cooperative approaches based on this technology have been studied [37]- [45]. The works in [37], [38] investigate the association issue across multiple vehicles, whereas [39] develops a decentralized cooperative localization method to fuse LiDAR, GNSS and high-definition maps. In [40], a cooperative Simultaneous Localization and Mapping (SLAM) approach is developed for vehicle pose estimation, while [41] proposes a cooperative mapping technique to fuse multiple LiDAR views into an improved unified map.…”

Section: A Related Workmentioning

confidence: 99%

See 2 more Smart Citations

Deep Learning-Based Cooperative LiDAR Sensing for Improved Vehicle Positioning

Barbieri,

Tedeschini,

Brambilla

et al. 2024

IEEE Trans. Signal Process.

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Accurate positioning is known to be a fundamental requirement for the deployment of Connected Automated Vehicles (CAVs). To meet this need, a new emerging trend is represented by cooperative methods where vehicles fuse information coming from navigation and imaging sensors via Vehicleto-Everything (V2X) communications for joint positioning and environmental perception. In line with this trend, this paper proposes a novel data-driven cooperative sensing framework, termed Cooperative LiDAR Sensing with Message Passing Neural Network (CLS-MPNN), where spatially-distributed vehicles collaborate in perceiving the environment via LiDAR sensors. Vehicles process their LiDAR point clouds using a Deep Neural Network (DNN), namely a 3D object detector, to identify and localize possible static objects present in the driving environment. Data are then aggregated by a centralized infrastructure that performs Data Association (DA) using a Message Passing Neural Network (MPNN) and runs the Implicit Cooperative Positioning (ICP) algorithm. The proposed approach is evaluated using two realistic driving scenarios generated by a high-fidelity automated driving simulator. The results show that CLS-MPNN outperforms a conventional non-cooperative localization algorithm based on Global Navigation Satellite System (GNSS) and a state-of-theart cooperative Simultaneous Localization and Mapping (SLAM) method while approaching the performances of an oracle system with ideal sensing and perfect association.

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Section: B Contributionsmentioning

confidence: 58%

Section: A Related Workmentioning

confidence: 99%

Section: A Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Deep Learning-Based Cooperative LiDAR Sensing for Improved Vehicle Positioning

Barbieri,

Tedeschini,

Brambilla

et al. 2024

IEEE Trans. Signal Process.

Self Cite

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“…In the context of cooperative intelligent transport systems (C-ITS), connected automated vehicles (CAV) rely on ML, and more specifically deep learning (DL), for various functions, including identifying and segmenting objects within images, controlling the vehicle and avoiding collisions, and determining the most efficient route [23], [24]. Because of the high complexity of such tasks (including precise positioning), urban areas may install computing units, namely roadside units (RSUs), on busy roads that CAV will be able to use to offload part of the computing activities [25], [26]. Cooperation between nearby RSUs, here referred to as BSs, is of paramount importance for enabling network-based precise localization [27], [28].…”

Section: Introductionmentioning

confidence: 99%

Cooperative Deep-Learning Positioning in mmWave 5G-Advanced Networks

Tedeschini,

Nicoli

2023

IEEE J. Select. Areas Commun.

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In application verticals that rely on mission-critical control, such as cooperative intelligent transport systems (C-ITS), 5G-Advanced networks must be able to provide dynamic positioning with accuracy down to the centimeter level. To achieve this level of precision, technology enablers, such as massive multiple-input multiple-output (mMIMO), millimeter waves (mmWave), machine learning and cooperation are of paramount importance. In this paper, we propose a cooperative deep learning (DL)-based positioning methodology that combines these key technologies into a new promising solution for precise 5G positioning. Sparse channel impulse response (CIR) data are used by the positioning infrastructure to extract positiondependent features. We model the problem as a joint task composed of non-line-of-sight (NLOS) identification and position estimation which permits to suitably handle geometrical location measurements and channel fingerprints. The network of base stations (BSs) automatically steers between egocentric (in case of NLOS) and cooperative (for LOS) positioning mode. We perform extensive standard-compliant simulations in a 5G urban micro (UMi) vehicular scenario obtained by ray-tracing and simulation of urban mobility (SUMO) software. Results show that the proposed cooperative DL architecture is able to outperform conventional geometrical positioning algorithms operating in LOS by 47%, achieving a median error of 71 cm on unseen trajectories.

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“…The major fields of application can be found in target tracking [12], [13], [14], internet-of-things (IoT) [15], [16], [17], crowd sensing [18], [19], smart environments [20] and industrial automation [21]. Strict requirements are foreseen for the most critical services such as automated driving [22], [23]. These include a lateral and longitudinal positioning error of 10 and 50 cm [24], respectively, and a latency down to 5 ms for fully autonomous driving vehicles [25].…”

mentioning

confidence: 99%

On the Latent Space of mmWave MIMO Channels for NLOS Identification in 5G-Advanced Systems

Tedeschini

Nicoli

Win

2023

IEEE J. Select. Areas Commun.

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In mission-critical verticals such as automated driving, 5G-advanced networks must provide centimeter-level dynamic positioning along with ultra-reliable low-latency communication services. Massive Multiple-Input Multiple-Output (mMIMO) and millimeter waves (mmWave) are the key enablers, allowing high accuracy angle and delay estimation. Still, extracting such information from highly-dimensional Channel Impulse Responses (CIRs) results in a complex task, due to channel sparsity and intermittent blockage. In this paper we focus on non-lineof-sight (NLOS) identification from CIR data, proposing a Deep Autoencoding Kernel Density Model (DAKDM) to characterize the statistics of the channel latent features. We formulate the problem as a semi-supervised anomaly detection task in which only LOS samples, i.e., normal data, are adopted for training. DAKDM is a single-stage training model that takes as input the full CIR thanks to an AutoEncoder (AE) structure. The proposed method is able to learn the latent distribution by means of a Kernel Density Estimator (KDE) in combination with a deep learning likelihood network. We validate the proposed solution in a 5G Urban micro (UMi) vehicular scenario. Results show that the proposed model can significantly outperform conventional algorithms and obtain similar performances to variational Bayes algorithms at one tenth of the inference time.

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Addressing data association by message passing over graph neural networks

Cited by 12 publications

References 40 publications

Deep Learning-Based Cooperative LiDAR Sensing for Improved Vehicle Positioning

Deep Learning-Based Cooperative LiDAR Sensing for Improved Vehicle Positioning

Cooperative Deep-Learning Positioning in mmWave 5G-Advanced Networks

On the Latent Space of mmWave MIMO Channels for NLOS Identification in 5G-Advanced Systems

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