Multimedia Fusion at Semantic Level in Vehicle Cooperactive Perception

Xiao, Zhongyang; Mo, Zhaobin; Jiang, Kun; Yang, Diange

doi:10.1109/icmew.2018.8551565

Cited by 25 publications

(13 citation statements)

References 6 publications

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“…Eckelmann et al [98] uses high-resolution three-dimensional point cloud data provided by Velodyne VLP-16 lidar, and then locates traffic objects through V2X communication for target recognition and tracking. Xiao et al [99] proposed a vehicle perception fusion framework based on V2V, vision and GPS. By fusing multimedia information of vision, GPS and digital map, the method of in-depth learning is used to deeply understand and extract the key information in the vision system, which significantly improves the vehicle perception ability and perception range, and eliminates most of the perception blind areas.…”

Section: Research On Traffic Conflict Based On Intelligent Vehiclmentioning

confidence: 99%

A Review of Research on Traffic Conflicts Based on Intelligent Vehicles

Huang

et al. 2020

IEEE Access

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The traffic situation at an urban intersection is complicated, due to the numerous internal conflicts and all kinds of traffic accidents. With the objective of improving road safety, the automotive industry is moving toward intelligent vehicles. The major challenges are accurately perceiving the road traffic environment, detecting the potential traffic conflicts, and proposing the alternative driving strategies. This paper summarizes the existing researches on traffic conflicts from the perspective of intelligent vehicles. The intelligent vehicles can perceive the surrounding environment, extract road condition information, and detect obstacles for avoiding collisions or mitigating accidents. It expounds that the perception technology of intelligent vehicles can be divided into three main categories, namely, the perception technology, the communication technology, and the fusion of perception-communication technology. At the same time, the existing technical problems are analyzed. Finally, future development trends in this field are discussed. INDEX TERMS Intelligent vehicles, traffic conflicts, V2X communications, environment perception. I. INTRODUCTION

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Section: Research On Traffic Conflict Based On Intelligent Vehiclmentioning

confidence: 99%

A Review of Research on Traffic Conflicts Based on Intelligent Vehicles

Huang

et al. 2020

IEEE Access

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“…In [35], the results of the awareness of other vehicles are integrated into the ego-vehicle’s perception system as virtual sensors to achieve perception enhancement. In [36], a multi-vehicle perception framework combining image and semantic features is proposed, and experiments have proved that the problem of front-vehicle occlusion can be solved. In these studies, the problems of self-positioning and localization of other targets were considered separately, which rendered the effect of the fusion very sensitive to their relative positioning.…”

Section: Introductionmentioning

confidence: 99%

A Unified Multiple-Target Positioning Framework for Intelligent Connected Vehicles

Xiao

Yang

Wen

et al. 2019

Sensors

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Future intelligent transport systems depend on the accurate positioning of multipletargets in the road scene, including vehicles and all other moving or static elements. The existingself-positioning capability of individual vehicles remains insufficient. Also, bottlenecks indeveloping on-board perception systems stymie further improvements in the precision and integrityof positioning targets. Vehicle-to-everything (V2X) communication, which is fast becoming astandard component of intelligent and connected vehicles, renders new sources of informationsuch as dynamically updated high-definition (HD) maps accessible. In this paper, we propose aunified theoretical framework for multiple-target positioning by fusing multi-source heterogeneousinformation from the on-board sensors and V2X technology of vehicles. Numerical and theoreticalstudies are conducted to evaluate the performance of the framework proposed. With a low-costglobal navigation satellite system (GNSS) coupled with an initial navigation system (INS), on-boardsensors, and a normally equipped HD map, the precision of multiple-target positioning attainedcan meet the requirements of high-level automated vehicles. Meanwhile, the integrity of targetsensing is significantly improved by the sharing of sensor information and exploitation of mapdata. Furthermore, our framework is more adaptable to traffic scenarios when compared withstate-of-the-art techniques.

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“…Current dynamic map fusion techniques can be classified as data level [4], [9], feature level [5]- [8], and object level [9]- [15]. Those data-level and feature-level methods likely incur high communication overheads and most object-level methods do not take uncertainties into account.…”

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confidence: 99%

“…Those data-level and feature-level methods likely incur high communication overheads and most object-level methods do not take uncertainties into account. On the other hand, various deep and federated learning schemes have been used to improve the quality of feature models [8], [14], [15], but few of them use point-cloud data. Besides, as most cloud-based model training schemes assume that datasets have been properly labeled [4]- [9], [11], [14], [15], knowledge distillation (KD) methods [16]- [18] need further investigation for label generation among networked vehicles.…”

mentioning

confidence: 99%

“…The labels of real-time sensory data are provided by road-side units or generated from the three-stage fusion algorithm at the edge server. feature level [5]- [8], and object level [9]- [15]. Data level methods collect raw data (such as point clouds) from different vehicles and achieve global dynamic map fusion by using deep networks [4], [9] at the cost of high communication overhead and extra data synchronization.…”

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confidence: 99%

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Distributed Dynamic Map Fusion via Federated Learning for Intelligent Networked Vehicles

Zhang,

Wang,

Hong

et al. 2021

Preprint

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The technology of dynamic map fusion among networked vehicles has been developed to enlarge sensing ranges and improve sensing accuracies for individual vehicles. This paper proposes a federated learning (FL) based dynamic map fusion framework to achieve high map quality despite unknown numbers of objects in fields of view (FoVs), various sensing and model uncertainties, and missing data labels for online learning. The novelty of this work is threefold: (1) developing a three-stage fusion scheme to predict the number of objects effectively and to fuse multiple local maps with fidelity scores; (2) developing an FL algorithm which fine-tunes feature models (i.e., representation learning networks for feature extraction) distributively by aggregating model parameters; (3) developing a knowledge distillation method to generate FL training labels when data labels are unavailable. The proposed framework is implemented in the Car Learning to Act (CARLA) simulation platform. Extensive experimental results are provided to verify the superior performance and robustness of the developed map fusion and FL schemes. I. INTRODUCTIONThe intelligent networked vehicle system (INVS) is an emerging vehicle-edge-cloud system that accomplishes cooperative perception, map management, planning and maneuvering tasks via vehicle-to-everything (V2X) communication [1]-[3]. Among all the tasks, distributed map management aims to enlarge sensing ranges and improve sensing accuracies for individual vehicles and plays a central role in . While static maps describe stationary objects (e.g., roads, buildings, and trees), dynamic maps emphasize updating information of mobile objects (e.g., pedestrians, cars, and animals) in real time. Fig. 1 shows the architecture of an intelligent networked vehicle system. There are three main steps for dynamic map fusion: (1) local sensing and perception, (2) local map fusion and uploading, and (3) global map fusion and broadcasting.

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Multimedia Fusion at Semantic Level in Vehicle Cooperactive Perception

Cited by 25 publications

References 6 publications

A Review of Research on Traffic Conflicts Based on Intelligent Vehicles

A Review of Research on Traffic Conflicts Based on Intelligent Vehicles

A Unified Multiple-Target Positioning Framework for Intelligent Connected Vehicles

Distributed Dynamic Map Fusion via Federated Learning for Intelligent Networked Vehicles

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