Collaborative Vehicle Tracking in Mixed-Traffic Environments: Scaled-Down Tests Using SimVille

Adamey, Emrah; Ozbilgin, Guchan; Özgüner, Ü.

doi:10.4271/2015-01-0282

Cited by 5 publications

(2 citation statements)

References 18 publications

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“…Kim et al [17] offer further insight on the same study and Liu et al present a related work using the same technique in [18]. Adamey et al have proposed a solution in [19,20] to CP by considering different classes of vehicles such as fully equipped (having both onboard sensing and communication equipment), V2V equipped (having communication equipment alone) and nonequipped vehicles and implemented their solution in a multi-robot testbed, following simulation. Their approach consists of steps involving pose realignment using scan matching, followed by constructing a map based on covariance intersection and finally Kalman filtering to fuse these perception maps over time.…”

Section: Related Workmentioning

confidence: 99%

Cooperative perception in autonomous ground vehicles using a mobile‐robot testbed

Sridhar

Eskandarian

2019

IET Intelligent Transport Systems

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With connected and autonomous vehicles, no optimal standard or framework currently exists, outlining the right level of information sharing for cooperative autonomous driving. Cooperative Perception is proposed among vehicles, where every vehicle is transformed into a moving sensor platform that is capable of sharing information collected using its on-board sensors. This helps extend the line of sight and field of view of autonomous vehicles, which otherwise suffer from blind spots and occlusions. This increase in situational awareness promotes safe driving over a short range and improves traffic flow efficiency over a long range. I'm deeply indebted to my graduate advisor and mentor, Dr.Azim Eskandarian for having provided me, the opportunity to work with him and learn from his years of experience on Intelligent Vehicles Research. I'm grateful for all the insight, both technical and on the general nature of research, and for his constant motivation, patience and optimism, which kept me going. He steered me in the right direction when necessary, but also provided the freedom that empowered me to develop independent ideas. I thank Dr.John Ferris and Dr.Saied Taheri for serving as my graduate thesis committee members, for their expert feedback and for reviewing this work to completeness. I am also indebted to Dr.John Ferris for his guidance and advice during my time at Virginia Tech. I'm thankful to all my labmates at the ASIM Lab, especially Dr.Yuan Lin for his contribution in developing the initial indoor test-track and positioning system and Dr.Lirong Wang for her feedback and advice. I'm grateful to my friends and colleagues for all the motivation, technical insight and support in much needed moments. I thank Savio Pereira for the detailed discussions that helped refine the idea. I thank my professors at Virginia Tech for equipping me with the necessary background for carrying out this research. I'm also thankful to the ME Department Staff, who always had the time and a solution, for all my queries, that helped complete this project on time. Lastly, I'm grateful to my parents for having believed in me. None of this would have seen the light of day, if not for them. They were a source of endless emotional support and their love, assurance and motivation kept me working through thick and thin and succeed in Graduate school.

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Section: Related Workmentioning

confidence: 99%

Cooperative perception in autonomous ground vehicles using a mobile‐robot testbed

Sridhar

Eskandarian

2019

IET Intelligent Transport Systems

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“…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.…”

Section: Introductionmentioning

confidence: 99%

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

Taddei,

Visintainer,

Stoffella

et al. 2024

Sustainability

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Automated Driving (AD) has been receiving considerable attention from industry, the public, and researchers for its ability to reduce accidents, emissions, and congestion. The purpose of this study is to extend the standardized Local Dynamic Map (LDM) by adding two new layers, and develop efficient and accurate algorithms designed to enhance AD by exploiting the LDM coupled with Cooperative Perception (CP). The LDM is implemented as a Neo4j graph database and extends the standard four-layer structure by adding a detection layer and a prediction layer. A custom Application Programming Interface (API) manages all incoming data, generates the LDM, and runs the algorithms. Currently, the API can match detected entities coming from different sources, correctly position them on the map even in the presence of high uncertainties in the data, and predict their future actions. We tested the developed LDM with real-world data, which we collected using a prototype vehicle from Centro Ricerche FIAT (CRF) Trento Branch—the supporting research center for this work—in urban, suburban, and highway areas of Trento, Italy. The results show that the developed solution is capable of accurately matching and predicting detected entities, is robust to high uncertainties in the data, and is efficient, achieving real-time performance in all scenarios. From these results we can conclude that the LDM and CP have the potential to be core parts of AD, bringing improvements to the development process.

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