6G-EWOC: Crowdsourced SLAM Data Fusion for Safe and Efficient ADAS Driving

Jose Antonio Làzaro; Josep Ramon Casas; Javier Ruiz Hidalgo; Marti Cortada Garcia; Gerard Martin Pey; Judit Salavedra Pujol; Mingrui Wang; Eleni Theodoropoulou; George Lyberopoulos; Carina Marcus; Olof Eriksson; Pablo Garcia; Santiago Royo; Jordi Riu; Bernhard Schrenk; Josep Maria Fabrega

6G-EWOC: Crowdsourced SLAM Data Fusion for Safe and Efficient ADAS Driving

Jose Antonio Làzaro (Vortragende:r), Josep Ramon Casas, Javier Ruiz Hidalgo, Marti Cortada Garcia, Gerard Martin Pey, Judit Salavedra Pujol, Mingrui Wang, Eleni Theodoropoulou, George Lyberopoulos, Carina Marcus, Olof Eriksson, Pablo Garcia, Santiago Royo, Jordi Riu, Bernhard Schrenk, Josep Maria Fabrega

Security & Communication Technologies

Publikation: Beitrag in Buch oder Tagungsband › Vortrag mit Beitrag in Tagungsband › Begutachtung

Abstract

The development of transport infrastructures for
Advanced Driver Assistance Systems (ADAS) and autonomous
vehicles operating efficiently and safely in congestion-free traffic
flows is a major challenge for telecommunications technologies.
Simultaneous Localization and Mapping (SLAM) plays a
crucial role in ensuring uninterrupted journeys for emergency
vehicles and increasing the safety of vulnerable road users in
complex traffic scenarios. Accurate SLAM mapping for ADAS
systems requires data from different sensor technologies –such
as high-resolution cameras or Radio/Light Detection and
Ranging (RaDAR/LiDAR)– to be effectively combined or fused.
Sensor fusion results in high data throughput and low latency
requirements. However, optimal mapping outcomes occur when
processing systems fuse data from sensors positioned at diverse
locations within the traffic scene. By crowdsourcing diverse
sensors, we can multiply the view angles, mitigate occlusions and
improve the overall scene coverage. Yet, this approach
introduces additional challenges for communication systems
within both the vehicles and the infrastructure. Addressing
these challenges is essential for seamless development of safe and
efficient ADAS driving techniques.

Originalsprache	Englisch
Titel	IEEE Future Networks World Forum
Untertitel	Symposium on Future Networks for Connected and Automated Mobility
Erscheinungsort	Dubai, UAE
Seitenumfang	6
Publikationsstatus	Veröffentlicht - 2024
Veranstaltung	IEEE Future Networks World Forum - Dubai, Dubai, Vereinigte Arabische Emirate Dauer: 15 Okt. 2024 → 17 Okt. 2024 https://fnwf2024.ieee.org/

Konferenz

Konferenz	IEEE Future Networks World Forum
Kurztitel	FNWF
Land/Gebiet	Vereinigte Arabische Emirate
Stadt	Dubai
Zeitraum	15/10/24 → 17/10/24
Internetadresse	https://fnwf2024.ieee.org/

Research Field

Enabling Digital Technologies

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Làzaro, J. A., Casas, J. R., Ruiz Hidalgo, J., Cortada Garcia, M., Martin Pey, G., Salavedra Pujol, J., Wang, M., Theodoropoulou, E., Lyberopoulos, G., Marcus, C., Eriksson, O., Garcia, P., Royo, S., Riu, J., Schrenk, B., & Fabrega, J. M. (2024). 6G-EWOC: Crowdsourced SLAM Data Fusion for Safe and Efficient ADAS Driving. in IEEE Future Networks World Forum: Symposium on Future Networks for Connected and Automated Mobility Artikel S01-4.

@inproceedings{eb711d2e61d1481f843c0b80e710d28e,

title = "6G-EWOC: Crowdsourced SLAM Data Fusion for Safe and Efficient ADAS Driving",

abstract = "The development of transport infrastructures for Advanced Driver Assistance Systems (ADAS) and autonomous vehicles operating efficiently and safely in congestion-free traffic flows is a major challenge for telecommunications technologies. Simultaneous Localization and Mapping (SLAM) plays a crucial role in ensuring uninterrupted journeys for emergency vehicles and increasing the safety of vulnerable road users in complex traffic scenarios. Accurate SLAM mapping for ADAS systems requires data from different sensor technologies –such as high-resolution cameras or Radio/Light Detection and Ranging (RaDAR/LiDAR)– to be effectively combined or fused. Sensor fusion results in high data throughput and low latency requirements. However, optimal mapping outcomes occur when processing systems fuse data from sensors positioned at diverse locations within the traffic scene. By crowdsourcing diverse sensors, we can multiply the view angles, mitigate occlusions and improve the overall scene coverage. Yet, this approach introduces additional challenges for communication systems within both the vehicles and the infrastructure. Addressing these challenges is essential for seamless development of safe and efficient ADAS driving techniques. ",

author = "L{\`a}zaro, \{Jose Antonio\} and Casas, \{Josep Ramon\} and \{Ruiz Hidalgo\}, Javier and \{Cortada Garcia\}, Marti and \{Martin Pey\}, Gerard and \{Salavedra Pujol\}, Judit and Mingrui Wang and Eleni Theodoropoulou and George Lyberopoulos and Carina Marcus and Olof Eriksson and Pablo Garcia and Santiago Royo and Jordi Riu and Bernhard Schrenk and Fabrega, \{Josep Maria\}",

year = "2024",

language = "English",

booktitle = "IEEE Future Networks World Forum",

note = "IEEE Future Networks World Forum, FNWF ; Conference date: 15-10-2024 Through 17-10-2024",

url = "https://fnwf2024.ieee.org/",

}

Làzaro, JA, Casas, JR, Ruiz Hidalgo, J, Cortada Garcia, M, Martin Pey, G, Salavedra Pujol, J, Wang, M, Theodoropoulou, E, Lyberopoulos, G, Marcus, C, Eriksson, O, Garcia, P, Royo, S, Riu, J, Schrenk, B & Fabrega, JM 2024, 6G-EWOC: Crowdsourced SLAM Data Fusion for Safe and Efficient ADAS Driving. in IEEE Future Networks World Forum: Symposium on Future Networks for Connected and Automated Mobility., S01-4, Dubai, UAE, IEEE Future Networks World Forum, Dubai, Vereinigte Arabische Emirate, 15/10/24.

6G-EWOC: Crowdsourced SLAM Data Fusion for Safe and Efficient ADAS Driving. / Làzaro, Jose Antonio (Vortragende:r); Casas, Josep Ramon; Ruiz Hidalgo, Javier et al.
IEEE Future Networks World Forum: Symposium on Future Networks for Connected and Automated Mobility. Dubai, UAE, 2024. S01-4.

Publikation: Beitrag in Buch oder Tagungsband › Vortrag mit Beitrag in Tagungsband › Begutachtung

TY - GEN

T1 - 6G-EWOC: Crowdsourced SLAM Data Fusion for Safe and Efficient ADAS Driving

AU - Casas, Josep Ramon

AU - Ruiz Hidalgo, Javier

AU - Cortada Garcia, Marti

AU - Martin Pey, Gerard

AU - Salavedra Pujol, Judit

AU - Wang, Mingrui

AU - Theodoropoulou, Eleni

AU - Lyberopoulos, George

AU - Marcus, Carina

AU - Eriksson, Olof

AU - Garcia, Pablo

AU - Royo, Santiago

AU - Riu, Jordi

AU - Schrenk, Bernhard

AU - Fabrega, Josep Maria

A2 - Làzaro, Jose Antonio

PY - 2024

Y1 - 2024

N2 - The development of transport infrastructures for Advanced Driver Assistance Systems (ADAS) and autonomous vehicles operating efficiently and safely in congestion-free traffic flows is a major challenge for telecommunications technologies. Simultaneous Localization and Mapping (SLAM) plays a crucial role in ensuring uninterrupted journeys for emergency vehicles and increasing the safety of vulnerable road users in complex traffic scenarios. Accurate SLAM mapping for ADAS systems requires data from different sensor technologies –such as high-resolution cameras or Radio/Light Detection and Ranging (RaDAR/LiDAR)– to be effectively combined or fused. Sensor fusion results in high data throughput and low latency requirements. However, optimal mapping outcomes occur when processing systems fuse data from sensors positioned at diverse locations within the traffic scene. By crowdsourcing diverse sensors, we can multiply the view angles, mitigate occlusions and improve the overall scene coverage. Yet, this approach introduces additional challenges for communication systems within both the vehicles and the infrastructure. Addressing these challenges is essential for seamless development of safe and efficient ADAS driving techniques.

AB - The development of transport infrastructures for Advanced Driver Assistance Systems (ADAS) and autonomous vehicles operating efficiently and safely in congestion-free traffic flows is a major challenge for telecommunications technologies. Simultaneous Localization and Mapping (SLAM) plays a crucial role in ensuring uninterrupted journeys for emergency vehicles and increasing the safety of vulnerable road users in complex traffic scenarios. Accurate SLAM mapping for ADAS systems requires data from different sensor technologies –such as high-resolution cameras or Radio/Light Detection and Ranging (RaDAR/LiDAR)– to be effectively combined or fused. Sensor fusion results in high data throughput and low latency requirements. However, optimal mapping outcomes occur when processing systems fuse data from sensors positioned at diverse locations within the traffic scene. By crowdsourcing diverse sensors, we can multiply the view angles, mitigate occlusions and improve the overall scene coverage. Yet, this approach introduces additional challenges for communication systems within both the vehicles and the infrastructure. Addressing these challenges is essential for seamless development of safe and efficient ADAS driving techniques.

M3 - Conference Proceedings with Oral Presentation

BT - IEEE Future Networks World Forum

CY - Dubai, UAE

T2 - IEEE Future Networks World Forum

Y2 - 15 October 2024 through 17 October 2024

ER -

6G-EWOC: Crowdsourced SLAM Data Fusion for Safe and Efficient ADAS Driving

Abstract

Konferenz

Research Field

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