Cloud Intelligence and Collective Learning  for Automated and Connected Driving

Lampe, Bastian; Reiher, Lennart; Woopen, Timo; Eckstein, Lutz

doi:10.1007/s38314-022-1405-9

Cited by 3 publications

(3 citation statements)

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“…These use cases consider close interaction between the automated driving and teleoperation modes, where the remote operator can take over the vehicle control at any point. Additionally, vehicles used the cloud infrastructure [6] to report the disengagement events and request assistance. Typical scenarios include navigating complicated traffic interactions (e.g., a policeman at an intersection waiving the right of way) or navigating construction sites, roadworks, and other complex situations not covered by the nominal ODD.…”

Section: B Project Unicaragil and Research Questionsmentioning

confidence: 99%

Survey on Teleoperation Concepts for Automated Vehicles

Majstorovic

Hoffmann

Pfab

et al. 2022

2022 IEEE International Conference on Systems, Man, and Cybernetics (SMC)

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Driving directionFig. 1: A teleoperated autoTAXI vehicle maneuvering around an improvised construction site C on a test track in Aldenhoven, Germany. The vehicle tracks the trajectory defined by the remote operator (path visualized in blue) from the start position A to the goal position B , effectively solving the AV disengagement scenario. Yellow circles ( ) depict approximate path waypoints specified by a remote operator using the presented teleoperation system.

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Section: B Project Unicaragil and Research Questionsmentioning

confidence: 99%

Survey on Teleoperation Concepts for Automated Vehicles

Majstorovic

Hoffmann

Pfab

et al. 2022

2022 IEEE International Conference on Systems, Man, and Cybernetics (SMC)

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“…Woopen et al. [21, 22] proposed a disruptive modular architecture for UGV and discussed the application of cloud services to improve efficiency.…”

Section: Introductionmentioning

confidence: 99%

“…Sell et al [20] designed a software system for UGV with an additional fleet management system to provide safe remote control for the single UGV or fleet in emergency. Woopen et al [21,22] proposed a disruptive modular architecture for UGV and discussed the application of cloud services to improve efficiency.…”

Section: Introductionmentioning

confidence: 99%

Overall architecture and system design of shuttle unmanned ground vehicle with road verification in intelligent transportation system zone

2023

IET Intelligent Trans Sys

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The shuttle unmanned ground vehicle (UGV) is an important element in the future intelligent transportation system (ITS), which will be widely used for public transportation in various scenarios. It has been widely accepted that the shuttle UGV will significantly improve the transportation efficiency and intelligent level of ITS in the near future. This paper presents an overall architecture and system of shuttle UGV for the future ITS. The hardware architecture includes sensor, actuator and computing platform, and is built on an X-by-wire chassis. The software architecture includes environment perception, decision making and dynamics control. Reliable and accurate autonomous driving algorithms are applied to the software system, which enable the shuttle UGV to drive intelligently and perform task stably. In addition, to address the large-scale deployment of shuttle UGVs in the future, this paper also introduces the concept of cloud brain system and discusses the application and prospect of the cloud brain system in ITS. The cloud brain system can store, monitor and mine the big data generated by the shuttle UGVs, which will further improve the operational safety and overall performance of the shuttle UGV group. An open road test in an ITS test zone is conducted to validate the performance of the proposed overall architecture and system for the shuttle UGV.

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