Cooperative Driving in Mixed Traffic with Heterogeneous Communications and Cloud Infrastructure

Auerswald, Rico; Busse, Roman; Dod, Markus; Fritzsche, Richard; Jungmann, Alexander; Klöppel-Gersdorf, Michael; Krems, Josef F.; Lorenz, Sven; Schmalfuß, Franziska; Springer, Sabine; Strobl, Severin

doi:10.5220/0007682900950105

Cited by 10 publications

(3 citation statements)

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“…Furthermore, in [34], [35], a full stack from the access layer was applied to the facility layer. Besides MCM, they used a maneuver recommended message for driving coordination.…”

Section: Related Workmentioning

confidence: 99%

Roadside-assisted Cooperative Planning using Future Path Sharing for Autonomous Driving

Hirata¹,

Tsukada²,

Okumura³

et al. 2021

Preprint

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Cooperative intelligent transportation systems (ITS) are used by autonomous vehicles to communicate with surrounding autonomous vehicles and roadside units (RSU). Current C-ITS applications focus primarily on real-time information sharing, such as cooperative perception. In addition to realtime information sharing, self-driving cars need to coordinate their action plans to achieve higher safety and efficiency. For this reason, this study defines a vehicle's future action plan/path and designs a cooperative path-planning model at intersections using future path sharing based on the future path information of multiple vehicles. The notion is that when the RSU detects a potential conflict of vehicle paths or an acceleration opportunity according to the shared future paths, it will generate a coordinated path update that adjusts the speeds of the vehicles. We implemented the proposed method using the open-source Autoware autonomous driving software and evaluated it with the LGSVL autonomous vehicle simulator. We conducted simulation experiments with two vehicles at a blind intersection scenario, finding that each car can travel safely and more efficiently by planning a path that reflects the action plans of all vehicles involved. The time consumed by introducing the RSU is 23.0 % and 28.1 % shorter than that of the stand-alone autonomous driving case at the intersection.

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“…Furthermore, in [34], [35], a full stack from the access layer was applied to the facility layer. Besides MCM, they used a maneuver recommended message for driving coordination.…”

Section: Related Workmentioning

confidence: 99%

Roadside-assisted Cooperative Planning using Future Path Sharing for Autonomous Driving

Hirata¹,

Tsukada²,

Okumura³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…A full-stack from the access layer to the facility layer is implemented in [18], [19]. Alongside MCM, a maneuver recommended message was used for driving coordination.…”

Section: Related Workmentioning

confidence: 99%

AutoMCM: Maneuver Coordination Service with Abstracted Functions for Autonomous Driving

Mizutani

Tsukada

Esaki

2021

Preprint

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A cooperative intelligent transport system (C-ITS) uses vehicle-to-everything (V2X) technology to make selfdriving vehicles safer and more efficient. Current C-ITS applications have mainly focused on real-time information sharing, such as for cooperative perception. In addition to better realtime perception, self-driving vehicles need to achieve higher safety and efficiency by coordinating action plans. This study designs a maneuver coordination (MC) protocol that uses seven messages to cover various scenarios and an abstracted MC support service. We implement our proposal as AutoMCM by extending two open-source software tools: Autoware for autonomous driving and OpenC2X for C-ITS. The results show that our system effectively reduces the communication bandwidth by limiting message exchange in an event-driven manner. Furthermore, it shows that the vehicles run 15% faster when the vehicle speed is 30 km/h and 28% faster when the vehicle speed is 50 km/h using our scheme. Our system shows robustness against packet loss in experiments when the message timeout parameters are appropriately set.

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“…The problem of addressing specific vehicles can be overcome by using the Manoeuvre Recommendation Message (MRM, experimental), which is similar to the MCM with the difference that it is sent by the RSU. More background on the MRM can be found in Auerswald et al (2019).…”

Section: Wlan80211p C-v2x or Lte)mentioning

confidence: 99%

Evaluating Suitable Glosa-Algorithms by Simulation Considering Realistic Traffic Conditions and V2X-Communication

Klöppel-Gersdorf

Grimm²

2020

Transport and Telecommunication Journal

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Vehicle-to-Everything (V2X) communication allows infrastructure elements, e.g., traffic lights, to directly communicate with vehicles, thereby allowing services like Green Light Optimized Speed Advisory (GLOSA), Probe Vehicle Data (PVD), and Traffic Signal Priority Request (TSP). The idea behind GLOSA is to assist vehicles approaching an intersection with speed advices in order to fulfill a given objective, e.g., minimizing fuel usage, emissions and/or delay. In a prior work (Kloeppel et al., 2019), several GLOSA algorithms were examined and their fitness (in the form of CO2 emissions, fuel usage and delay) were evaluated under largely realistic conditions. This paper is an extension of the prior work and presents further examinations, which include a more detailed study on the behaviour of Diesel-powered vehicles when using the GLOSA algorithm of Stebbins et al. (2017) as well as a study considering fixed-time control coupled with intelligent vehicles.

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Cooperative Driving in Mixed Traffic with Heterogeneous Communications and Cloud Infrastructure

Cited by 10 publications

References 0 publications

Roadside-assisted Cooperative Planning using Future Path Sharing for Autonomous Driving

Roadside-assisted Cooperative Planning using Future Path Sharing for Autonomous Driving

AutoMCM: Maneuver Coordination Service with Abstracted Functions for Autonomous Driving

Evaluating Suitable Glosa-Algorithms by Simulation Considering Realistic Traffic Conditions and V2X-Communication

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