AutoC2X: Open-source software to realize V2X cooperative perception among autonomous vehicles

Tsukada, Manabu; Oi, Takaharu; Ito, Akinori; Hirata, Mai; Esaki, Hiroshi

doi:10.1109/vtc2020-fall49728.2020.9348525

Cited by 36 publications

(18 citation statements)

References 14 publications

(13 reference statements)

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“…The shared information must preserve the respective frames of reference and coordinate points for the environmental objects that are perceived [24]. An open-source V2X cooperative perception system integrating the individual perception of an AV obtained from its software: Autoware with an open-source experimental and prototyping software for cooperative Intelligent Transport Systems (ITS): OpenC2X is presented in [25]. Open-source implementations are more reliable since they are in public domain and are accessible to all.…”

Section: Inter-vehicular Network Based Perceptionmentioning

confidence: 99%

A Review of IoT Technology for the Connected Autonomous Vehicles Ecosystem

Sharma

Habibullah

2022

Trends Sci

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Connected Autonomous Vehicles Ecosystem (CAVE) is fast becoming a reality. This paper reviews various architectures that leverage IoT technology to progressively create a system of interconnected Autonomous Vehicles (AV) that is effective, robust and secure. Accurate and fast perception of the external environment is key to movement of AVs in CAVE. IoT technology through sensors and internetworking can help an AV to do reliable perception. State of the art methods for perception of AVs in CAVE are reviewed. Self-awareness is another important aspect. IoT and Artificial Intelligence (AI) can come together to assist an AV in self-diagnosing anything abnormal. One such use case is presented for onboard diagnostics in an AV. Accurate AI is key to making effective strategies and to take control actions with error feedbacks that carefully maneuver an AV from its source to destination. The transition from legacy vehicles to CAVE requires AI methods that combat threats and have effective overall movement in mixed mode traffic systems (MMTS). Strategies for effective movement in MMTS are presented. HIGHLIGHTS Role of IoT technology is identified in different areas of Connected Autonomous Vehicle Ecosystem (CAVE) A comparison of different technologies in various domains of CAVE is presented and their pros and cons are discussed Use case for onboard diagnostics of an autonomous vehicle in CAVE is presented to emphasize the role of IoT Technology in [automated] preventive maintenance Key studies are reviewed that emphasize important issues like robust architectures for CAVE, threats in CAVE and reliable functioning in mixed mode traffic systems GRAPHICAL ABSTRACT

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Section: Inter-vehicular Network Based Perceptionmentioning

confidence: 99%

A Review of IoT Technology for the Connected Autonomous Vehicles Ecosystem

Sharma

Habibullah

2022

Trends Sci

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“…Gabb et al [18] presented a scheme for data sent from a mobile edge computing (MEC) server to a car and proposed a system that fuses data from the car's sensors with an environmental model based on infrastructure sensor data at the MEC server. Tsukada et al [14], [19] proposed AutoC2X, a system that shares information collected by roadside equipment with an automatic vehicle using Autoware [3] and OpenC2X, opensource experimental and prototype platforms for cooperative ITS.…”

Section: Related Workmentioning

confidence: 99%

“…In many field demonstrations, infrastructure-based CPM has been demonstrated to improve the perception of automated vehicles [13], [14]. However, research on the infrastructure which supports the vehicle's better prediction and planning is still in its nascent stage.…”

Section: Introductionmentioning

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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“…In [22], MCM is used to reduce uncertainty in automated vehicles and proposed the MCM as a more general protocol. In our previous works [23], [24], we have implemented AutoC2X, which enable cooperative perception on Autoware and OpenC2X.…”

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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AutoC2X: Open-source software to realize V2X cooperative perception among autonomous vehicles

Cited by 36 publications

References 14 publications

A Review of IoT Technology for the Connected Autonomous Vehicles Ecosystem

A Review of IoT Technology for the Connected Autonomous Vehicles Ecosystem

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

AutoMCM: Maneuver Coordination Service with Abstracted Functions for Autonomous Driving

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