Autonomous Maneuver Coordination Via Vehicular Communication

Xu, Wenbo; Willecke, Alexander; Wegner, Martin; Wolf, Lars; Kapitza, Rüdiger

doi:10.1109/dsn-w.2019.00022

Cited by 33 publications

(9 citation statements)

References 15 publications

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“…We use the LC2013 model (Erdmann, 2015) as the rule‐based model to serve as a base method for purposes of comparing the efficacy of the proposed method. This model is commonly used in several traffic simulation and autonomous vehicle (AV) operations research studies (Nilsson, 2019; Xu et al., 2019). In general, the LC2013 model computes the lane‐changing logic strategically mainly based on the remaining distance to a dead end, the speed gain from lane changing, and the willingness for performing cooperative lane changing.…”

Section: Experiments Settingsmentioning

confidence: 99%

Graph neural network and reinforcement learning for multi‐agent cooperative control of connected autonomous vehicles

Chen

Dong

et al. 2021

Computer aided Civil Eng

148

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A connected autonomous vehicle (CAV) network can be defined as a set of connected vehicles including CAVs that operate on a specific spatial scope that may be a road network, corridor, or segment. The spatial scope constitutes an environment where traffic information is shared and instructions are issued for controlling the CAVs movements. Within such a spatial scope, high‐level cooperation among CAVs fostered by joint planning and control of their movements can greatly enhance the safety and mobility performance of their operations. Unfortunately, the highly combinatory and volatile nature of CAV networks due to the dynamic number of agents (vehicles) and the fast‐growing joint action space associated with multi‐agent driving tasks pose difficultly in achieving cooperative control. The problem is NP‐hard and cannot be efficiently resolved using rule‐based control techniques. Also, there is a great deal of information in the literature regarding sensing technologies and control logic in CAV operations but relatively little information on the integration of information from collaborative sensing and connectivity sources. Therefore, we present a novel deep reinforcement learning‐based algorithm that combines graphic convolution neural network with deep Q‐network to form an innovative graphic convolution Q network that serves as the information fusion module and decision processor. In this study, the spatial scope we consider for the CAV network is a multi‐lane road corridor. We demonstrate the proposed control algorithm using the application context of freeway lane‐changing at the approaches to an exit ramp. For purposes of comparison, the proposed model is evaluated vis‐à‐vis traditional rule‐based and long short‐term memory‐based fusion models. The results suggest that the proposed model is capable of aggregating information received from sensing and connectivity sources and prescribing efficient operative lane‐change decisions for multiple CAVs, in a manner that enhances safety and mobility. That way, the operational intentions of individual CAVs can be fulfilled even in partially observed and highly dynamic mixed traffic streams. The paper presents experimental evidence to demonstrate that the proposed algorithm can significantly enhance CAV operations. The proposed algorithm can be deployed at roadside units or cloud platforms or other centralized control facilities.

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Section: Experiments Settingsmentioning

confidence: 99%

Graph neural network and reinforcement learning for multi‐agent cooperative control of connected autonomous vehicles

Chen

Dong

et al. 2021

Computer aided Civil Eng

148

View full text Add to dashboard Cite

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“…First-order risks may lead to safety risk in different ways. For example, poor accuracy may lead to the system failing to recognize a pedestrian and running them over [33], a melanoma identifier trained on insufficiently diverse data may result in unnecessary chemotherapy [169], or swarming ML systems that endanger human agents (e.g., high-speed maneuvers via inter-vehicular coordination making traffic conditions dangerous for traditional vehicles) [196]. The inability to assume/regain control in time may also result in increased safety risk, (e.g, overriding an autonomous weapon before it mistakenly shoots a civilian) [68].…”

Section: Safety Riskmentioning

confidence: 99%

The Risks of Machine Learning Systems

Tan¹,

Taeihagh²,

Baxter³

2022

Preprint

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The speed and scale at which machine learning (ML) systems are deployed are accelerating even as an increasing number of studies highlight their potential for negative impact. There is a clear need for companies and regulators to manage the risk from proposed ML systems before they harm people. To achieve this, private and public sector actors first need to identify the risks posed by a proposed ML system. A system's overall risk is influenced by its direct and indirect effects. However, existing frameworks for ML risk/impact assessment often address an abstract notion of risk or do not concretize this dependence. Keeping discussions of risk at an abstract level puts the onus of defining them on the assessor, which may result in incomplete and inconsistent assessments.We propose to address this gap with a context-sensitive framework for identifying ML system risks comprising two components: a taxonomy of the first-and second-order risks posed by ML systems, and their contributing factors. First-order risks stem from aspects of the ML system, while second-order risks stem from the consequences of first-order risks. These consequences are system failures that result from design and development choices. We explore how different risks may manifest in various types of ML systems, the factors that affect each risk, and how first-order risks may lead to second-order effects when the system interacts with the real world.Throughout the paper, we show how real events and prior research fit into our Machine Learning System Risk framework (MLSR).MLSR operates on ML systems rather than technologies or domains, recognizing that a system's design, implementation, and use case all contribute to its risk. In doing so, it unifies the risks that are commonly discussed in the ethical AI community (e.g., ethical/human rights risks) with system-level risks (e.g., application, design, control risks), paving the way for holistic risk assessments of ML systems.

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“…All of these proposals build upon the assumption that one received message is sufficient for the V2X application. However, current research studies argue that multiple message exchanges must eliminate divergence while performing a cooperative maneuver [50]. Therefore, Sepulcre et al [51] investigate IN-TEgRatioN of congestion and awareness control (INTERN) that combines the previously described approaches.…”

Section: A Content-agnostic Resource Allocationmentioning

confidence: 99%

Prioritizing Relevant Information: Decentralized V2X Resource Allocation for Cooperative Driving

Bischoff¹,

Schiegg

Schuller³

et al. 2021

IEEE Access

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Cooperative driving is a promising approach to increase traffic efficiency and safety. However, cooperative driving requires high communication performance from the application perspective when coordinating and executing a cooperative maneuver, especially in scenarios with high vehicle densities under congested channel conditions. Recent studies identified that content-agnostic congestion control mechanisms deployed in decentralized vehicular networks, e. g., from the European Telecommunications Standard Institute, maintain the radio communication performance under congested channel conditions but can severely degrade the communication performance from the application perspective. In this paper, we propose a collaborative resource allocation mechanism with content-aware congestion control for decentralized vehicular networks that prioritizes vehicles with relevant information under congested channel conditions. Our evaluation results show that our proposed approach maintains the radio communication performance and significantly increases the communication performance from the application perspective compared to content-agnostic congestion control mechanisms.

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Autonomous Maneuver Coordination Via Vehicular Communication

Cited by 33 publications

References 15 publications

Graph neural network and reinforcement learning for multi‐agent cooperative control of connected autonomous vehicles

Graph neural network and reinforcement learning for multi‐agent cooperative control of connected autonomous vehicles

The Risks of Machine Learning Systems

Prioritizing Relevant Information: Decentralized V2X Resource Allocation for Cooperative Driving

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