Impact of LTE-V2X Connectivity on Global Occupancy Maps in a Cooperative Collision Avoidance (CoCA) System

Mouawad, Nadia; Mannoni, Valérian; Denis, Benoît; Silva, Alex Pereira da

doi:10.1109/vtc2021-spring51267.2021.9449034

Cited by 9 publications

(11 citation statements)

References 12 publications

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“…The RSUs fused all the messages into a global occupancy map and sent it back to all vehicles in range. The authors found that the configuration for the best performance was a message size of 1,685 bytes for global occupancy maps, which led to the lowest obstacle mis-detection rate and a packet generation rate of 10 Hz when the number of vehicles was less than 70 and 5 Hz otherwise [71].…”

Section: Author -Institutementioning

confidence: 99%

Environmental and Safety Impacts of Vehicle-to-Everything Enabled Applications: A Review of State-of-the-Art Studies

Du¹,

Ahn²,

Farag³

et al. 2022

Preprint

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With the rapid development of communication technology, connected vehicles (CV) have the potential, through the sharing of data, to enhance vehicle safety and reduce vehicle energy consumption and emissions. Numerous research efforts have been conducted to quantify the impacts of CV applications, assuming instant and accurate communication among vehicles, devices, pedestrians, infrastructure, the network, the cloud, and the grid, collectively known as V2X (vehicle-to-everything). The use of cellular vehicle-to-everything (C-V2X), to share data is emerging as an efficient means to achieve this objective. C-V2X releases 14 and 15 utilize the 4G LTE technology and release 16 utilizes the new 5G new radio (NR) technology. C-V2X can function without network infrastructure coverage and has a better communication range, improved latency, and greater data rates compared to older technologies. Such highly efficient interchange of information among all participating parts in a CV environment will not only provide timely data to enhance the capacity of the transportation system but can also be used to develop applications that enhance vehicle safety and minimize negative environmental impacts. However, before the full benefits of CV can be achieved, there is a need to thoroughly investigate the effectiveness, strengths, and weaknesses of different CV applications, the communication protocols, the varied results with different CV market penetration rates (MPRs), the interaction of CVs and human driven vehicles, the integration of multiple applications, and the errors and latencies associated with data communication. This paper reviews existing literature on the environmental, mobility and safety impacts of CV applications, identifies the gaps in our current research of CVs and recommends future research directions. The results of this paper will help shape the future research direction for CV applications to realize their full potential benefits.

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Section: Author -Institutementioning

confidence: 99%

Environmental and Safety Impacts of Vehicle-to-Everything Enabled Applications: A Review of State-of-the-Art Studies

Du¹,

Ahn²,

Farag³

et al. 2022

Preprint

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“…The tracking quality of locally perceived objects will not be improved by the perception data from other ITS-Ss, which would otherwise be achieved in joint fusion schemes. There are also occupancy-grid based data fusion methods for CP, such as in [6], [7]. This kind of fusion framework is considered computationally expensive, where pixel-wise iterative fusion is required.…”

Section: Introductionmentioning

confidence: 99%

A Novel Probabilistic V2X Data Fusion Framework for Cooperative Perception

Shan¹,

Narula²,

Wong³

et al. 2022

Preprint

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The paper addresses the vehicle-to-X (V2X) data fusion for cooperative or collective perception (CP). This emerging and promising intelligent transportation systems (ITS) technology has enormous potential for improving efficiency and safety of road transportation. Recent advances in V2X communication primarily address the definition of V2X messages and data dissemination amongst ITS stations (ITS-Ss) in a traffic environment. Yet, a largely unsolved problem is how a connected vehicle (CV) can efficiently and consistently fuse its local perception information with the data received from other ITS-Ss. In this paper, we present a novel data fusion framework to fuse the local and V2X perception data for CP that considers the presence of cross-correlation. The proposed approach is validated through comprehensive results obtained from numerical simulation, CARLA simulation, and real-world experimentation that incorporates V2X-enabled intelligent platforms. The realworld experiment includes a CV, a connected and automated vehicle (CAV), and an intelligent roadside unit (IRSU) retrofitted with vision and lidar sensors. We also demonstrate how the fused CP information can improve the awareness of vulnerable road users (VRU) for CV/CAV, and how this information can be considered in path planning/decision making within the CAV to facilitate safe interactions.

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“…For each vehicle, occupancy grids are calculated from sensor measurements and then merged in terms of occupancy and confidence. In [9], authors evaluate the impact of LTE-V2X connectivity performance on the fusion of local occupancy maps. Moreover, a study was conducted in order to define the best compromises between the physical layer configuration, occupancy maps resolution, message periodicity, and obstacle detection capabilities.…”

Section: Introductionmentioning

confidence: 99%

“…None of these papers have considered a fusion algorithm that can analyze received CPMs according to the standardized generation rules. The occupancy maps impose the use of large packets and thus high Modulation Code Schemes (MCS) [9], which may affect the connectivity performance. Besides, the fusion of local occupancy maps proposed in [8] may induce computation complexity.…”

Section: Introductionmentioning

confidence: 99%

“…Besides, the fusion of local occupancy maps proposed in [8] may induce computation complexity. Thus, in order to enhance V2X connectivity performance compared to [9] and reduce the fusion algorithm complexity in comparison with [8], we propose a low complexity fusion algorithm that can process CPM according to the ETSI generation rules, while taking into account the impact of V2X connectivity on the CPM fusion.…”

Section: Introductionmentioning

confidence: 99%

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Collective Perception Messages: New Low Complexity Fusion and V2X Connectivity Analysis

Mouawad

Mannoni

2021

2021 IEEE 94th Vehicular Technology Conference (VTC2021-Fall)

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Connected vehicles are equipped with sensors that can detect surrounding objects. However, vehicles perception is limited by sensors range or by the presence of other obstacles. Collective perception can improve vehicles perception by allowing them to exchange information about detected obstacles. In this context, vehicles rely on on-board sensors in order to generate Collective Perception Messages (CPM) that are exchanged by means of LTE-V2X connectivity. In order to reveal hidden obstacles and obtain a coherent visualization about the environment, CPM fusion is then crucial. In this work, we propose a novel low complexity fusion algorithm for CPM. Moreover, we evaluate the impact of LTE-V2X connectivity performance, specially in terms of packets loss, on the fusion. Simulation results in a smart junction demonstrate the relevance and efficiency of our algorithm in terms of obstacles detection capabilities.

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Impact of LTE-V2X Connectivity on Global Occupancy Maps in a Cooperative Collision Avoidance (CoCA) System

Cited by 9 publications

References 12 publications

Environmental and Safety Impacts of Vehicle-to-Everything Enabled Applications: A Review of State-of-the-Art Studies

Environmental and Safety Impacts of Vehicle-to-Everything Enabled Applications: A Review of State-of-the-Art Studies

A Novel Probabilistic V2X Data Fusion Framework for Cooperative Perception

Collective Perception Messages: New Low Complexity Fusion and V2X Connectivity Analysis

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