On the Feasibility of Integrating mmWave and IEEE 802.11p for V2V Communications

Giordani, Marco; Zanella, Andréa; Higuchi, Tetsuya; Altintas, Onur; Zorzi, Michele

doi:10.1109/vtcfall.2018.8690697

Cited by 23 publications

(13 citation statements)

References 19 publications

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“…Data packets are set to the minimum size of an IPv6 packet 26 and users have a maximum throughput of about 10 Mbps 27 . The maximum range of users is about 105 to 110 m with a path loss based on a −log 10 ( d ) ( d is the distance) model 28,29 . The amount of static RSUs (fourth algorithm) is set to 5.…”

Section: Performance Evaluationmentioning

confidence: 99%

Selection of relays based on the classification of mobility‐type and localized network metrics in the Internet of Vehicles

Fabian

Rachedi

Gueguen

2021

Trans Emerging Tel Tech

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The ever increasing amount of connected mobile devices and users in the Internet of vehicles and the fact bicycles, electric scooters and users' smartphones are also connected poses a real challenge in terms of ensuring quality of service. The constantly changing topology due to the high‐mobility of devices and users greatly impacts its stability and the connectivity of devices. Furthermore, to ensure safety of people in the case of autonomous vehicles it is of paramount importance to ensure excellent reliability. This is why we have developed a solution that is based on machine learning to classify devices according to their mobility profile and that uses a scoring system to select the best candidates to act as mobile relays amongst devices with a suitable mobility profile. The scoring system allows to find critical locations in terms of user density. This solution does not require a dedicated infrastructure such as road side units. Simulations results will show the proposed solution increases the packet delivery ratio by up to 6%, reduces the energy consumption by up to 30% and increases the efficiency of bandwidth usage without sacrificing the end delay of users and devices compared with the state‐of‐the‐art.

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Section: Performance Evaluationmentioning

confidence: 99%

Selection of relays based on the classification of mobility‐type and localized network metrics in the Internet of Vehicles

Fabian

Rachedi

Gueguen

2021

Trans Emerging Tel Tech

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“…Compared to other types of sensors such as RADARs or color/thermal cameras [2], LiDARs are robust under almost all lighting and weather conditions, with or without glare and shadows, and are currently the most precise sensors to measure range [3]. On the other hand, LiDAR acquisitions may produce very large volumes of data that can be challenging to handle with standard Vehicle-to-Everything (V2X) technologies [4], [5]. One possible method to solve capacity issues is by leveraging the millimeter wave (mmWave) spectrum, as promoted by recent IEEE and 3GPP standardization activities for future vehicular networks [6].…”

Section: Introductionmentioning

confidence: 99%

Point Cloud Compression for Efficient Data Broadcasting: A Performance Comparison

Nardo¹,

Peressoni²,

Testolina³

et al. 2022

Preprint

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The worldwide commercialization of fifth generation (5G) wireless networks and the exciting possibilities offered by connected and autonomous vehicles (CAVs) are pushing toward the deployment of heterogeneous sensors for tracking dynamic objects in the automotive environment. Among them, Light Detection and Ranging (LiDAR) sensors are witnessing a surge in popularity as their application to vehicular networks seem particularly promising. LiDARs can indeed produce a threedimensional (3D) mapping of the surrounding environment, which can be used for object detection, recognition, and topography. These data are encoded as a point cloud which, when transmitted, may pose significant challenges to the communication systems as it can easily congest the wireless channel. Along these lines, this paper investigates how to compress point clouds in a fast and efficient way. Both 2D-and a 3D-oriented approaches are considered, and the performance of the corresponding techniques is analyzed in terms of (de)compression time, efficiency, and quality of the decompressed frame compared to the original. We demonstrate that, thanks to the matrix form in which LiDAR frames are saved, compression methods that are typically applied for 2D images give equivalent results, if not better, than those specifically designed for 3D point clouds.

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“…However, LiDAR point clouds may generate very large volumes of data. For example, a 3D frame with 0.7 million points acquired at 30 fps needs a bandwidth of around 500 Mbps [3], which can be challenging to handle with standard communication systems [4] (e.g., the IEEE 802.11p transmission service, currently employed for Vehicle-to-Vehicle (V2V) operations, offers data exchange at a nominal rate up to 27 Mbps [5]).…”

Section: Introductionmentioning

confidence: 99%

Hybrid Point Cloud Semantic Compression for Automotive Sensors: A Performance Evaluation

Varischio¹,

Mandruzzato²,

Bullo³

et al. 2021

Preprint

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In a fully autonomous driving framework, where vehicles operate without human intervention, information sharing plays a fundamental role. In this context, new network solutions have to be designed to handle the large volumes of data generated by the rich sensor suite of the cars in a reliable and efficient way. Among all the possible sensors, Light Detection and Ranging (LiDAR) can produce an accurate 3D point cloud representation of the surrounding environment, which in turn generates high data rates. For this reason, efficient point cloud compression is paramount to alleviate the burden of data transmission over bandwidth-constrained channels and to facilitate real-time communications. In this paper, we propose a pipeline to efficiently compress LiDAR observations in an automotive scenario. First, we leverage the capabilities of RangeNet++, a Deep Neural Network (DNN) used to semantically infer point labels, to reduce the channel load by selecting the most valuable environmental data to be disseminated. Second, we compress the selected points using Draco, a 3D compression algorithm which is able to obtain compression up to the quantization error. Our experiments, validated on the Semantic KITTI dataset, demonstrate that it is possible to compress and send the information at the frame rate of the LiDAR, thus achieving real-time performance.

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On the Feasibility of Integrating mmWave and IEEE 802.11p for V2V Communications

Cited by 23 publications

References 19 publications

Selection of relays based on the classification of mobility‐type and localized network metrics in the Internet of Vehicles

Selection of relays based on the classification of mobility‐type and localized network metrics in the Internet of Vehicles

Point Cloud Compression for Efficient Data Broadcasting: A Performance Comparison

Hybrid Point Cloud Semantic Compression for Automotive Sensors: A Performance Evaluation

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