A Cooperative Vehicle Ego-localization Application Using V2V Communications with CBL Clustering

Rivoirard, Lucas; Wahl, Martine; Sondi, Patrick; Gruyer, Dominique; Berbineau, Marion

doi:10.1109/ivs.2018.8500688

Cited by 10 publications

(6 citation statements)

References 6 publications

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“…Therefore, unlike most of the geographic-based routing protocols that would require an infrastructure at least for the location service, CBL can perform without any preexisting infrastructure. Moreover, CBL supports unicast communications and oriented broadcast (upstream or downstream) in the road traffic, which can be useful for some applications such as cooperative perception and cooperative localization [19]. Through simulation, we already showed that CBL reduces routing traffic load [18] in OLSR in comparison with the MPR, without degrading network performance for the applications, notably on end-toend delay and packet delivery ratio [20].…”

Section: Fig 2 Node Status In Clustering Schemesmentioning

confidence: 93%

Multipoint Relaying Versus Chain-Branch-Leaf Clustering Performance in Optimized Link State Routing-Based Vehicular Ad Hoc Networks

Rivoirard

Wahl

Sondi

2020

IEEE Trans. Intell. Transport. Syst.

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Routing protocols for vehicular ad hoc networks resort to clustering in order to optimize network performance. Concerning the Optimized Link State Routing (OLSR) protocol and the plethora of its derivatives, the multipoint relaying (MPR) technique has proven its efficiency as an accurate clustering scheme over the last two decades. However, it has been emphasized recently that the MPR technique, which was originally designed for open areas, does not benefit from the particular configuration of road sections which are intrinsically spatially constrained. A clustering scheme exploiting this particularity, namely Chain-Branch-Leaf (CBL), has been introduced in order to enhance the flooding of broadcast traffic, including that related to routing operations. In this paper, both MPR and CBL are evaluated through MATLAB simulation over several scenarios based on realistic road configurations and traffic generated with SUMO simulator. The results show that CBL actually reduces the number of nodes acting as relays (cluster-heads) in the network, thus decreasing the routing traffic related to creation and retransmission of topology control (TC) messages. Also, they show that, with CBL, the nodes chosen as relays remain longer in this role, thus favoring the overall network stability, and that most of the nodes remain attached longer to the same relay than with the MPR technique.

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Section: Fig 2 Node Status In Clustering Schemesmentioning

confidence: 93%

Multipoint Relaying Versus Chain-Branch-Leaf Clustering Performance in Optimized Link State Routing-Based Vehicular Ad Hoc Networks

Rivoirard

Wahl

Sondi

2020

IEEE Trans. Intell. Transport. Syst.

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“…The authors of that work implemented a road constraint of nearby vehicles to reduce positioning uncertainty and a DDGPS for pseudo-range correction. Then, Rivoirard et al [102] proposed a Chain-Branch Leaf (CBL) clustering scheme to guarantee that the vehicle in reference [7] can exchange its state and pose and error correction. Meanwhile, this scheme can provide an accurate V2V communication service to the vehicles under a VANET.…”

Section: A V2v-based Localisationmentioning

confidence: 99%

Real-Time Performance-Focused Localization Techniques for Autonomous Vehicle: A Review

Lü

Smart

et al. 2022

IEEE Trans. Intell. Transport. Syst.

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Real-time, accurate, and robust localisation is critical for autonomous vehicles (AVs) to achieve safe, efficient driving, whilst real-time performance is essential for AVs to achieve their current position in time for decision making. To date, no review paper has quantitatively compared the real-time performance between different localisation techniques based on various hardware platforms and programming languages and analysed the relations among localisation methodologies, real-time performance and accuracy. Therefore, this paper discusses the state-of-the-art localisation techniques and analyses their overall performance in AV application. For further analysis, this paper firstly proposes a localisation algorithm operations capability (LAOC)-based equivalent comparison method to compare the relative computational complexity of different localisation techniques; then, it comprehensively discusses the relations among methodologies, computational complexity, and accuracy. Analysis results show that the computational complexity of localisation approaches differs by a maximum of about times, whilst accuracy varies by about 100 times. Vision-and data fusion-based localisation techniques have about 2-5 times potential for improving accuracy compared with lidar-based localisation. Lidar-and vision-based localisation can reduce computational complexity by improving image registration method efficiency. Data fusion-based localisation can achieve better real-time performance compared with lidar-and vision-based localisation because each standalone sensor does not need to develop a complex algorithm to achieve its best localisation potential. Vehicle-toeverything (V2X) technology can improve positioning robustness. Finally, the potential solutions and future orientations of AVs' localisation based on the quantitative comparison results are discussed.

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“…High-accuracy localization of vehicles is one of the most important functions in intelligent transportation systems (ITS) and autonomous driving. In recent years, many methods of high-accuracy vehicle localization have been developed, such as vision-based localization methods [1], map-based methods [2,3], vehicle-to-vehicle (V2V)-based methods [4,5], vehicle-to-infrastructure (V2I)-based methods [6][7][8][9], and so on. However, both visionand map-based vehicle localization methods have high computation and storage resource overhead, making them ineffective for multi-sensor multi-vehicle systems (MSMVs) [10].…”

Section: Introductionmentioning

confidence: 99%

Cooperative Vehicle Localization in Multi-Sensor Multi-Vehicle Systems Based on an Interval Split Covariance Intersection Filter with Fault Detection and Exclusion

Shan,

Cabani,

Chafouk

2024

Vehicles

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In the cooperative multi-sensor multi-vehicle (MSMV) localization domain, the data incest problem yields inconsistent data fusion results, thereby reducing the accuracy of vehicle localization. In order to address this problem, we propose the interval split covariance intersection filter (ISCIF). At first, the proposed ISCIF method is applied to the absolute positioning step. Then, we combine the interval constraint propagation (ICP) method and the proposed ISCIF method to realize relative positioning. Additionally, in order to enhance the robustness of the MSMV localization system, a Kullback–Leibler divergence (KLD)-based fault detection and exclusion (FDE) method is implemented in our system. Three simulations were carried out: Simulation scenarios 1 and 2 aimed to assess the accuracy of the proposed ISCIF with various capabilities of absolute vehicle positioning, while simulation scenario 3 was designed to evaluate the localization performance when faults were present. The simulation results of scenarios 1 and 2 demonstrated that our proposed vehicle localization method reduced the root mean square error (RMSE) by 8.9% and 15.5%, respectively, compared to the conventional split covariance intersection filter (SCIF) method. The simulation results of scenario 3 indicated that the implemented FDE method could effectively reduce the RMSE of vehicles (by about 55%) when faults were present in the system.

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A Cooperative Vehicle Ego-localization Application Using V2V Communications with CBL Clustering

Cited by 10 publications

References 6 publications

Multipoint Relaying Versus Chain-Branch-Leaf Clustering Performance in Optimized Link State Routing-Based Vehicular Ad Hoc Networks

Multipoint Relaying Versus Chain-Branch-Leaf Clustering Performance in Optimized Link State Routing-Based Vehicular Ad Hoc Networks

Real-Time Performance-Focused Localization Techniques for Autonomous Vehicle: A Review

Cooperative Vehicle Localization in Multi-Sensor Multi-Vehicle Systems Based on an Interval Split Covariance Intersection Filter with Fault Detection and Exclusion

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