Vanets Meet Autonomous Vehicles: Multimodal Surrounding Recognition Using Manifold Alignment

Maalej, Yassine; Sorour, Sameh; Abdel-Rahim, Ahmed; Guizani, Mohsen

doi:10.1109/access.2018.2839561

Cited by 25 publications

(12 citation statements)

References 16 publications

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“…Their method is able to handle probability density functions that mix uniform and Gaussian distributions; such a flexibility would be also applicable to the optimal-sampling method [13] used in the present work, which is explained in Section 2.2. The integration of the localization techniques into vehicle systems’ architecture demands more computationally efficient techniques [14,15]. When high level tasks are demanded, as cooperative driving, real-time performance is critical [16].…”

Section: Introductionmentioning

confidence: 99%

Benchmarking Particle Filter Algorithms for Efficient Velodyne-Based Vehicle Localization

Blanco-Claraco

Mañas-Álvarez

Torres-Moreno

et al. 2019

Sensors

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Keeping a vehicle well-localized within a prebuilt-map is at the core of any autonomous vehicle navigation system. In this work, we show that both standard SIR sampling and rejection-based optimal sampling are suitable for efficient (10 to 20 ms) real-time pose tracking without feature detection that is using raw point clouds from a 3D LiDAR. Motivated by the large amount of information captured by these sensors, we perform a systematic statistical analysis of how many points are actually required to reach an optimal ratio between efficiency and positioning accuracy. Furthermore, initialization from adverse conditions, e.g., poor GPS signal in urban canyons, we also identify the optimal particle filter settings required to ensure convergence. Our findings include that a decimation factor between 100 and 200 on incoming point clouds provides a large savings in computational cost with a negligible loss in localization accuracy for a VLP-16 scanner. Furthermore, an initial density of ∼2 particles/m 2 is required to achieve 100% convergence success for large-scale (∼100,000 m 2 ), outdoor global localization without any additional hint from GPS or magnetic field sensors. All implementations have been released as open-source software.

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Section: Introductionmentioning

confidence: 99%

Benchmarking Particle Filter Algorithms for Efficient Velodyne-Based Vehicle Localization

Blanco-Claraco

Mañas-Álvarez

Torres-Moreno

et al. 2019

Sensors

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show abstract

“…To guarantee an immediate reaction, such vehicles need to collect information from their environment through their sensors: the position and velocity of other vehicles, the presence of obstacles, etc. But at the same time, this information needs to be completed by redundant information coming from V2X communication [5]. Thus, a vehicle will be able to react safely to events that may occur suddenly.…”

Section: Context and Motivationmentioning

confidence: 99%

Performance evaluation of an active signaling based time-slot scheduling scheme for connected vehicles

et al. 2021

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Latency is a very important metric to be taken into account in the design of the Connected and Automated Driving (CAD) technology. Today, connected vehicles have a dedicated technology named vehicle-to-everything (V2X). Many current research efforts and standardization activities aim to make the next generation of V2X technology able to offer new usages and services, with the main focus being on supporting road safety applications: hazard, obstacle and collision avoidance, etc. To satisfy the real-time constraints of safety applications, Time Division Multiple Access (TDMA) is a widely-used technique to control and share the channel. TDMA-based Medium Access Control (MAC) protocols have resolved many issues in VANETs and shown good performances in the literature by ensuring a bounded access delay to send an emergency message. However, latency can be further improved by eradicating some imperfections in the functionality of these protocols, such as the access collision problem which can occur when two or more vehicles in the same two-hop neighborhood set try to access the same slot at the same time. That is why we recently proposed an Active Signaling-based TDMA MAC protocol, called AS-DTMAC, which operates above the existing DTMAC protocol. In this paper, we analytically study the performance of AS-DMTAC when we have a homogeneous arrival on each time slot with an error in the signaling process.

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“…Existing system also offers a scheme for catering up demands of autonomous vehicles. The work carried out by Maalej et al [32] uses computer vision in order to develop a multimodal scheme for identification of object by the vehicle followed by recognition of it. A hypothetical scheme towards improving vehicular cloud has been also emphasized by Gong et al [33] and Huang et al [34].…”

Section: Integration Cloud With Vehicular Networkmentioning

confidence: 99%

Advancement in infotainment system in automotive sector with vehicular cloud network and current state of art

Reshma¹,

Chetanaprakash²

2020

IJECE

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The automotive industry has been incorporating various technological advancement on top-end versions of the vehicle order to improvise the degree of comfortability as well as enhancing the safer driving system. Infotainment system is one such pivotal system which not only makes the vehicle smart but also offers abundance of information as well as entertainment to the driver and passenger. The capability to offer extensive relay of service through infotainment system is highly dependent on vehicular adhoc network as well as back end support of cloud environment. However, it is know that such legacy system of vehicular adhoc network is also characterized by various problems associated with channel capacity, latency, heterogeneous network processing, and many more. Therefore, this paper offers a comprehensive insight to the research work being carried out towards leveraging the infotainment system in order to obtain the true picture of strength, limitation, and open end problems associated with infotainment system.

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Vanets Meet Autonomous Vehicles: Multimodal Surrounding Recognition Using Manifold Alignment

Cited by 25 publications

References 16 publications

Benchmarking Particle Filter Algorithms for Efficient Velodyne-Based Vehicle Localization

Benchmarking Particle Filter Algorithms for Efficient Velodyne-Based Vehicle Localization

Performance evaluation of an active signaling based time-slot scheduling scheme for connected vehicles

Advancement in infotainment system in automotive sector with vehicular cloud network and current state of art

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