Localization Prediction in Vehicular Ad Hoc Networks

Balico, Leandro N.; Loureiro, Antônio A. F.; Nakamura, Eduardo F.; Barreto, Raimundo; Pazzi, Richard W.; Oliveira, Horácio A. B. F.

doi:10.1109/comst.2018.2841901

Cited by 83 publications

(40 citation statements)

References 97 publications

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“…of auto-driving is vehicular positioning [2]- [4], namely positioning nearby vehicles and tracking other parameters such as sizes and trajectories. The information then serves as inputs for computing and control tasks such as navigation and accidence avoidance.…”

Section: Hidden Vehiclementioning

confidence: 99%

Hidden Vehicle Sensing via Asynchronous V2V Transmission: A Multi-Path-Geometry Approach

Han

Chae

et al. 2019

IEEE Access

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Accurate vehicular sensing is a basic and important operation in autonomous driving. Unfortunately, the existing techniques have their own limitations. For instance, the communication-based approach (e.g., transmission of GPS information) has high latency and low reliability while the reflection-based approach (e.g., RADAR) is incapable of detecting hidden vehicles (HVs) without line-of-sight. This is arguably the reason behind some recent fatal accidents involving autonomous vehicles. To address this issue, this paper presents a novel HV-sensing technology that exploits multi-path transmission from a HV to a sensing vehicle (SV). The powerful technology enables the SV to detect multiple HV-state parameters including position, orientation of driving direction, and size. Its implementation does not even require transmitterreceiver synchronization like conventional mobile positioning techniques. Our design approach leverages estimated information on multi-path [namely their angles-of-arrival (AoA), angles-of-departure (AoD), and time-of-arrival (ToA)] and their geometric relations. As a result, a complex system of equations or optimization problems, where the desired HV-state parameters are variables, can be formulated for different channel-noise conditions. The development of intelligent solution methods ranging from leastsquare estimator to disk/box minimization yields a set of practical HV-sensing techniques. We study their feasibility conditions in terms of the required number of paths. Furthermore, practical solutions, including sequential path combining and random directional beamforming, are proposed to enable HVsensing given insufficient paths. Last, realistic simulation of driving in both highway and rural scenarios demonstrates the effectiveness of the proposed techniques. In summary, the proposed technique will enhance the capabilities of existing vehicular sensing technologies (e.g., RADAR and LIDAR) by enabling HV-sensing.

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Section: Hidden Vehiclementioning

confidence: 99%

Hidden Vehicle Sensing via Asynchronous V2V Transmission: A Multi-Path-Geometry Approach

Han

Chae

et al. 2019

IEEE Access

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“…Similarly, Feng and Zhu [25] and Zheng [26] also surveyed various applications of large-scale data mining but focused on providing a quick understanding of the field of trajectory data mining. Balico et al [27] have studied and analyzed proposed approaches for localization, target tracking and time-series prediction techniques that can be leveraged to estimate the future location of a vehicle in vehicular ad hoc networks (VANETs) rather than common mobile networks. And in [4], which compared the most commonly found machine learning (ML) algorithms in terms of certain self organizing networks (SON) and provided a guideline of applying ML techniques to SON, only part of self-optimization (mobility management, resource optimization) has mentioned the aspect of mobility prediction.…”

Section: Overview Of Survey Papers and Main Contributionsmentioning

confidence: 99%

Mobility Prediction: A Survey on State-of-the-Art Schemes and Future Applications

2019

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Recently, mobility has gathered tremendous interest as the users' desire for consecutive connections and better quality of service has increased. An accurate prediction of user mobility in mobile networks provides efficient resource and handover management, which can avoid unacceptable degradation of the perceived quality. Therefore, mobility prediction in wireless networks is of great importance and many works have been dedicated to this issue. In this paper, the necessity of mobility prediction, together with its intrinsic characteristics in terms of movement predictability, prediction outputs, and performance metrics is discussed. Moreover, the learning perspective of solutions to mobility prediction has been studied. Specifically, an overview of the state-of-the-art approaches is provided, including Markov chain, hidden Markov model, artificial neural network, Bayesian network, and data mining based on different kinds of knowledge. At last, this paper also explores the open research challenges due to the advent of the fifthgeneration mobile system and puts forward some potential trends in the near future. INDEX TERMS Mobility prediction, quality of service (QoS), resource reservation, handover management, the fifth-generation mobile system (5G).

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“…Similarly, the authors of [137] proposes an online probabilistic neural network for predicting the next serving access point using the vehicles' mobility information. Strictly related is the ability to predict the vehicles trajectory, and that can be a valuable indicator for effective mobility management and routing protocols [138]. Nevertheless, the issue in these ML algorithms comes from the difficulty in formulating a proper objective that jointly optimizes the performance of the various links and the definition of a numeric reward, especially in settings where energy consumption must be taken into consideration.…”

Section: Frequent Handover and Mobility Managementmentioning

confidence: 99%