Infrastructure Wi-Fi for connected autonomous vehicle positioning: A review of the state-of-the-art

Adegoke, Elijah I.; Zidane, Jasmine; Kampert, Erik; Ford, Col R.; Birrell, Stewart A.; Higgins, Matthew D.

doi:10.1016/j.vehcom.2019.100185

Cited by 36 publications

(19 citation statements)

References 59 publications

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“…CP solutions have been demonstrated to be useful for positioning and navigation of mobile platforms operating in swarms or networks. However, these solutions are mainly based on integration of inertial sensors with GNSS and/or radio signals [4][5][6]. The work of the joint IAG/FIG WG focuses on the use of alternative positioning, navigation and timing (PNT) techniques in combination with commonly employed localisation techniques and sensors.…”

Section: Introductionmentioning

confidence: 99%

Experimental Assessment of UWB and Vision-Based Car Cooperative Positioning System

et al. 2021

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The availability of global navigation satellite systems (GNSS) on consumer devices has caused a dramatic change in every-day life and human behaviour globally. Although GNSS generally performs well outdoors, unavailability, intentional and unintentional threats, and reliability issues still remain. This has motivated the deployment of other complementary sensors in such a way that enables reliable positioning, even in GNSS-challenged environments. Besides sensor integration on a single platform to remedy the lack of GNSS, data sharing between platforms, such as in collaborative positioning, offers further performance improvements for positioning. An essential element of this approach is the availability of internode measurements, which brings in the strength of a geometric network. There are many sensors that can support ranging between platforms, such as LiDAR, camera, radar, and many RF technologies, including UWB, LoRA, 5G, etc. In this paper, to demonstrate the potential of the collaborative positioning technique, we use ultra-wide band (UWB) transceivers and vision data to compensate for the unavailability of GNSS in a terrestrial vehicle urban scenario. In particular, a cooperative positioning approach exploiting both vehicle-to-infrastructure (V2I) and vehicle-to-vehicle (V2V) UWB measurements have been developed and tested in an experiment involving four cars. The results show that UWB ranging can be effectively used to determine distances between vehicles (at sub-meter level), and their relative positions, especially when vision data or a sufficient number of V2V ranges are available. The presence of NLOS observations is one of the principal factors causing a decrease in the UWB ranging performance, but modern machine learning tools have shown to be effective in partially eliminating NLOS observations. According to the obtained results, UWB V2I can achieve sub-meter level of accuracy in 2D positioning when GNSS is not available. Combining UWB V2I and GNSS as well V2V ranging may lead to similar results in cooperative positioning. Absolute cooperative positioning of a group of vehicles requires stable V2V ranging and that a certain number of vehicles in the group are provided with V2I ranging data. Results show that meter-level accuracy is achieved when at least two vehicles in the network have V2I data or reliable GNSS measurements, and usually when vehicles lack V2I data but receive V2V ranging to 2–3 vehicles. These working conditions typically ensure the robustness of the solution against undefined rotations. The integration of UWB with vision led to relative positioning results at sub-meter level of accuracy, an improvement of the absolute positioning cooperative results, and a reduction in the number of vehicles required to be provided with V2I or GNSS data to one.

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Section: Introductionmentioning

confidence: 99%

Experimental Assessment of UWB and Vision-Based Car Cooperative Positioning System

et al. 2021

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“…• Autonomous driving. In order for autonomous cars to be truly autonomous for navigation, it is essential that the vehicle is aware of its position, surrounding environment and nearby vehicles [22]. In addition, these vehicles may be very close to each other and drive at higher speeds (up to 200km/h).…”

Section: Vehicular Network Use Casesmentioning

confidence: 99%

Deep reinforcement learning techniques for vehicular networks: Recent advances and future trends towards 6G

Mekrache

Bradai

Moulay

et al. 2022

Vehicular Communications

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Employing machine learning into 6G vehicular networks to support vehicular application services is being widely studied and a hot topic for the latest research works in the literature. This article provides a comprehensive review of research works that integrated reinforcement and deep reinforcement learning algorithms for vehicular networks management with an emphasis on vehicular telecommunications issues. Vehicular networks have become an important research area due to their specific features and applications such as standardization, efficient traffic management, road safety, and infotainment. In such networks, network entities need to make decisions to maximize network performance under uncertainty. To achieve this goal, Reinforcement Learning (RL) can effectively solve decision-making problems. However, the state and action spaces are massive and complex in large-scale wireless networks. Hence, RL may not be able to find the best strategy in a reasonable time. Therefore, Deep Reinforcement Learning (DRL) has been developed to combine RL with Deep Learning (DL) to overcome this issue. In this survey, we first present vehicular networks and give a brief overview of RL and DRL concepts. Then we review RL and especially DRL approaches to address emerging issues in 6G vehicular networks. We finally discuss and highlight some unresolved challenges for further study.

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“…Moreover, with the growth of automotive internet of things (IoT) and increased wireless network penetration, radio access technologies such as Wi-Fi can be used to enhance localization in urban areas. As shown in surveyed works in [141], this technology is now gradually being adopted in GNSS receiver design. Furthermore, with advances in computing, more machine learning/artificial intelligence techniques will be adopted in addressing these GNSS vulnerabilities.…”

Section: Medium Lowmentioning

confidence: 99%

GNSS Vulnerabilities and Existing Solutions: A Review of the Literature

et al. 2021

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This literature review paper focuses on existing vulnerabilities associated with global navigation satellite systems (GNSSs). With respect to the civilian/non encrypted GNSSs, they are employed for proving positioning, navigation and timing (PNT) solutions across a wide range of industries. Some of these include electric power grids, stock exchange systems, cellular communications, agriculture, unmanned aerial systems and intelligent transportation systems. In this survey paper, physical degradations, existing threats and solutions adopted in academia and industry are presented. In regards to GNSS threats, jamming and spoofing attacks as well as detection techniques adopted in the literature are surveyed and summarized. Also discussed are multipath propagation in GNSS and non line-of-sight (NLoS) detection techniques. The review also identifies and discusses open research areas and techniques which can be investigated for the purpose of enhancing the robustness of GNSS.

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Infrastructure Wi-Fi for connected autonomous vehicle positioning: A review of the state-of-the-art

Cited by 36 publications

References 59 publications

Experimental Assessment of UWB and Vision-Based Car Cooperative Positioning System

Experimental Assessment of UWB and Vision-Based Car Cooperative Positioning System

Deep reinforcement learning techniques for vehicular networks: Recent advances and future trends towards 6G

GNSS Vulnerabilities and Existing Solutions: A Review of the Literature

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