Cooperative vehicle localisation method based on the fusion of GPS, inter‐vehicle distance, and bearing angle measurements

Song, Xiaolin; Ling, Yifei; Cao, Haotian; Huang, Zhi Feng

doi:10.1049/iet-its.2018.5091

Cited by 15 publications

(15 citation statements)

References 23 publications

(30 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The most common vehicle localization method is the global navigation satellite system (GNSS), which estimates the vehicle location from pseudorange measurements of multiple satellites. The existing errors of the pseudorange, for example, satellite clock error, ionospheric delay and multipath error, make GNSS fulfill only the road-level localization applications [ 7 ]. To achieve higher positioning accuracy for CAVs, a variety of techniques have been proposed, including combining measurements of additional sensors (Inertial Measurement Unit) [ 8 ], differential GNSS techniques (Real Time Kinematic) [ 9 ], Cooperative Map Matching (CMM) [ 10 ], Simultaneous Localization and Mapping (SLAM) [ 11 ] and so on.…”

Section: Introductionmentioning

confidence: 99%

Robust Inter-Vehicle Distance Measurement Using Cooperative Vehicle Localization

Wang

Zhuang

Yin

et al. 2021

Sensors

View full text Add to dashboard Cite

Precise localization is critical to safety for connected and automated vehicles (CAV). The global navigation satellite system is the most common vehicle positioning method and has been widely studied to improve localization accuracy. In addition to single-vehicle localization, some recently developed CAV applications require accurate measurement of the inter-vehicle distance (IVD). Thus, this paper proposes a cooperative localization framework that shares the absolute position or pseudorange by using V2X communication devices to estimate the IVD. Four IVD estimation methods are presented: Absolute Position Differencing (APD), Pseudorange Differencing (PD), Single Differencing (SD) and Double Differencing (DD). Several static and dynamic experiments are conducted to evaluate and compare their measurement accuracy. The results show that the proposed methods may have different performances under different conditions. The DD shows the superior performance among the four methods if the uncorrelated errors are small or negligible (static experiment or dynamic experiment with open-sky conditions). When multi-path errors emerge due to the blocked GPS signal, the PD method using the original pseudorange is more effective because the uncorrelated errors cannot be eliminated by the differential technique.

show abstract

Section: Introductionmentioning

confidence: 99%

Robust Inter-Vehicle Distance Measurement Using Cooperative Vehicle Localization

Wang

Zhuang

Yin

et al. 2021

Sensors

View full text Add to dashboard Cite

show abstract

“…The evasive integration of sensing tools brings additional complications. To acquire the appropriate understanding of the environment, algorithms with demanding computational resources have been utilised [9, 10]. The main purpose from the extra inter‐connectivity of the vehicle and its neighbourhood is to raise autonomy [11].…”

Section: Introductionmentioning

confidence: 99%

Controller area network reliability: overview of design challenges and safety related perspectives of future transportation systems

Lakhal

Nasri

Adouane

et al. 2020

IET intell. transp. syst

View full text Add to dashboard Cite

The in‐vehicular networked control system is among the most critical embedded processes. The controller area network (CAN) has prevailed intra‐vehicle communication for decades. Meanwhile, requirements of future transportation systems are expected to emphasise the in‐vehicle communication complexity, which endangers the reliability/safety of the intelligent navigation. At first, this study reviews the recent solutions proposed to overcome the CAN expanding complexity. Challenges that tomorrow's intelligent vehicles may raise for CAN reliability are investigated. The comprehensive coverage of current research efforts to remove the impact of these challenges is presented. Further, the in‐vehicle system reliability of future automated vehicles is also related to the fault diagnosis performances. Hence, different classes of system‐level diagnosis strategies are compared relatively to the requirements of automotive embedded networks. Furthermore, to thoroughly cover CAN reliability engineering issues, focus is given to the automotive validation techniques. The hardware in the loop, real‐time analysis and computer‐aided‐design tools intervene in various phases along the in‐vehicular network life cycle. Parameters that stand behind the efficiency and accuracy of these techniques in validating the new generation of vehicles are analysed. The authors finally draw some deductive predictions about the future directions related to the reliability of the intelligent transportation system in‐vehicular communication.

show abstract

“…Especially, vehicular ad‐hoc network (VANET) applications have become of great interest in recent years for the advantage of no energy limitation, quickly changing network topology, and easy to integrate with other sensors [21]. In the VANET, the target vehicle can obtain relative position measurements [22] to its neighbouring vehicles, thus exploiting position measurements with other on‐board navigation sensors measurements to improve the navigation performance in accuracy, reliability, and robustness [23]. However, the large‐scale usage of the VANET still needs more time to be widely deployed.…”

Section: Introductionmentioning

confidence: 99%

Augmented extended Kalman filter with cooperative Bayesian filtering and multi‐models fusion for precise vehicle localisations

Xiao

Song

Cao

et al. 2020

IET Radar, Sonar & Navigation

Self Cite

View full text Add to dashboard Cite

Cooperative vehicle localisation method based on the fusion of GPS, inter‐vehicle distance, and bearing angle measurements

Cited by 15 publications

References 23 publications

Robust Inter-Vehicle Distance Measurement Using Cooperative Vehicle Localization

Robust Inter-Vehicle Distance Measurement Using Cooperative Vehicle Localization

Controller area network reliability: overview of design challenges and safety related perspectives of future transportation systems

Augmented extended Kalman filter with cooperative Bayesian filtering and multi‐models fusion for precise vehicle localisations

Contact Info

Product

Resources

About