V2I electromagnetic system for lateral position estimation of a vehicle

Mohsen, Ibrahim; Houdali, Nabil; Ditchi, Thierry; Holé, Stephane; Géron, Emmanuel

doi:10.1016/j.sna.2018.01.020

Cited by 5 publications

(11 citation statements)

References 22 publications

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“…In [32], the absolute round trip distance D is calculated using SAW with two resonant frequencies to determine the value of n. However in the present paper, n has no longer to be determined thanks to the evolution of D m during the vehicle displacement.…”

Section: Round Trip Distance D Determinationmentioning

confidence: 97%

“…To overcome these difficulties and to guaranty a low cost system only compatible with a large scale deployment, a functional prototype of a V2I sensor based on a high-frequency interaction was presented and described in [ 30 , 31 , 32 ]. The system operate in the Ultra High Frequency (UHF) band to provide a large penetration depth in water and snow.…”

Section: Introductionmentioning

confidence: 99%

“…The sensor described in [ 31 , 32 ] is composed of two parts: the first part is a high-frequency transceiver present on each side of the vehicle and the second part is a set of passive transponders sufficiently flat to be integrated in the lane markings. Based on the phase of the back-scattered signal from the transponders that are excited by the embedded transceiver, round trip distance between transmitting and receiving antenna on the vehicle is determined.…”

Section: Introductionmentioning

confidence: 99%

“…Assuming a transponder placed on the perpendicular bisector of the segment joining emitting and receiving antennas, the distance between vehicle and lane marking is then calculated with an accuracy of ±3 cm. Papers [ 31 , 32 ] validated the use of two resonant frequencies to avoid phases wrapping in

range.…”

Section: Introductionmentioning

confidence: 99%

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Lateral Position Measurement Based on Vehicles’ Longitudinal Displacement

Mohsen

Ditchi²,

Holé³

et al. 2020

Sensors

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The lateral position of a vehicle in its lane is crucial information required to develop intelligent assistant driving systems. Current studies reveal this information by mixing multiple sources such as cameras, LiDAR or accurate GNSS. Because these systems are not efficient in some degraded weather conditions, a cooperative Vehicle-to-Infrastructure sensor has been developed to help to determine lateral position of a vehicle in its lane. In this paper, the authors propose a completely new and original way to estimate lateral position of the vehicle in its lane using the longitudinal displacement. Using a system based on a hyper-frequency interaction between a transceiver module embedded in the vehicle and passive transponders that can be integrated in the road, for instance under the lane markings, a new signal processing algorithm is presented in order to determine the lateral distance between the vehicle and the transponder axis. The sensor has been tested in an external environment and has shown an estimated lateral distance error of 8 cm at most.

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Section: Round Trip Distance D Determinationmentioning

confidence: 97%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

range.…”

Section: Introductionmentioning

confidence: 99%

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Lateral Position Measurement Based on Vehicles’ Longitudinal Displacement

Mohsen

Ditchi²,

Holé³

et al. 2020

Sensors

Self Cite

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“…• Very short-range "direct" communication using RFID antenna (Mohsen et al, 2018) and Zigbee (Von Arnim et al, 2008).…”

Section: Connectivity An Automation Enablermentioning

confidence: 99%

Are Connected and Automated Vehicles the Silver Bullet for Future Transportation Challenges? Benefits and Weaknesses on Safety, Consumption, and Traffic Congestion

Gruyer

Orfila

Glaser

et al. 2021

Front. Sustain. Cities

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With the growing development of Cooperative, Connected, and Automated Mobility (CCAM), questions arise about the real impact of this innovative mobility on our daily life. CCAM originally promised to improve road safety. It is now a holistic solution for future mobility: the CCAM is there to optimize traffic, which can translate into strategies for reducing energy consumption or polluting emissions, without compromising road safety. The capability of CCAM is dependent on the reliability and robustness of its components, as it will be making life-impacting decisions. It is therefore necessary to be able to guarantee a high-level quality of sensors, communication, software, and hardware architecture. In this mobility ecosystem, the infrastructure and data that it will be able to produce is at the heart of current research issues. This paper addresses the following question: Are the Connected and automated Vehicles (CAVs) the silver bullet solution with which to answer the issues of the current mobility systems? This question is discussed by investigating the technologies used, the digital infrastructures, its robustness to cyber-attack, and their relationship with the claimed benefits on safety, energy and pollution management, traffic optimization, deployment strategy, and a link with the new generation of road infrastructures.

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A New Control Strategy of Vehicle Changing Lane Behaviors in the Weaving Area of the Urban Expressway under Connected Vehicle Environment

Jia

Meng

et al. 2019

Cictp 2019

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V2I electromagnetic system for lateral position estimation of a vehicle

Cited by 5 publications

References 22 publications

Lateral Position Measurement Based on Vehicles’ Longitudinal Displacement

Lateral Position Measurement Based on Vehicles’ Longitudinal Displacement

Are Connected and Automated Vehicles the Silver Bullet for Future Transportation Challenges? Benefits and Weaknesses on Safety, Consumption, and Traffic Congestion

A New Control Strategy of Vehicle Changing Lane Behaviors in the Weaving Area of the Urban Expressway under Connected Vehicle Environment

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