Steering control of automated vehicles using absolute positioning gps and magnetic markers

Hernandez, J.I.; Kuo, Chen-Yuan

doi:10.1109/tvt.2002.807224

Cited by 52 publications

(21 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…We can also define an upper bound on each relative acceleration component by setting the other components equal to zero and solving the quadratic equation in (8). Thus, for a = [a 1 , a 2 , a 3 ] T , the inequality…”

Section: V2v Large-scale Coherence In Log-distance Modelmentioning

confidence: 99%

“…For link adaptation with path loss, the receiver may feed back one of two measurements: 1) received signal strength 2) position/motion information [7]. Position/motion information is made available by GPS or similar localization technologies which have become pervasive in vehicular environments [8], [9]. For 1) the transmitter must complete a windowed average of received signal strength to estimate path loss.…”

mentioning

confidence: 99%

See 1 more Smart Citation

Link Adaptation with Position/Motion Information in Vehicle-to-Vehicle Networks

Daniels

2012

IEEE Trans. Wireless Commun.

View full text Add to dashboard Cite

Section: V2v Large-scale Coherence In Log-distance Modelmentioning

confidence: 99%

mentioning

confidence: 99%

Link Adaptation with Position/Motion Information in Vehicle-to-Vehicle Networks

Daniels

2012

IEEE Trans. Wireless Commun.

View full text Add to dashboard Cite

“…Lateral deviation y may be obtained, for instance, using a magnetometer and magnetic markers or cables embedded in the road centerline [10], [19] or using a visionbased lane-sensing system [11], [13]. Reference [12] proposes a sensor fusion concept wherein a magnetic marker system is used in combination with absolute positioning GPS to achieve better accuracy at lower cost. This paper only considers the two-car platooning problem.…”

Section: A Control Objectivementioning

confidence: 99%

Neuroadaptive Output Tracking of Fully Autonomous Road Vehicles With an Observer

Kumarawadu

Lee

2009

IEEE Trans. Intell. Transport. Syst.

View full text Add to dashboard Cite

Automated vehicle control systems are a key technology for intelligent vehicle highway systems (IVHSs). This paper presents an automated vehicle control algorithm for combined longitudinal and lateral motion control of highway vehicles, with special emphasis on front-wheel-steered four-wheel road vehicles. The controller is synthesized using an online neural-estimator-based control law that works in combination with a lateral velocity observer. The online adaptive neural-estimator-based design approach enables the controller to counteract for inherent model discrepancies, strong nonlinearities, and coupling effects. The neurocontrol approach can guarantee the uniform ultimate bounds (UUBs) of the tracking and observer errors and the bounds of the neural weights. The key design features are 1) inherent coupling effects will be taken into account as a result of combining of the two control issues, viz., lateral and longitudinal control; 2) rather ad hoc numerical approximations of lateral velocity will be avoided via a combined controller-observer design; and 3) closed-loop stability issues of the overall system will be established. The algorithm is validated via a formative mathematical analysis based on a Lyapunov approach and numerical simulations in the presence of parametric uncertainties, as well as severe and adverse driving conditions. Index Terms-Automated highway vehicle, combined lateral and longitudinal control, intelligent vehicle highway system (IVHS), lateral velocity observer, neural networks (NNs), vehicle dynamics. NOMENCLATURE mVehicle mass. I z Vehicle yaw moment of inertia. fRolling friction coefficient. a Distance from the front wheel (FW) to the center of gravity (c.o.g.). bDistance from the rear wheel to the c.o.g.

show abstract

“…Many vehicle tracking methods have been previously proposed where one popular method is using global positioning system (GPS) sensors and/or magnetic markers [18]. Other tracking methods specific for DWC applications are also proposed in [19][20][21], where a radio-frequency identification (RFID) tag and reader were implemented.…”

Section: Introductionmentioning

confidence: 99%

An Autonomous Coil Alignment System for the Dynamic Wireless Charging of Electric Vehicles to Minimize Lateral Misalignment

et al. 2017

View full text Add to dashboard Cite

This paper proposes an autonomous coil alignment system (ACAS) for electric vehicles (EVs) with dynamic wireless charging (DWC) to mitigate the reduction in received power caused by lateral misalignment between the source and load coils. The key component of the ACAS is a novel sensor coil design, which can detect the load coil's left or right position relative to the source coil by observing the change in voltage phase. This allows the lateral misalignment to be estimated through the wireless power transfer (WPT) system alone, which is a novel tracking method for vehicular applications. Once misalignment is detected, the vehicle's lateral position is self-adjusted by an autonomous steering function. The feasibility of the overall operation of the ACAS was verified through simulation and experiments. In addition, an analysis based on experimental results was conducted, demonstrating that 26% more energy can be transferred during DWC with the ACAS, just by keeping the vehicle's load coil aligned with the source coil.

show abstract

Steering control of automated vehicles using absolute positioning gps and magnetic markers

Cited by 52 publications

References 14 publications

Link Adaptation with Position/Motion Information in Vehicle-to-Vehicle Networks

Link Adaptation with Position/Motion Information in Vehicle-to-Vehicle Networks

Neuroadaptive Output Tracking of Fully Autonomous Road Vehicles With an Observer

An Autonomous Coil Alignment System for the Dynamic Wireless Charging of Electric Vehicles to Minimize Lateral Misalignment

Contact Info

Product

Resources

About