Data fusion and path-following controllers comparison for autonomous vehicles

Nunes, Urbano; Bento, Luis Conde

doi:10.1007/s11071-006-9108-y

Cited by 27 publications

(9 citation statements)

References 16 publications

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“…ISR-TRAFSIM 4.0 has been used as the simulation environment (available at: http://www.isr.uc.pt/ conde/isr-trafsim/). It is an open-source Matlab-based simulator and it has been used in dierent studies such as automatic trac control, vehicle emissions analysis (Bento , 2013), vehicle pathfollowing control (Nunes , 2007) and sensor fusion for vehicle awareness. Seven vehicles traveling on an urban road network have been simulated (see Figure 4(a)(c)).…”

Section: Simulation Setupmentioning

confidence: 99%

Cooperative GNSS positioning aided by road-features measurements

Bento

Nunes

2017

Transportation Research Part C: Emerging Technologies

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Cooperation between road users trough V2X communication is a way to improve GNSS localization accuracy. When vehicles localization systems involve standalone GNSS receivers, the resulting accuracy can be aected by satellite-specic errors of several meters. This paper studies how road-features like lane marking detected by on-board cameras can be exploited to reduce absolute position errors of cooperative vehicles sharing information in real-time in a network. The algorithms considered in this work are based on a error bounded set membership strategy. In every vehicle, a set membership algorithm computes the absolute position and an estimation of the satellite-specic errors by using raw GNSS pseudoranges, lane boundary measurements and a 2D georeferenced road map which provides absolute geometric constraints. As lane-boundary measurements provide essentially cross-track corrections in the position estimation process, cooperation enables the vehicles to improve their own estimates thanks to the dierent orientation of the roads. Set-membership methods are very ecient to solve this problem since they don't involve any independence hypothesis of the errors and so, the same information can be used several times in the computation. Such class of algorithm provides a novel approach to improve position accuracy for connected vehicles guaranteeing the integrity of the computed solution which is pivoting for automated automotive systems requiring guaranteed safety-critical solutions. Results from simulations and real experiments show that sharing position corrections reduces signicantly satellite-specic GNSS errors eects in both cross-track and along-track components. Moreover, it is shown that lane-boundary measurements help reducing estimation errors for all the networked vehicles even those which are not equipped with an embedded perception system.

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Section: Simulation Setupmentioning

confidence: 99%

Cooperative GNSS positioning aided by road-features measurements

Bento

Nunes

2017

Transportation Research Part C: Emerging Technologies

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“…In [2]- [8], several data are used to estimate the orientation angle. Basically, these method use odometry data.…”

Section: Estimation Of Orientation Anglementioning

confidence: 99%

Position and Orientation Estimation of a Maneticalluy Guided-Articulated Vehicle

Yun¹,

Kim²,

Min³

et al. 2011

The Transactions of The Korean Institute of Electrical Engineer

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-For automated guidance control of a magnetically guided-all wheel steered vehicle, it is necessary to have information about position and orientation of the vehicle, and deviations from the reference path in real time. The magnet reference system considered here consists of three magnetic sensors mounted on the vehicle and magnetic markers, which are non-equidistantly buried in the road. This paper presents an observer to estimate such position and orientation at the center of gravity of the vehicle. This algorithm is based on the simple kinematic model of vehicle and uses the data of wheel velocity, steering angle, and the discrete measurements of marker positions. Since this algorithm requires the exact values of initial states, we have also proposed an algorithm of determining the initial position and orientation from the 16 successive magnet pole data, which are given by the magnetic measurement system(MMS). The proposed algorithm is capable of continuing to estimate for the case that the magnetic sensor fail to measure up to three successive magnets. It is shown through experimental data that the proposed algorithm works well within permissible error range. 서 론The articulated vehicle is a new type of public transportation to combine the advantage of commuter buses and railroad vehicles. To achieve good tracking performance similar to railroad vehicle, these vehicles have to be equipped with a lateral guidance system. Such a lateral guidance system requires informations about the vehicles position to calculate the tracking errors relative to the path to be followed. In most cases, the absolute position of the vehicle cannot be measured continuously. msec. It turns out that the information necessary for the guidance control should be estimated at the intermediate time when the magnetic sensors are moving from a marker to next marker, so that this is continuously available for guidance control. Furthermore, it is required to estimate the position and orientation without large errors for the case that the MMS fails to detect three markers consecutively.In this paper, we propose an observer of estimating the vehicle position and orientation. The estimator is composed of an integration algorithm based on vehicle kinematic model, EKF, position update algorithm and orientation angle compensation algorithm using the MMS data. The EKF is used to calculate the vehicle velocity and the side-slip angle at a reference point of the vehicle by using wheel encoders and steering angles data. This is the similar method as one in [5]. The MMS update algorithm is used for estimating the absolute position of vehicle and the orientation angle compensation algorithm

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“…methods were proposed to solve the lateral dynamic control problem and the problem regarding the anti-idling system; the learning control method proposed in [13] can be used to solve the path-following problem in autonomous vehicles, while the adaptive robust control method proposed in [14] can be used to control the active suspensions in electrohydraulic actuators. Moreover, artificial neural network control [15], the chained systems theory [16], optimal control [17], fuzzy control [18], data-driven control [19], and many other approaches have also been suggested for other applications [20], [21].…”

Section: Introductionmentioning

confidence: 99%

State Feedback Control Based on Regional Pole Placement on Handling Stability of Ground Vehicles

Zhang

Tian

et al. 2019

IEEE Access

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A controller based on regional pole placement for the handling stability of an intelligent ground vehicle is proposed in this paper. The 2 degree-of-freedom model for the vehicle was applied to analysis the uncertainty generated by the tires and model of the vehicle. Another parameter that must be considered is the change in the vehicle's speed. A polytope with finite vertices was applied for the uncertainty generated by the vehicle's speed in the model. The linear matrix inequality (LMI) method was used to solve the gain of the controller's matrix. Simulations were also used to verify the performance of the proposed controller. The simulation results prove that the controller can improve the vehicle's handling stability against the uncertainty generated by the parameters.

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Data fusion and path-following controllers comparison for autonomous vehicles

Cited by 27 publications

References 16 publications

Cooperative GNSS positioning aided by road-features measurements

Cooperative GNSS positioning aided by road-features measurements

Position and Orientation Estimation of a Maneticalluy Guided-Articulated Vehicle

State Feedback Control Based on Regional Pole Placement on Handling Stability of Ground Vehicles

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