Neural-network multiple models filter (NMM)-based position estimation system for autonomous vehicles

Gwak, M.; Jo, Kichun; Sunwoo, Myoungho

doi:10.1007/s12239-013-0030-2

Cited by 22 publications

(6 citation statements)

References 15 publications

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“…In [137], the adaptive neuro-fuzzy inference system (ANFIS) with input delay technique was developed to estimate vehicle velocity and position through the fusion of datasets from the GPS and inertial navigation system (INS); experimental results have demonstrated that ANFIS can provide improved estimation accuracy when compared with the EKF method. The NNs have also been employed to estimate vehicle states by fusing multi-sensors [138,139,140,159]. The integration of GPS/INS through NNs considered in [139] was done to process the GPS signal in case of INS signal loss so that it can obtain accurate position and data, whereas the neural network-based MMF in [140] was adopted in order to obtain accurate and reliable position estimation of autonomous vehicles by combining GPS and on-board sensors.…”

Section: Data-driven-based Vehicle Estimationmentioning

confidence: 99%

“…The NNs have also been employed to estimate vehicle states by fusing multi-sensors [138,139,140,159]. The integration of GPS/INS through NNs considered in [139] was done to process the GPS signal in case of INS signal loss so that it can obtain accurate position and data, whereas the neural network-based MMF in [140] was adopted in order to obtain accurate and reliable position estimation of autonomous vehicles by combining GPS and on-board sensors. The deep learning (DL)-based GNSS network was also structured to improve the GNSS absolute positioning accuracy of automatic navigation of vehicles by combining various sensors [141].…”

Section: Data-driven-based Vehicle Estimationmentioning

confidence: 99%

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Advanced Estimation Techniques for Vehicle System Dynamic State: A Survey

Jin

Yin

Chen

2019

Sensors

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In order to improve handling stability performance and active safety of a ground vehicle, a large number of advanced vehicle dynamics control systems—such as the direct yaw control system and active front steering system, and in particular the advanced driver assistance systems—towards connected and automated driving vehicles have recently been developed and applied. However, the practical effects and potential performance of vehicle active safety dynamics control systems heavily depend on real-time knowledge of fundamental vehicle state information, which is difficult to measure directly in a standard car because of both technical and economic reasons. This paper presents a comprehensive technical survey of the development and recent research advances in vehicle system dynamic state estimation. Different aspects of estimation strategies and methodologies in recent literature are classified into two main categories—the model-based estimation approach and the data-driven-based estimation approach. Each category is further divided into several sub-categories from the perspectives of estimation-oriented vehicle models, estimations, sensor configurations, and involved estimation techniques. The principal features of the most popular methodologies are summarized, and the pros and cons of these methodologies are also highlighted and discussed. Finally, future research directions in this field are provided.

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Section: Data-driven-based Vehicle Estimationmentioning

confidence: 99%

Section: Data-driven-based Vehicle Estimationmentioning

confidence: 99%

Advanced Estimation Techniques for Vehicle System Dynamic State: A Survey

Jin

Yin

Chen

2019

Sensors

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“…In this paper, in order to cover the various driving conditions, an Interacting Multiple Model (IMM) filter-based information fusion system is used for the localization system [16,19]. The localization system can adapt to changing vehicle dynamic characteristics under various driving conditions since the IMM filter selects the kinematics and dynamics model according to driving conditions.…”

Section: ) Localizationmentioning

confidence: 99%

Development of Autonomous Car—Part II: A Case Study on the Implementation of an Autonomous Driving System Based on Distributed Architecture

Kim

et al. 2015

IEEE Trans. Ind. Electron.

204

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Part I of this paper proposed a development process and system platform for the development of autonomous cars based on distributed system architecture. The proposed development methodology enabled the design and development of an autonomous car with benefits such as a reduction of computational complexity, fault-tolerant characteristics, and system modularity. In present paper (Part II), a case study of the proposed development methodology are addressed by showing the implementation process of an autonomous driving system. In order to describe the implementation process intuitively, core autonomous driving algorithms (localization, perception, planning, vehicle control, and system management) are briefly introduced and applied to the implementation of an autonomous driving system. We are able to examine the advantages of a distributed system architecture and the proposed development process by conducting a case study on the autonomous system implementation. The validity of the proposed methodology is proved through the autonomous car, A1 that won the 2012 Autonomous Vehicle Competition in Korea with all missions completed.

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“…Various types of Bayesian filtering algorithm such as a Kalman filter, an unscented Kalman filter and a particle filter are generally used to integrate the GPS information and the vehicle motion information. [36][37][38][39] The Bayesian filtering algorithm estimates the posterior probability P(x k |z 1:k ) of a state, based on the measurements z 1:k up to that time k. However, the filtering algorithm is not appropriate for the post-processing because the measurements after the time k are not addressed for the measurement update. Since the post-processing is performed after finishing the data acquisition, we can apply all the measurements z 1:N of the GPS and the onboard sensors in the time range from 1 to N to estimate the state x k .…”

Section: Post-processingmentioning

confidence: 99%

Construction process of a three-dimensional roadway geometry map for autonomous driving

Lee

Kim

et al. 2016

Proceedings of the Institution of Mechanical Engineers, Part D:

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This paper presents a construction process of the three-dimensional roadway geometry map for an autonomous driving system. The presented process focuses on the post-processing and the three-dimensional roadway geometry modelling algorithms. The post-processing algorithm refines the raw Global Positioning System position data by combining the novel three-dimensional roadway geometry model which originated from the construction engineering of roadways and vehicle motion data from onboard sensors using a Rauch–Tung–Striebel smoother. The three-dimensional geometry modelling algorithm approximates the road geometry information described in three-dimensional point clouds into a mathematical curve model based on the B-spline. An adaptive curve refinement method using dominant points was applied to the road modelling algorithm. This dominant-point-based refinement method can reduce the number of knots and the number of control points of the B-spline road model while maintaining the desired accuracy of the roadway map. Also, since the dominant-point refinement method considers a road shape factor, such as the curvature and the arc length, for the road modelling, it is more efficient than the previous B-spline road modelling algorithms. The proposed map generation algorithm was verified and evaluated through experiments in various test conditions. The experimental results show that the presented construction process of the three-dimensional roadway geometry map can provide sufficient accuracy, reliability and efficiency for applications of autonomous driving systems in comparison with those of other roadway map construction processes.

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Neural-network multiple models filter (NMM)-based position estimation system for autonomous vehicles

Cited by 22 publications

References 15 publications

Advanced Estimation Techniques for Vehicle System Dynamic State: A Survey

Advanced Estimation Techniques for Vehicle System Dynamic State: A Survey

Development of Autonomous Car—Part II: A Case Study on the Implementation of an Autonomous Driving System Based on Distributed Architecture

Construction process of a three-dimensional roadway geometry map for autonomous driving

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