Dynamic Localization of Car-like Vehicles using Data Fusion of Redundant ABS Sensors

Bouron, P.; Meizel, Dominique; Crubillé, P.

doi:10.1017/s037346330300242x

Cited by 13 publications

(3 citation statements)

References 9 publications

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“…DMIs consist of wheel rotation sensors, such as differential odometers and ABS (anti-breaking systems) sensors (Bonnifait, Bouron, Meizel, & Crubille, 2003). Those are the one usually used in automotive localization systems.…”

Section: Differential Odometer Modelmentioning

confidence: 99%

An Overview of Positioning and Data Fusion Techniques Applied to Land Vehicle Navigation Systems

Gingras

2009

Automotive Informatics and Communicative Systems

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In this chapter, the authors will review the problem of estimating in real-time the position of a vehicle for use in land navigation systems. After describing the application context and giving a definition of the problem, they will look at the mathematical framework and technologies involved to design positioning systems. The authors will compare the performance of some of the most popular data fusion approaches and provide some insights on their limitations and capabilities. They will then look at the case of robustness of the positioning system when one or some of the sensors are faulty and will describe how the positioning system can be made more robust and adaptive in order to take into account the occurrence of faulty or degraded sensors. Finally, they will go one step further and explore possible architectures for collaborative positioning systems, whereas many vehicles are interacting and exchanging data to improve their own position estimate. The chapter is concluded with some remarks on the future evolution of the field.

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Section: Differential Odometer Modelmentioning

confidence: 99%

An Overview of Positioning and Data Fusion Techniques Applied to Land Vehicle Navigation Systems

Gingras

2009

Automotive Informatics and Communicative Systems

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“…First, on-board vehicle sensors are applied to compensate for the INS error. Most of the previous researches are the integration of WSS with GNSS/INS to prevent INS error degradation during GNSS outages [5][6][7] Reference [8] developed five types of sensors integration algorithms for precise positioning, namely GPS/INS/WSS, GPS/INS/GS/YRS, GPS/INS/SAS, GPS/INS/WSS/SAS, and GPS/INS/WSS/GL/YRS/SAS. Second, on-board vehicle sensors are applied for dead reckoning (DR) to overcome the limitation of GNSS technique.…”

Section: Introductionmentioning

confidence: 99%

Performance evaluation on GNSS, wheel speed sensor, yaw rate sensor, and gravity sensor integrated positioning algorithm for automotive navigation system

Han

Park

2019

E3S Web Conf.

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The Global Navigation Satellite System (GNSS) positioning technique is widely used for the automotive navigation system since it can provide the stable and accurate position and velocity in the most road environments at an affordable price. However, the performance of GNSS positioning technique is degraded in certain areas, where GNSS signals are blocked by buildings and tunnel. To overcome this problem, the GNSS positioning technique should be integrated with dead reckoning (DR) sensors such as accelerometer, gyroscope, and odometer. Recently, the most passenger cars are equipped with the Advanced Driver Assistance System (ADAS) based on numerous sensors to improve safety and convenience in driving. Among sensors for the ADAS, vehicle dynamic sensors such as wheel speed sensor (WSS), yaw rate sensor (YRS), gravity sensor (GS) can be used for the DR algorithm since those sensors measure vehicle’s motions. Therefore, this paper evaluates the vehicle positioning algorithm that integrate the GNSS with a three-dimensional dead reckoning based on WSS, YRS, and GS. The vehicle positioning algorithm is implemented through the extended Kalman filter of a loosely-coupled mode. Performance was evaluated through tests carried out in real driving trajectory including various GNSS signals reception environments. It is found that the proposed algorithm can be an alternative solution to compensate the limitation of the GNSS positioning technique, without the use of a low-cost inertial measurement unit.

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“…Therefore, as vehicle dynamic sensors can be applied to overcome the limitations of the GNSS technique, various studies combining GNSS and vehicle dynamic sensors have been conducted. Bonnifait et al [9] developed a localization system using GPS and WSS. They showed that a two-dimensional DR navigation based on WSS could estimate the horizontal positions with meter-level accuracy with GPS.…”

Section: Introductionmentioning

confidence: 99%

Performance Analysis of Two-Dimensional Dead Reckoning Based on Vehicle Dynamic Sensors during GNSS Outages

Han¹,

Park²,

Hong³

et al. 2017

Journal of Sensors

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Recently, to improve safety and convenience in driving, numerous sensors are mounted on cars to operate advanced driver assistant systems. Among various sensors, vehicle dynamic sensors can measure the vehicle motions such as speed and rotational angular speed for dead reckoning, which can be applied to develop a land vehicle positioning system to overcome the weaknesses of the GNSS technique. In this paper, three land vehicle positioning algorithms that integrate GNSS with vehicle dynamic sensors including a wheel speed sensor (WSS), a yaw rate sensor (YRS), and a steering angle sensor (SAS) are implemented, and then a performance evaluation was conducted during GNSS outages. Using a loosely coupled strategy, three integration algorithms are designed, namely, GNSS/WSS, GNSS/WSS/YRS, and GNSS/WSS/YRS/SAS. The performance of the three types of integration algorithm is evaluated based on two data sets. The results indicate that both the GNSS/WSS/YRS integration and the GNSS/WSS/YRS/SAS integration could estimate the horizontal position with meter-level accuracy during 30-second GNSS outages. However, the GNSS/WSS integration would provide an unstable navigation solution during GNSS outages due to the accuracy limitation of the computed yaw rate using WSS.

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Dynamic Localization of Car-like Vehicles using Data Fusion of Redundant ABS Sensors

Cited by 13 publications

References 9 publications

An Overview of Positioning and Data Fusion Techniques Applied to Land Vehicle Navigation Systems

An Overview of Positioning and Data Fusion Techniques Applied to Land Vehicle Navigation Systems

Performance evaluation on GNSS, wheel speed sensor, yaw rate sensor, and gravity sensor integrated positioning algorithm for automotive navigation system

Performance Analysis of Two-Dimensional Dead Reckoning Based on Vehicle Dynamic Sensors during GNSS Outages

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