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

Abstract

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.

Performance Evaluation of Autonomous Driving Control Algorithm for a Crawler-Type Agricultural Vehicle Based on Low-Cost Multi-Sensor Fusion Positioning

“…Noguchi et al [13] optic gyroscope (FOG) and an IMU driving robot 3 cm Japan Takai et al [14] GNSS-RTK and an IMU crawler-type tractor 5 cm Japan Xiang et al [15] GNSS-RTK and IMU rice transplanter Less than 10 cm China Recently, advances in micro electromechanical system (MEMS) technology have led to the development of a small and inexpensive MEMS-IMU, and the performance of the MEMS-IMU is being improved [16]. In addition, the performance of a low-cost GNSS-RTK module is improving due to advances in the multi-GNSS positioning technique using GPS, a global navigation satellite system (GLONASS), BeiDou, Galileo, and a quasi-zenith satellite system (QZSS).…”

“…For example, major agricultural machinery manufacturers, such as John Deere, Case IH, Yanmar, and New Holland, developed autonomous driving technology or have a concept/plan to carry it out, and more than 20 of the projects are underway across the world [2,3]. John Deere presented an autonomous tractor, the 8320 [9] GNSS-RTK Automatic steering system 2.5-6.9 cm Germany Gan-Mor et al [10] GNSS-RTK tractor Centimeters Israel Han et al [11] single-frequency GNSS-RTK speed sprayer Centimeters Korea Noguchi et al [13] GNSS-RTK, a fiber optic gyroscope (FOG) and an IMU autonomous driving robot 3 cm Japan Takai et al [14] GNSS-RTK and an IMU crawler-type tractor 5 cm Japan Xiang et al [15] GNSS-RTK and IMU rice transplanter Less than 10 cm China Recently, advances in micro electromechanical system (MEMS) technology have led to the development of a small and inexpensive MEMS-IMU, and the performance of the MEMS-IMU is being improved [16]. In addition, the performance of a low-cost GNSS-RTK module is improving due to advances in the multi-GNSS positioning technique using GPS, a global navigation satellite system (GLONASS), BeiDou, Galileo, and a quasi-zenith satellite system (QZSS).…”

MEMS 3D DR/GPS Integrated System for Land Vehicle Application Robust to GPS Outages

“…Since a vehicle moves dynamically, we use the odometer scale factor error model instead of odometer bias error model. From (28) and (29), the system matrix F of the 3D DR/GPS error equation is expressed by (30), and its submatrices are expressed by (31,32).…”

“…Previous works [29], [30] proposed methods to estimate vehicle's attitude accurately by compensating external accelerations of the vehicle. Performance analysis was done at GPS outage areas according to combination of sensors including wheel speed sensor, steering angle sensor, and yaw rate sensor [31]. To improve navigation performance with additional sensors, visual odometry-based algorithms were also studied [32], [33].…”