Integrating INS sensors with GPS velocity measurements for continuous estimation of vehicle sideslip and tire cornering stiffness

Bevly, David M.; Sheridan, R.; Gerdes, J. Christian

doi:10.1109/acc.2001.945508

Cited by 77 publications

(32 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…A different solution to overcome the low sampling rate issue has been proposed by Bevly [70,71,81]. This method uses a KF, together with the data fusion from GPS and IS, to predict the IS biases when GPS data are available, and integrate the "corrected" IS signals when such data become unavailable.…”

Section: Gps-aided Estimationmentioning

confidence: 99%

See 1 more Smart Citation

On the Vehicle Sideslip Angle Estimation: A Literature Review of Methods, Models, and Innovations

2018

View full text Add to dashboard Cite

Typical active safety systems that control the dynamics of passenger cars rely on the real-time monitoring of the vehicle sideslip angle (VSA), together with other signals such as the wheel angular velocities, steering angle, lateral acceleration, and the rate of rotation about the vertical axis, which is known as the yaw rate. The VSA (also known as the attitude or "drifting" angle) is defined as the angle between the vehicle's longitudinal axis and the direction of travel, taking the centre of gravity as a reference. It is basically a measure of the misalignment between vehicle orientation and trajectory; therefore, it is a vital piece of information enabling directional stability assessment, such as in transience following emergency manoeuvres, for instance. As explained in the introduction, the VSA is not measured directly for impracticality, and it is estimated on the basis of available measurements such as wheel velocities, linear and angular accelerations, etc. This work is intended to provide a comprehensive literature review on the VSA estimation problem. Two main estimation methods have been categorised, i.e., observer-based and neural network-based, focussing on the most effective and innovative approaches. As the first method normally relies on a vehicle model, a review of the vehicle models has been included. The advantages and limitations of each technique have been highlighted and discussed.

show abstract

Section: Gps-aided Estimationmentioning

confidence: 99%

“…However, Bevly et al in [70,71,81], proposed a single GPS receiver is used, resulting in the ability to predict the gyro bias only when the vehicle is travelling straight. A dual GPS antenna setup is therefore introduced by Bevly et al in [82] and Ryu et al in [83] that allows the yaw angle to be measured at any instant.…”

Section: Gps-aided Estimationmentioning

confidence: 99%

On the Vehicle Sideslip Angle Estimation: A Literature Review of Methods, Models, and Innovations

2018

View full text Add to dashboard Cite

show abstract

“…This kind of higher order dynamic model has been fused with GPS velocity to estimate slip ( [5], [23]- [25]). Other parameters estimated include roll and tire cornering stiffness.…”

Section: Literature Reviewmentioning

confidence: 99%

Kalman Filter-Based Integration of DGPS and Vehicle Sensors for Localization

Rezaei

Sengupta

2007

IEEE Trans. Contr. Syst. Technol.

201

View full text Add to dashboard Cite

Abstract-We present a position estimation scheme for cars based on the integration of Global Positioning System (GPS) with vehicle sensors. The aim is to achieve enough accuracy to enable in vehicle cooperative collision warning, i.e., systems that provides warnings to drivers based on information about the motions of neighboring vehicles obtained by wireless communications from those vehicles, without use of ranging sensors. The vehicle sensors consist of wheel speed sensors, steering angle encoder, and a fiber optic gyro. We fuse these in an extended Kalman filter. The process model is a dynamic bicycle model. We present data from about 60 km of driving in urban environments including stops, intersection turns, U-turns, and lane changes, at both low and high speeds. The data show the filter estimates position, speed, and heading with the accuracies required by cooperative collision warning in all except two kinds of settings. The data also shows GPS and vehicle sensor integration through a bicycle model compares favorably with position estimation by fusing GPS and inertial navigation system (INS) through a kinematic model.

show abstract

“…The importance of sideslip is twofold; it allows better description of vehicle dynamics and on the other hand it plays an important role in wheel-road interaction, allowing us to determine friction or cornering forces. (Bevly et al 2001) proposes a method to integrate inertial sensors with GPS to estimate sideslip angle and cornering stiffness. Sensor fusion is essential to solve this problem since GPS sensors have high noise and low sample rates, inertial sensors are fast and accurate but their measurements need to be integrated leading to unbounded errors.…”

Section: Slip Measurementmentioning

confidence: 99%