Details
| Original language | English |
|---|---|
| Article number | 104409 |
| Pages (from-to) | 104409 |
| Number of pages | 1 |
| Journal | Control engineering practice |
| Volume | 100 |
| Publication status | Published - 2020 |
| Externally published | Yes |
Abstract
In the context of autonomous driving in urban environments accurate and reliable information about the vehicle motion is crucial. This article presents a multi-modal sensor fusion scheme that, based on standard production car sensors and an inertial measurement unit, estimates the three-dimensional vehicle velocity and attitude angles (pitch and roll). Moreover, in order to enhance the estimation accuracy, the scheme simultaneously estimates the gyroscope and accelerometer biases. The approach relies on a state-affine representation of a kinematic model with an additional measurement equation based on a single-track model. The sensor fusion scheme is built upon a recently proposed adaptive estimator, which allows a direct consideration of model uncertainties and sensor noise. In order to provide accurate estimates during collision avoidance manoeuvres, a measurement covariance adaptation is introduced, which reduces the influence of the single-track model when its information is superfluous. A validation using experimental data demonstrates the effectiveness of the method during both regular urban drives and collision avoidance manoeuvres.
Keywords
- Automotive industry, Autonomous driving, Collision avoidance, Inertial sensors, Kalman filter, Motion estimation, Non-linear systems, Observability, Odometry, Simultaneous state and parameter estimation, Systems and control engineering
ASJC Scopus subject areas
- Mathematics(all)
- Applied Mathematics
- Engineering(all)
- Electrical and Electronic Engineering
- Engineering(all)
- Control and Systems Engineering
- Computer Science(all)
- Computer Science Applications
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In: Control engineering practice, Vol. 100, 104409, 2020, p. 104409.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Multi-modal sensor fusion for highly accurate vehicle motion state estimation
AU - Marco, Vicent Rodrigo
AU - Kalkkuhl, Jens
AU - Raisch, Jörg
AU - Scholte, Wouter J.
AU - Nijmeijer, Henk
AU - Seel, Thomas
PY - 2020
Y1 - 2020
N2 - In the context of autonomous driving in urban environments accurate and reliable information about the vehicle motion is crucial. This article presents a multi-modal sensor fusion scheme that, based on standard production car sensors and an inertial measurement unit, estimates the three-dimensional vehicle velocity and attitude angles (pitch and roll). Moreover, in order to enhance the estimation accuracy, the scheme simultaneously estimates the gyroscope and accelerometer biases. The approach relies on a state-affine representation of a kinematic model with an additional measurement equation based on a single-track model. The sensor fusion scheme is built upon a recently proposed adaptive estimator, which allows a direct consideration of model uncertainties and sensor noise. In order to provide accurate estimates during collision avoidance manoeuvres, a measurement covariance adaptation is introduced, which reduces the influence of the single-track model when its information is superfluous. A validation using experimental data demonstrates the effectiveness of the method during both regular urban drives and collision avoidance manoeuvres.
AB - In the context of autonomous driving in urban environments accurate and reliable information about the vehicle motion is crucial. This article presents a multi-modal sensor fusion scheme that, based on standard production car sensors and an inertial measurement unit, estimates the three-dimensional vehicle velocity and attitude angles (pitch and roll). Moreover, in order to enhance the estimation accuracy, the scheme simultaneously estimates the gyroscope and accelerometer biases. The approach relies on a state-affine representation of a kinematic model with an additional measurement equation based on a single-track model. The sensor fusion scheme is built upon a recently proposed adaptive estimator, which allows a direct consideration of model uncertainties and sensor noise. In order to provide accurate estimates during collision avoidance manoeuvres, a measurement covariance adaptation is introduced, which reduces the influence of the single-track model when its information is superfluous. A validation using experimental data demonstrates the effectiveness of the method during both regular urban drives and collision avoidance manoeuvres.
KW - Automotive industry
KW - Autonomous driving
KW - Collision avoidance
KW - Inertial sensors
KW - Kalman filter
KW - Motion estimation
KW - Non-linear systems
KW - Observability
KW - Odometry
KW - Simultaneous state and parameter estimation
KW - Systems and control engineering
UR - http://www.scopus.com/inward/record.url?scp=85084185890&partnerID=8YFLogxK
U2 - 10.1016/j.conengprac.2020.104409
DO - 10.1016/j.conengprac.2020.104409
M3 - Article
VL - 100
SP - 104409
JO - Control engineering practice
JF - Control engineering practice
SN - 0967-0661
M1 - 104409
ER -