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E Longitudinal velocity estimation
as parked cars, sewer covers or traffic signs) have a better reflection capability than
the road surface, for example. In addition, the road conditions influence the received
signal power. In particular, the presence of water is limiting, since it diffuses the signal,
[Win09b]. Nevertheless, for many driving states, a sensorfusion of radar-based velocity
estimation with on-board sensors seems to be sensible for this application.
E.4. GNSS-based estimation of the longitudinal velocity
Global navigation satellite system (GNSS) is the umbrella term for existing satellite-
basednavigation infrastructures, suchas theglobalpositioningsystem(GPS)orGalileo,
[WHW09b, p.677]. Starting in October 2015, it will be mandatory for new vehicles in
the European Union to be able to automatically communicate with the European emer-
gency call infrastructure system (eCall) after a severe accident (e.g. in case the driver is
unconscious and unable to call an emergency service), [Dat14]. The data received by the
emergency services include the time of the accident, the position of the vehicle, and its
direction, which can be relevant on highways or in tunnels. The technical considerations
for the vehicle’s on-board system to be approved include the use of GNSS, [EotEC14].
Thus, it can be assumed that GNSS will be available in series production vehicles in the
near future.
Belvy and Gerdes used GNSS velocity information to calculate the longitudinal slips
sx,i, slip angles αi and the vehicle’s side slip angle β, [BG00]. To calculate the wheel
slip, the GNSS velocity was directly used in combination with the wheel speed sensors.
During free rolling of the tires, they propose using the GNSS velocity to calibrate the
wheel speed sensors’ signals. The results showed high slip noise and an offset that could
have been subject to miscalibration. Nevertheless, it was also shown that the noise was
brought in by the wheel speed sensors rather than the GNSS velocity measurement. A
similar approach was used by Rajamani et al., [RPPL12], where sx is calculated directly
using the GNSS velocity and the wheel speeds.
Miller et al. usedtheGNSSvelocity toestimate theeffective tire radiusandthe longi-
tudinal stiffness of the tires, [MYM+01]. Beyond calculating sx directly using the GNSS
velocity, it has been used to calculate the effective tire radius re,i of non-driven wheels
during free rolling within a sub-millimeter accuracy. Then, sxwas calculated using only
the wheel speed measurements, which excludes estimation of braking conditions (other
than engine braking) and for 4WD vehicles.
148
Maximum Tire-Road Friction Coefficient Estimation
- Title
- Maximum Tire-Road Friction Coefficient Estimation
- Author
- Cornelia Lex
- Publisher
- Verlag der Technischen Universität Graz
- Location
- Graz
- Date
- 2015
- Language
- English
- License
- CC BY-NC-ND 3.0
- ISBN
- 978-3-85125-423-5
- Size
- 21.0 x 29.7 cm
- Pages
- 189
- Category
- Technik