Knowledge of a vehicle's speed is fundamental to the driver, but also for many modern control systems as anti-lock braking systems (ABS), dynamic stability systems, anti-spin systems and traction control systems. Also, recent approaches to driver safety information systems, as road friction indicators and sensor-free tire pressure monitoring systems require precise velocity information.
There are no standard sensors in commercial cars that can measure absolute velocity. The speedometer of wheeled vehicles is based on the formula ν=ωnom [m/s], where ω is the wheel rotational speed and rnom is the nominal wheel radius. The rotational speed of a wheel is accurately measured by on-board sensors, as available for example in the antilock braking system (ABS). Wheel radii, however, may change with temperature, wear and even with the velocity to be measured due to centripetal forces which makes it impossible to exactly determine the wheel radius. Thus, in practice only a nominal approximate value can be used which leads to up to 10% error in the velocity measurement. This may be acceptable for the driver, who can tolerate such an error, but both the driver and the control and information systems listed above benefit from increased precision of the velocity measurement.
Prior art describes different principles to measure the absolute velocity of a vehicle:                In image correlation techniques, a camera provides an image of an area of the road surface beneath the vehicle, and by taking a new one which partly covers the same road surface area, image correlation provides an absolute velocity. While being very accurate and insensitive to wheel grip and surface, its drawback is its cost and sensitivity to dirt and damages.        Navigation using GPS and digital street maps enables information of the driven path from which the absolute velocity can be computed. The price is still high, and there is no integrity guarantee for such systems when used in safety critical applications.        Fix point reference based approaches, either requiring an on-board vision system or markers in or along the road and corresponding sensors in the car.        Using a well calibrated extra free-rolling wheel. Though being used by road authorities, it is hardly a mass market solution.        Sensor fusion of longitudinal accelerometer signal and wheel rotational speed, as disclosed in EP 1 274 613 A1. This approach promises accurate estimation of absolute wheel radius, and thus vehicle velocity, but requires a non-standard though cheap sensor.        
Another approach is based on measuring vehicle vibrations both at the front axle and at the rear axle of the car. The front axle and the rear axle feel road bumpiness in a time delayed manner. From the time difference between vibrations at the front axle and at the rear axle and the axle distance, the velocity of the vehicle can in principle be determined. In prior art, there are several implementations which are based on this approach.
The German patent application DE 34 35 866 A1 discloses a system which uses the correlation of wheel suspension signals to determine a vehicle velocity. The suspension sensors measure up-down movement of the front and the rear axles to determine the velocity. The European patent application EP 1 014 092 A2 describes a similar system based on the same type of sensors. A main drawback of these systems is that few vehicles are today equipped with the necessary sensors.
The European patent publication EP 0 294 803 A2 discloses a system based on force sensors in a spring leg. These sensors are spaced in the driving direction, and the absolute velocity is computed by correlation analysis as above.
The system disclosed in US patent publication U.S. Pat. No. 5,301,130 is based on correlation analysis of data obtained by vertical acceleration sensors.
Another type of system which is based on correlation analysis is disclosed in the German patent applications DE 27 51 012 A2 and DE 28 49 028 A2. The systems described therein use transducers for generating electrical signals reflecting the road surface unevenness.
All these known systems have at least two of the following drawbacks:                Most approaches require dedicated (costly) sensors.        Correlation based approaches uses Fast Fourier Transform (FFT) algorithms which are memory and computational intensive.        Standard approaches to correlation analysis described in prior art using either time-domain or frequency domain (FFT) inevitably require that the velocity must be constant during each batch, which is hardly true in practice. Even small velocity variations imply that accuracy is lost.        
Furthermore, none of the above presented correlation techniques disclose a solution for the problem of varying vehicle velocities. Indicative of the vehicle velocity is the maximum peak of a correlation function whose position depends on the time delay between rear axle vibrations and front axle vibrations. Since the position of the correlation peak shifts with varying velocity, a smeared correlation peak is obtained from a data sample which was recorded with varying vehicle velocity. This peak shift deteriorates velocity measurements. But particularly in situations with varying velocity (breaking, acceleration, cornering, etc.) accurate vehicle velocity information is required by safety control systems.