With the advent of advanced driver assistant systems (ADAS) in urban scenarios, a fast and reliable classification and motion estimation of wheel-based vehicles such as cars, trucks or motorcycles is crucial. The fact that the wheels' velocities differ from the vehicle's chassis velocity is exploited. For the first time, a fully automated approach based on the Doppler distribution extracts the exact positions of the wheels. The Normalized Doppler Moment is calculated, describing the Doppler signature of each reflection based on the Doppler distributions of wheels. Locations with high values reveal the positions of the wheels. Besides the classification, the vehicle's orientation and therefore the driving direction can be estimated. Furthermore the position of the rear axle is estimated, which is essential for a reliable prediction of rotational movements and yaw rate estimation. Experimental results with a 77 GHz automotive radar sensor demonstrate the feasibility of the approach.