Estimating severe irregularities of road ahead based on preceding vehicle responses

Road irregularities, e.g. potholes or bulges, will cause discomfort, vehicle damages or even accidents, if not being carefully handled by drivers. For driving safety and comfort, especially in highly automated vehicles, there is a need for accurate and efficient way to estimate the road condition ahead in advance. Existing direct sensing based methods are difficult to give detailed irregularity information, while current response based approaches require either too many measurements or accurate system parameters to guarantee estimation performances. Therefore, using the information of preceding vehicle responses, a Kalman filter based algorithm to estimate severe road irregularities is proposed. The single degree of freedom vertical dynamics model is reorganized to reduce the measurement requirements of the Kalman filter. To cope with the limited information of the preceding vehicle, the model parameters are approximated according to suspension design theories. Simulation and field data validation shows that the estimation algorithm is effective and robust on different vehicles.