Fusion of Heterogeneous Friction Estimates for Traction Adaptive Motion Planning and Control

Traction adaptive motion planning and control has the potential to improve the ability of automated vehicles to avoid accidents in critical situations. However, such functionality requires an accurate friction estimate for the road ahead of the vehicle, that is updated in real time. Current state of the art friction estimation techniques include high accuracy local friction estimation in the presence of tire slip, as well as rough classification of the road surface ahead of the vehicle, based on forward looking camera. In this paper, we show that neither of these techniques in isolation yield satisfactory behavior when deployed with traction adaptive motion planning and control functionality. However, we also identify that the two techniques are complementary in terms of the key properties accuracy, availability and foresight, indicating that combining the techniques may be meaningful. To this end, we propose a fusion method based on heteroscedastic gaussian process regression, and present initial simulation based results indicating that near-optimal traction adaptive motion planning and control can be achieved by such a method.