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

Abstract

Traction adaptive motion planning and control has potential to improve an an automated vehicle's ability to avoid accident in a critical situation. However, such functionality require 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, fusion of the two provides sufficient accuracy, availability and foresight to yield near optimal behavior. To this end, we propose a fusion method based on heteroscedastic gaussian process regression, and present initial simulation based results.

Traction Adaptive Motion Planning and Control at the Limits of Handling

“…It is intuitively clear that the characteristics and performance of such friction estimates will influence the planned behavior. We do not include that aspect in the scope of this article, instead we present a follow-up study, dedicated to that topic [39], where we conclude that combining camera-based and traditional friction estimation techniques enable near-optimal traction adaptive motion planning and control. These preliminary results are obtained by emulating The computing system of the truck includes the microservices-based software stack OpenDLV 4 that enables transparent communication of distributed software components using inter-process communication (IPC), for example, network communication and shared memory for low latency in real-time setups and for large volume sensor data handling.…”