Title: Sensor-fusion-based road friction estimation for robust safety-critical trajectory planning of automated driving

Authors: Liang Shao; Huangsong Chen; Jun Liu; Hesheng Tang

Addresses: Department of Mechanical and Electrical Engineering, Wenzhou University, Wenzhou, 325035, China ' Department of Mechanical and Electrical Engineering, Wenzhou University, Wenzhou, 325035, China ' Shanghai X-AI Auto Technology Co., Ltd. Shanghai, 201419, China ' Department of Mechanical and Electrical Engineering, Wenzhou University, Wenzhou, 325035, China

Abstract: Combining vehicle-dynamics-based methods (VDM) with camera-based methods (CBM) for a better road friction coefficient (RFC) estimation is a trend for safe automated driving. However, misclassification of road condition in CBM and reliable detection of driving excitation in VDM are not well considered, leading to poor RFC estimation and thus causing accidents in safety-critical scenarios. To overcome such problems, this work proposes a robust framework to estimate RFC and then applies it for safety-critical trajectory planning. Firstly, the RFC is estimated with a stable nonlinear estimator based on robust excitation detection with VDM. Then, RFC from VDM and CBM are fused considering camera mis-classification. The estimation of RFC is subsequently applied for safety-critical trajectory planning with two-stage model predictive control. Simulations based on CarSim demonstrate that the proposed framework can better guarantee planning safety than CBM and VDM combined method without considering camera mis-classification or reliable excitation detection.

Keywords: road friction estimation; sensor fusion; trajectory planning; two-stage model predictive control.

DOI: 10.1504/IJVD.2024.139169

International Journal of Vehicle Design, 2024 Vol.95 No.3/4, pp.186 - 204

Received: 31 Jul 2023
Accepted: 27 Nov 2023
Published online: 24 Jun 2024 *

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