TY - GEN

T1 - Model-Based Maximum Friction Coefficient Estimation for Road Surfaces with Gradient or Cross-Slope

AU - Lampe, Nicolas

AU - Ehlers, Simon Friedrich Gerhard

AU - Kortmann, Karl-Philipp

AU - Westerkamp, Clemens

AU - Seel, Thomas

PY - 2024/6/2

Y1 - 2024/6/2

N2 - For the development of advanced driver assistance systems (ADAS) and autonomous driving, a perception of the vehicle's environment is necessary. This includes, among others, road gradients, cross-slopes, and the road surface condition, with the maximum friction coefficient of the tire-road contact as a safety-relevant parameter. However, these three road parameters cannot be measured directly while driving by sensors installed in modern vehicles. Current estimation methods provide either the maximum friction coefficient or the road gradient and cross-slope but never combined. Since the road angles influence the maximum friction coefficient estimation and vice versa, separate estimation of these parameters, in general, leads to incorrect estimation results. In this paper, a new Unscented Kalman Filter (UKF)-based approach is proposed for simultaneous estimation of all three mentioned road parameters. For this purpose, a dynamic vehicle model considering road gradients and cross-slopes is introduced and integrated into the UKF. It is demonstrated that, in contrast to a state-of-the-art UKF, the proposed algorithm yields improved accuracy and correct maximum friction coefficient estimates even on roads with gradients or cross-slopes.

AB - For the development of advanced driver assistance systems (ADAS) and autonomous driving, a perception of the vehicle's environment is necessary. This includes, among others, road gradients, cross-slopes, and the road surface condition, with the maximum friction coefficient of the tire-road contact as a safety-relevant parameter. However, these three road parameters cannot be measured directly while driving by sensors installed in modern vehicles. Current estimation methods provide either the maximum friction coefficient or the road gradient and cross-slope but never combined. Since the road angles influence the maximum friction coefficient estimation and vice versa, separate estimation of these parameters, in general, leads to incorrect estimation results. In this paper, a new Unscented Kalman Filter (UKF)-based approach is proposed for simultaneous estimation of all three mentioned road parameters. For this purpose, a dynamic vehicle model considering road gradients and cross-slopes is introduced and integrated into the UKF. It is demonstrated that, in contrast to a state-of-the-art UKF, the proposed algorithm yields improved accuracy and correct maximum friction coefficient estimates even on roads with gradients or cross-slopes.

UR - http://www.scopus.com/inward/record.url?scp=85199811885&partnerID=8YFLogxK

U2 - 10.1109/IV55156.2024.10588642

DO - 10.1109/IV55156.2024.10588642

M3 - Conference contribution

SN - 979-8-3503-4882-8

T3 - IEEE Intelligent Vehicles Symposium

SP - 2141

EP - 2147

BT - Conference Proceedings - 2024 35th IEEE Intelligent Vehicles Symposium (IV)

PB - Institute of Electrical and Electronics Engineers Inc.

ER -