Key Takeaways & Executive Findings
- •• Proposes a novel data-enforced unscented Kalman filter (DeUKF) that integrates historical driving data to correct tire model uncertainties, improving TRFC estimation accuracy. • Leverages Willems’ Fundamental Lemma to adaptively correct the vehicle dynamics model within the UKF framework, mitigating the adverse effects of model parameter uncertainties. • Validates the proposed method through real vehicle experiments, demonstrating its effectiveness and superiority over conventional model-based approaches. • Enhances the reliability of TRFC estimation for intelligent chassis systems, contributing to improved vehicle safety and dynamic stability.
Abstract
The tire-road friction coefficient (TRFC) plays a critical role in vehicle safety and dynamic stability, with model-based approaches being the primary method for TRFC estimation. However, the accuracy of these methods is often constrained by the complexity of tire force expressions and uncertainties in tire model parameters, particularly under diverse and complex driving conditions. To address these challenges, this paper proposes a novel data-enforced unscented Kalman filter (DeUKF) approach for precise TRFC estimation in intelligent chassis systems. First, an Unscented Kalman Filter is constructed using a nominal tire model-based vehicle dynamics formulation. Then, leveraging Willems’ Fundamental Lemma and historical real-world driving data, the vehicle dynamics model is adaptively corrected within the Unscented Kalman Filter framework. This correction effectively mitigates the adverse effects of tire model uncertainties, thereby enhancing TRFC estimation accuracy. Finally, real vehicle experiments are conducted to validate the effectiveness and superiority of the proposed method.
1. Introduction
The safety of autonomous vehicles relies heavily on the tire-road friction coefficient, especially on slippery road surfaces. Due to the limitations on the maximum force that tires can generate, the risk of traffic accidents increases significantly [1]. The TRFC governs the tire’s ability to produce steering, traction, and braking forces, which in turn influence vehicle motion and stability [2]. Accurate estimation of the TRFC can improve the performance of active safety systems significantly, such as anti-lock braking systems (ABS), electronic stability control (ESC), adaptive cruise control, and driver assistance systems like the collision avoidance system [3–5]. While real-time TRFC measurement is crucial for vehicle safety, it cannot be directly acquired through sensors, presenting a substantial challenge for researchers.
Currently, existing TRFC estimation methods can be categorized into two main approaches: sensor-based and model-based methods [6]. The sensor-based approach primarily relies on additional sensors to detect exogenous materials on road surfaces using techniques such as vision, temperature, or other sensor modalities [7]. Zhao et al. [8] proposed an adaptive fusion estimation framework incorporating an image-based estimator (IBE) to identify road conditions and estimate the TRFC range. Erdogan et al. [9] introduced a novel wireless piezoelectric tire sensor to estimate the maximum friction coefficient, while this approach utilizes specialized sensors mounted on the vehicle. This work introduced additional hardware costs in production vehicles and can provide an approximate range of the TRFC only [10], lacking the ability to accurately estimate the precise TRFC value. Consequently, sensor-based TRFC estimation methods have not been widely applied in practice. In contrast, model-based methods have garnered increasing attention currently.
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Jinheng Han, Junzhi Zhang, Chen Lv, Ruihai Ma, Henglai Wei (2025). Estimation of Road Friction Coefficient via the Data Enforced Unscented Kalman Filter. Chinese Journal of Mechanical Engineering. https://doi.org/10.1186/s10033-025-01354-z
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Frequently Asked Questions
What is the main contribution of this paper?
The paper proposes a novel data-enforced unscented Kalman filter (DeUKF) that integrates historical driving data to correct tire model uncertainties, improving the accuracy of tire-road friction coefficient estimation.
How does the DeUKF method work?
The DeUKF constructs an Unscented Kalman Filter using a nominal tire model-based vehicle dynamics formulation, then leverages Willems' Fundamental Lemma and historical real-world driving data to adaptively correct the vehicle dynamics model within the UKF framework.
What are the advantages of the proposed method over existing approaches?
The proposed method mitigates the adverse effects of tire model uncertainties, enhancing TRFC estimation accuracy without requiring additional hardware sensors, making it more practical and cost-effective.
How was the proposed method validated?
The method was validated through real vehicle experiments, demonstrating its effectiveness and superiority in estimating the tire-road friction coefficient under various driving conditions.
What are the potential applications of this research?
The research can improve the performance of active safety systems such as ABS, ESC, adaptive cruise control, and collision avoidance systems, contributing to safer autonomous driving.
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