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<p>Active collision avoidance technology based on steering and braking coordination has been widely studied. However, existing studies often assume the road adhesion coefficient as a known constant, limiting system adaptability in dynamic road environments. To address this limitation, this paper proposes a lane change collision avoidance controller with real-time estimation and application of the road adhesion coefficient. First, an adaptive generalized high-order cubature Kalman filter (AGHCKF) algorithm was developed based on a nonlinear vehicle dynamics model for real-time adhesion coefficient estimation. Then, combined with the estimated information and relevant constraints, real-time feasible lane change paths were planned via fifth-order polynomial optimization. Finally, lateral vehicle control was implemented through an estimated road adhesion coefficient-based model predictive control (µ-MPC) method to ensure accurate trajectory tracking. The joint simulation results demonstrated that the proposed AGHCKF algorithm can effectively estimate the adhesion coefficient under various road conditions. The µ-MPC controller significantly outperformed traditional model predictive control (MPC), linear quadratic regulator (LQR), and proportional-integral-derivative (PID) controllers in terms of trajectory tracking accuracy and stability, reducing the maximum lateral displacement error to 0.058&#xa0;m. Moreover, key vehicle state parameters remained within safety boundaries across different conditions, demonstrating excellent robustness.</p>

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Research on Lane Changing and Collision Avoidance Control for Intelligent Vehicle Based on Road Adhesion Coefficient Estimation

  • 期刊论文

  • 发表日期：

    2026年3月23日

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  • Shunan Liang,
  • Shaopeng Tian,
  • Congda Xiao

摘要

Active collision avoidance technology based on steering and braking coordination has been widely studied. However, existing studies often assume the road adhesion coefficient as a known constant, limiting system adaptability in dynamic road environments. To address this limitation, this paper proposes a lane change collision avoidance controller with real-time estimation and application of the road adhesion coefficient. First, an adaptive generalized high-order cubature Kalman filter (AGHCKF) algorithm was developed based on a nonlinear vehicle dynamics model for real-time adhesion coefficient estimation. Then, combined with the estimated information and relevant constraints, real-time feasible lane change paths were planned via fifth-order polynomial optimization. Finally, lateral vehicle control was implemented through an estimated road adhesion coefficient-based model predictive control (µ-MPC) method to ensure accurate trajectory tracking. The joint simulation results demonstrated that the proposed AGHCKF algorithm can effectively estimate the adhesion coefficient under various road conditions. The µ-MPC controller significantly outperformed traditional model predictive control (MPC), linear quadratic regulator (LQR), and proportional-integral-derivative (PID) controllers in terms of trajectory tracking accuracy and stability, reducing the maximum lateral displacement error to 0.058 m. Moreover, key vehicle state parameters remained within safety boundaries across different conditions, demonstrating excellent robustness.