报告时间：2026年6月5日14:30-15:30
报告地点：yl7703永利集团天心校区综合实验楼226
报告人：王子妮
报告标题：Regularized Online Adaptation of Parametric Interactive Multiple Model against Overfitting for Vehicle Motion Tracking
报告摘要：Interactive Multiple Model (IMM) has been widely applied in motion tracking tasks due to its capability in handling multiple macroscopic behavior hypotheses. In the context of human-driven vehicle motion tracking, drivers often exhibit diverse and personalized driving preferences, which challenge conventional IMM algorithms that rely on predefined models. Nevertheless, the adaptation of multiple models to match observations may induce an overfitting issue, which compromises the ability to identify the real behavior. To address these challenges, this paper presents a regularized online adaptation approach against overfitting for IMM-based vehicle motion tracking. We introduce an advanced motion modeling framework in the Frenet coordinate frame, which naturally decouples longitudinal and lateral motions. Then, a differentiable parametric motion policy is incorporated for enhancing the model adaptability based on simultaneous state and parameter estimation using extended Kalman filtering (EKF). Furthermore, the overfitting problem is addressed by imposing regularization constraints on the model parameters in the filtering correction step based on the Alternating direction method of multipliers (ADMM) iteration. In simulation, the proposed method shows advantages in accurate motion tracking while remaining computationally efficient.
