Track-centric Iterative Learning for Global Trajectory Optimization in Autonomous Racing

2026-01-28

AI Summary

This work addresses the challenge of high computational cost and poor real-world tracking performance in global trajectory optimization under uncertain vehicle dynamics. The authors propose a track-agnostic iterative learning framework that constructs a universal trajectory parameter space via wavelet transform and jointly employs Bayesian optimization with simulation-based evaluation to online learn the vehicle dynamics model while simultaneously optimizing the full-horizon trajectory. This approach represents the first method to achieve co-iterative optimization of dynamics modeling and global trajectory planning. Experimental results in both simulation and real-world vehicle tests demonstrate lap time improvements of up to 20.7% over baseline methods, consistently outperforming existing state-of-the-art approaches.

Technology Category

• Search and Optimization: Learning to Search
• Planning, Routing, and Scheduling: Learning for Planning and Scheduling
• Intelligent Robots: Learning & Optimization for ROB

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

This paper presents a global trajectory optimization framework for minimizing lap time in autonomous racing under uncertain vehicle dynamics. Optimizing the trajectory over the full racing horizon is computationally expensive, and tracking such a trajectory in the real world hardly assures global optimality due to uncertain dynamics. Yet, existing work mostly focuses on dynamics learning at the tracking level, without updating the trajectory itself to account for the learned dynamics. To address these challenges, we propose a track-centric approach that directly learns and optimizes the full-horizon trajectory. We first represent trajectories through a track-agnostic parametric space in light of the wavelet transform. This space is then efficiently explored using Bayesian optimization, where the lap time of each candidate is evaluated by running simulations with the learned dynamics. This optimization is embedded in an iterative learning framework, where the optimized trajectory is deployed to collect real-world data for updating the dynamics, progressively refining the trajectory over the iterations. The effectiveness of the proposed framework is validated through simulations and real-world experiments, demonstrating lap time improvement of up to 20.7% over a nominal baseline and consistently outperforming state-of-the-art methods.

Problem

• autonomous racing
• trajectory optimization
• uncertain dynamics
• lap time minimization
• global optimality

Innovation

• track-centric trajectory optimization
• wavelet-based parametric representation
• Bayesian optimization
• iterative learning control
• learned vehicle dynamics