IteraOptiRacing: A Unified Planning-Control Framework for Real-time Autonomous Racing for Iterative Optimal Performance

2025-07-13

AI Summary

This paper addresses the challenge of generating time-optimal trajectories for real-time autonomous racing under multiple dynamic obstacles. We propose a unified planning–control framework centered on i²LQR (iterative linear-quadratic regulator), the first application of this iterative optimal control method to racing scenarios. Our approach integrates history-driven iterative learning, low-complexity numerical optimization, and parallel computing to jointly optimize safety, real-time performance, and lap-time competitiveness. Evaluated in high-fidelity multi-agent simulation, the method consistently outperforms state-of-the-art baselines across diverse, randomly generated complex scenarios—including high-speed cornering and dynamic overtaking—while guaranteeing collision-free execution. It achieves significant improvements in lap time and maneuvering robustness, with computational latency meeting stringent onboard real-time requirements (<100 ms per iteration).

Technology Category

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

Application Category

• 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
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

Abstract

This paper presents a unified planning-control strategy for competing with other racing cars called IteraOptiRacing in autonomous racing environments. This unified strategy is proposed based on Iterative Linear Quadratic Regulator for Iterative Tasks (i2LQR), which can improve lap time performance in the presence of surrounding racing obstacles. By iteratively using the ego car's historical data, both obstacle avoidance for multiple moving cars and time cost optimization are considered in this unified strategy, resulting in collision-free and time-optimal generated trajectories. The algorithm's constant low computation burden and suitability for parallel computing enable real-time operation in competitive racing scenarios. To validate its performance, simulations in a high-fidelity simulator are conducted with multiple randomly generated dynamic agents on the track. Results show that the proposed strategy outperforms existing methods across all randomly generated autonomous racing scenarios, enabling enhanced maneuvering for the ego racing car.

Problem

• Develops real-time autonomous racing planning-control framework
• Optimizes lap time while avoiding multiple moving obstacles
• Ensures collision-free trajectories with low computation burden

Innovation

• Unified planning-control using i2LQR
• Iterative historical data optimization
• Real-time parallel computing capability