标题： IteraOptiRacing：用于实时自主竞速的统一规划-控制框架，以实现迭代最优性能 显示英文标题

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

摘要： 本文提出了一种统一的规划-控制策略，用于在自主赛车环境中与其他赛车竞争，称为IteraOptiRacing。 这种统一策略是基于针对迭代任务的迭代线性二次调节器（i2LQR）提出的，可以在存在周围赛车障碍物的情况下提高圈速性能。 通过迭代使用自车的历史数据，该统一策略同时考虑了多辆移动车辆的避障和时间成本优化，从而生成无碰撞且时间最优的轨迹。 该算法具有恒定的低计算负担，并适合并行计算，使其能够在竞争性赛车场景中实现实时运行。 为了验证其性能，在高保真模拟器中进行了仿真，赛道上有多名随机生成的动态代理。 结果表明，所提出的策略在所有随机生成的自主赛车场景中均优于现有方法，使自车能够实现更出色的操控。 显示英文摘要

摘要： 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.