一分钟内基于学习的规模化自动驾驶赛车赛道系统辨识

IF 4.6 2区 计算机科学 Q2 ROBOTICS IEEE Robotics and Automation Letters Pub Date : 2025-01-08 DOI:10.1109/LRA.2025.3527336
Onur Dikici;Edoardo Ghignone;Cheng Hu;Nicolas Baumann;Lei Xie;Andrea Carron;Michele Magno;Matteo Corno
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引用次数: 0

摘要

准确的轮胎建模对于优化自动驾驶赛车至关重要,因为基于最先进(SotA)模型的技术依赖于对车辆参数的精确了解,但由于赛道和轮胎条件的变化,动态赛车条件下的系统识别具有挑战性。传统的方法需要广泛的操作范围,在赛车场景中往往不切实际。基于机器学习(ML)的方法在提高性能的同时,与泛化和依赖于准确的初始化相斗争。本文介绍了一种新的轨道系统识别算法,该算法将神经网络(NN)用于纠错,然后将其用于基于虚拟生成数据的传统系统识别。至关重要的是,该过程是迭代重复应用的,轮胎参数在每个循环中更新,导致在缩放车辆上测试的准确性显着提高。实验表明,只需要30秒的驾驶数据和3秒的训练时间,就可以在没有先验知识的情况下学习轮胎模型。在噪声条件下,该方法比基线非线性最小二乘(NLS)方法具有更高的一步预测精度,实现了3.3倍的低均方根误差(RMSE),并且生成的轮胎模型具有与传统稳态系统识别相当的精度。此外,与需要大空间和特定实验设置的稳态方法不同,该方法直接在动态赛车环境中的赛道上识别轮胎参数。
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Learning-Based On-Track System Identification for Scaled Autonomous Racing in Under a Minute
Accurate tire modeling is crucial for optimizing autonomous racing vehicles, as State-of-the-Art (SotA) model-based techniques rely on precise knowledge of the vehicle's parameters, yet system identification in dynamic racing conditions is challenging due to varying track and tire conditions. Traditional methods require extensive operational ranges, often impractical in racing scenarios. Machine Learning (ML)-based methods, while improving performance, struggle with generalization and depend on accurate initialization. This paper introduces a novel on-track system identification algorithm, incorporating a Neural Network (NN) for error correction, which is then employed for traditional system identification with virtually generated data. Crucially, the process is iteratively reapplied, with tire parameters updated at each cycle, leading to notable improvements in accuracy in tests on a scaled vehicle. Experiments show that it is possible to learn a tire model without prior knowledge with only 30 seconds of driving data, and 3 seconds of training time. This method demonstrates greater one-step prediction accuracy than the baseline Nonlinear Least Squares (NLS) method under noisy conditions, achieving a 3.3x lower Root Mean Square Error (RMSE), and yields tire models with comparable accuracy to traditional steady-state system identification. Furthermore, unlike steady-state methods requiring large spaces and specific experimental setups, the proposed approach identifies tire parameters directly on a race track in dynamic racing environments.
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来源期刊
IEEE Robotics and Automation Letters
IEEE Robotics and Automation Letters Computer Science-Computer Science Applications
CiteScore
9.60
自引率
15.40%
发文量
1428
期刊介绍: The scope of this journal is to publish peer-reviewed articles that provide a timely and concise account of innovative research ideas and application results, reporting significant theoretical findings and application case studies in areas of robotics and automation.
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