将车辆动力学先验知识融入神经网络，提升自动驾驶赛车转向控制的精度与鲁棒性。
Model-Structured Neural Networks to Control the Steering Dynamics of Autonomous Race Cars
- 在神经网络结构中嵌入非线性车辆动力学先验知识
- 小样本训练下精度和泛化能力优于通用神经网络
- 对权重初始化不敏感，适合实际赛车场景部署
自动驾驶赛车近年来受到越来越多关注，因其提供了一个安全环境以加速自动驾驶运动规划与控制方法的发展。基于神经网络（NNs）的深度学习模型在建模车辆动力学和执行各类自动驾驶任务方面展现出显著潜力。然而，其黑箱特性在自动驾驶赛车场景中尤为关键，因安全与鲁棒性要求对决策算法有深入理解。为此，本文提出一种新型模型结构化神经网络 MS-NN-steer，将非线性车辆动力学先验知识整合进神经网络架构中，用于车辆转向控制。该控制器在阿布扎比自动驾驶赛车联盟（A2RL）竞赛的真实世界数据上进行了验证，使用全尺寸自动驾驶赛车。相较于通用神经网络，MS-NN-steer 在小样本训练下表现出更高的精度与更强的泛化能力，且对权重初始化更不敏感。此外，其性能优于 A2RL 冠军团队所使用的转向控制器。代码已开源至 GitHub 仓库。
原文摘要 · Abstract (English)
Autonomous racing has gained increasing attention in recent years, as a safe environment to accelerate the development of motion planning and control methods for autonomous driving. Deep learning models, predominantly based on neural networks (NNs), have demonstrated significant potential in modeling the vehicle dynamics and in performing various tasks in autonomous driving. However, their black-box nature is critical in the context of autonomous racing, where safety and robustness demand a thorough understanding of the decision-making algorithms. To address this challenge, this paper proposes MS-NN-steer, a new Model-Structured Neural Network for vehicle steering control, integrating the prior knowledge of the nonlinear vehicle dynamics into the neural architecture. The proposed controller is validated using real-world data from the Abu Dhabi Autonomous Racing League (A2RL) competition, with full-scale autonomous race cars. In comparison with general-purpose NNs, MS-NN-steer is shown to achieve better accuracy and generalization with small training datasets, while being less sensitive to the weights' initialization. Also, MS-NN-steer outperforms the steering controller used by the A2RL winning team. Our implementation is available open-source in a GitHub repository.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。