引用本文:刘磊,杨晔,刘赛,等.基于生存理论训练机器学习的智能驾驶路径生成方法[J].控制与决策,2020,35(10):2433-2441
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基于生存理论训练机器学习的智能驾驶路径生成方法
刘磊1,2, 杨晔2, 刘赛2, 高岩1, 王富正1, 王亚刚2
(1. 上海理工大学管理学院,上海200093;2. 上海理工大学光电学院,上海200093)
摘要:
机器学习技术广泛应用于车辆的智能驾驶,其中模型训练是该技术的关键,由于训练数据难以覆盖全部驾驶情况,使得极端状态下基于机器学习的智能驾驶系统存在失效风险,会造成重大交通事故.生存理论应用于车辆的道路安全态势感知具有理论优势,能客观地计算出车辆最大的高维生存空间,但该理论迭代计算繁琐,输出结果所需时间较长,无法满足高速车辆的实时控制,且生存核表面复杂,智能驾驶系统难以直接使用,需要将生存核转化为局部最优路径.鉴于此,设计一种基于生存理论的局部路径规划机器学习训练方法,通过对多种机器学习方法的特点进行分析,最终选定径向基神经网络来输出生存核中线投影.通过对比两种网络训练数据的输出效果,分析参数敏感性以及泛化能力,论证所提出训练方法的合理性.仿真实验表明,所训练的机器学习模型可快速输出高精度、大裕度的道路优化路径,即使使用简单的控制律也能实现无人车辆的大曲率转弯.由于所提出机器学习方法的安全性具有理论保障,又能大幅提升安全计算的实时性,在智能驾驶领域拥有广阔的应用前景.
关键词:  智能驾驶  路径规划  生存理论  径向基神经网络
DOI:10.13195/j.kzyjc.2019.0059
分类号:TP273
基金项目:上海市自然科学基金项目(17ZR1419000);国家自然科学基金项目(61074087,11502145,61703277).
Path generation method for intelligent driving based on machine learning trained by viability theory
LIU Lei1,2,YANG Ye2,LIU Sai2,GAO Yan1,WANG Fu-zheng1,WANG Ya-gang2
(1. School of Management,University of Shanghai for Science and Technology,Shanghai200093,China;2. School of Optical-electrical,University of Shanghai for Science and Technology,Shanghai200093,China)
Abstract:
Machine learning has been widely used in the area of intelligent driving. Model training is the key point for the application of machine learning. Due to the insufficient training data for all driving situiaitons, serious traffic accidents would happen if the intelligent driving system fails under the extreme conditions. By applying the viability theory to the dynamic adaptive analysis of the vehicle, the maximum safety space in high-dimension can be objectively calculated, which reflects the special advantages of this theory in the field of vehicle's safety. However, due to the high complexity of computation and long analyzing time, the iterative algorithm of this theory cannot meet the real-time control requirements of high-speed vehicles. The surface of the viability kernel is also too complex to be used directly by the intelligent driving system, for which the viability kernel eads to be transformed into the optimal path of the road. Therefore, we design a training method for machine learning of local path planning by the viability theory. According to the character analysis of multiple machine learning, we finally select radial basis function(RBF) neural network to output the central line projection of the viaibity kernel. By comparing two training methods, analyzing the parameter sensitivity and generalization ability, the proposed training method is approved to be rational. Simulation experiments show that the data-driven model trained by our method can output the safety results quickly and precisely. Meanwhile, the control margin is large, so it can help to drive the unmanned vehicles in a high challenging environment by a simple control rule. Since the safety of the machine learning method can be guaranteed by the theory, and the computing speed is enhanced, the method has a wide application in the area of intelligent driving.
Key words:  intelligent driving  path planning  viability theory  radial basis function neural network

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