A Control Theoretical Adaptive Human Pilot Model: Theory and Experimental Validation

The Adaptive Stress Testing Formulation

Validation is a key challenge in the search for safe autonomy. Simulations are often either too simple to provide robust validation, or too complex to tractably compute. Therefore, approximate validation methods are needed to tractably find failures without unsafe simplifications. This paper presents the theory behind one such black-box approach: adaptive stress testing (AST). We also provide three examples of validation problems formulated to work with AST.

利用自主水下航行器与卫星影像进行藻华自适应采样：波罗的海实验验证

Adaptive Sampling of Algal Blooms Using Autonomous Underwater Vehicle and Satellite Imagery: Experimental Validation in the Baltic Sea

本研究提出结合卫星数据与AUV的藻华锋面自适应采样方法，通过卫星数据预训练高斯过程核参数、AUV数据在线建模求锋面方向，经仿真对比及敏感性分析验证后，于2022年夏在斯德哥尔摩群岛完成实地实验。

非线性系统基于多项式的自适应控制

Polynomial Chaos-Based Adaptive Control for Nonlinear Systems

本文提出了一种非线性自适应次优控制策略，通过广义多项式混沌展开处理随机参数不确定性，设计了确定性模型并验证了控制律的渐近弱稳定性。

Adaptive Stress Testing with Reward Augmentation for Autonomous Vehicle Validation

Determining possible failure scenarios is a critical step in the evaluation of autonomous vehicle systems. Real-world vehicle testing is commonly employed for autonomous vehicle validation, but the costs and time requirements are high. Consequently, simulation-driven methods such as Adaptive Stress Testing (AST) have been proposed to aid in validation. AST formulates the problem of finding the most likely failure scenarios as a Markov decision process, which can be solved using reinforcement learning. In practice, AST tends to find scenarios where failure is unavoidable and tends to repeatedly…

Validation of Image-Based Neural Network Controllers through Adaptive Stress Testing

Neural networks have become state-of-the-art for computer vision problems because of their ability to efficiently model complex functions from large amounts of data. While neural networks can be shown to perform well empirically for a variety of tasks, their performance is difficult to guarantee. Neural network verification tools have been developed that can certify robustness with respect to a given input image; however, for neural network systems used in closed-loop controllers, robustness with respect to individual images does not address multi-step properties of the neural network controll…

人机系统验证中的人类模型综述

Survey of Human Models for Verification of Human-Machine Systems

本综述调研了人工智能早期认知模型到人机交互形式化任务模型的人类操作员建模概况，评估其在单驾驶员飞机等未来安全关键航空系统设计验证中的适用性，并指出不足与未来方向。

面向自适应巡航控制电动汽车的相位感知AI跟驰模型：基于真实数据的开发与验证

A phase-aware AI car-following model for electric vehicles with adaptive cruise control: Development and validation using real-world data

针对电动汽车独特跟驰动态，提出融合相位识别AI的PAAI模型，利用ACC真实轨迹数据验证，显著提升预测精度。

Efficient Autonomy Validation in Simulation with Adaptive Stress Testing

During the development of autonomous systems such as driverless cars, it is important to characterize the scenarios that are most likely to result in failure. Adaptive Stress Testing (AST) provides a way to search for the most-likely failure scenario as a Markov decision process (MDP). Our previous work used a deep reinforcement learning (DRL) solver to identify likely failure scenarios. However, the solver's use of a feed-forward neural network with a discretized space of possible initial conditions poses two major problems. First, the system is not treated as a black box, in that it requires…

物理信息神经网络估计电路参数自适应调制的DAB

Physics Informed Neural Network Estimated Circuit Parameter Adaptive Modulation of DAB

本文提出一种基于物理信息神经网络估计电路参数的自适应三移相调制方案，用于双有源桥DC-DC变换器，实现损耗优化与参数敏感控制。