Intelligent control for trustworthy autonomous vehicles

值得信赖的自动驾驶车辆的智能控制

• 批准号: 2892782
• 金额: --
• 依托单位: Queen's University Belfast
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2892782
• 关键词: Intelligent; control; trustworthy; autonomous; vehicles

"Autonomous vehicle control presents a significant challenge for artificial intelligence and control theory. To safely control the autonomous vehicle systems, the control system needs to understand the behavior of system dynamics under the effects of model uncertainties and environmental disturbances. This can be achieved by embedding a control scheme that integrates: (1) the design of model-based controllers to ensure the safety of the system, (2) the design of online learning technique for estimating the unknown system dynamics, and (3) the design of model free-learning technique such as reinforcement learning to estimate the unknown environment. The integration between the model-based control with online system dynamics approximation and environment learning will enable the autonomous vehicle to highly have confidence on safety during driving. Therefore, the aim of the project is to design a controller architecture that combines: (1) a model-based controllers utilizing the safety critical control concepts, (2) an online learning of the unknown system dynamics, and (3) a model-free reinforcement learning technique, which estimates the external environment, to ensure safety during learning and exploration.The objectives of the project include:1. To design a model-based safety critical control that guarantees the autonomous vehicles will always operate within the safety zone.2. To develop dynamic identification technique, for example Gaussian Processes (GPs), to model the system dynamics.3. To develop learning algorithms based on (deep)-reinforcement learning to understand the surrounding environment, i.e, human, obstacles.4. To implement and validate the proposed algorithms in computer simulation platforms: Gazebo platform or Matlab/Simulink.5. To implement the developed algorithms in a UAV platform, i.e., HuskyA200 AGV robot, PAWs robot."

"Autonomous vehicle control presents a significant challenge for artificial intelligence and control theory. To safely control the autonomous vehicle systems, the control system needs to understand the behavior of system dynamics under the effects of model uncertainties and environmental disturbances. This can be achieved by embedding a control scheme that integrates: (1) the design of model-based controllers to ensure the safety of the system, (2) the design of online learning technique for estimating the unknown system dynamics, and (3) the design模型的自由学习技术，例如增强学习，以估算未知的环境。基于模型的控制与在线系统动态近似和环境学习将使自动驾驶汽车在驾驶过程中对安全性有高度的信心估计外部环境的强化学习技术，以确保在学习和探索过程中的安全性。项目的目标包括：1。设计基于模型的安全关键控制，以确保自动驾驶汽车将始终在安全区内运行2。开发动态识别技术，例如高斯过程（GPS），以建模系统动力学3。基于（深）强化学习以理解周围环境，即人类，障碍4的学习算法。4。在计算机模拟平台中实现和验证所提出的算法：凉亭平台或MATLAB/SIMULINK.5。要在无人机平台（即Huskya200 AGV机器人，PAWS机器人）中实现开发算法。”